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January 2009 Research Report: UCPRC RR 2009 01 IInnvveessttiiggaattiioonn ooff Nooiissee aanndd Duurraabbiilliittyy Peerrffoorrmaannccee Trreennddss ffoorr Asspphhaallttiicc Paavveemeenntt SSuurrffaaccee Tyyppeess:: Thhrreeee Yeeaarr Reessuullttss Authors: Qing Lu, Erwin Kohler, John T. Harvey, and Aybike Ongel Partnered Pavement Research Program ( PPRC) Contract Strategic Plan Element 4.19: Third Year Field Evaluation of Tire/ Pavement Noise, IRI, Macrotexture and Surface Condition of Flexible Pavements PREPARED FOR: California Department of Transportation Division of Research and Innovation Office of Roadway Research PREPARED BY: University of California Pavement Research Center UC Davis, UC Berkeley ii UCPRC RR 2009 01 DOCUMENT RETRIEVAL PAGE Research Report UCPRC RR 2009 01 Title: Investigation of Noise and Durability Performance Trends for Asphaltic Pavement Surface Types: Three Year Results Author: Q. Lu, E. Kohler, J. Harvey, and A. Ongel Prepared for: Caltrans FHWA No.: CA101881A Work Submitted: March 26, 2009 Date: January 2009 Strategic Plan No: 4.19 Status: Final Version No: May 5, 2010 Abstract: The work presented in this report is part of an on going research project, whose central purpose is to support the Caltrans Quieter Pavement Research Program, that has as its goals and objectives the identification of quieter, smoother, safer and more durable pavement surfaces. The research has been carried out as part of Partnered Pavement Research Center Strategic Plan Element 4.19 ( PPRC SPE 4.19). In the study documented in this report, field data regarding tire/ pavement noise, surface condition, ride quality, and macrotexture were collected over three consecutive years from pavements in California placed with open graded and other asphaltic mixes. The three year data were analyzed to evaluate the durability and effectiveness of open graded mixes in reducing noise compared to other asphalt surfaces, including dense and gap graded mixes, and to evaluate the pavement characteristics that affect tire/ pavement noise. The analysis in this report is a supplement and update to a previous study on the first two years of data collected, which is detailed in a separate report prepared as part of PPRC SPE 4.16, the previous phase of the Quieter Pavement Research Program. Conclusions are made regarding the performance of open graded mixes and rubberized mixes ( RAC G), comparisons are made with dense graded mixes ( DGAC); and the effects of variables affecting tire/ pavement noise are examined. The report presents interim results that will be finalized after supplementation with data collected in 2009 as part of the fourth year ( PPRC SPE 4.27) of the study. Keywords: asphalt concrete, decibel ( dB), noise, absorption, macrotexture, microtexture, open graded, gap graded, densegraded, onboard sound intensity, permeability, flexible pavement Proposals for implementation: No proposals for implementation are presented in this report. Related documents: • Investigation of Noise, Durability, Permeability, and Friction Performance Trends for Asphaltic Pavement Surface Types: First and Second Year Results, by A. Ongel, J. Harvey, E. Kohler, Q. Lu, and B. Steven. February 2008. ( UCPRC RR 2007 03). Report prepared by UCPRC for the Caltrans Department of Research and Innovation. • Summary Report: Investigation of Noise, Durability, Permeability, and Friction Performance Trends for Asphalt Pavement Surface Types: First and Second Year Results, by Aybike Ongel, John T. Harvey, Erwin Kohler, Qing Lu, Bruce D. Steven and Carl L. Monismith. August 2008. ( UCPRC SR 2008 01). Report prepared by UCPRC for the Caltrans Department of Research and Innovation. • Acoustical Absorption of Open Graded, Gap Graded, and Dense Graded Asphalt Pavements, by A. Ongel and E. Kohler. July 2007. ( UCPRC TM 2007 13) Report prepared by UCPRC for the Caltrans Department of Research and Innovation. • State of the Practice in 2006 for Open Graded Asphalt Mix Design, by A. Ongel, J. Harvey, and E. Kohler. December 2007. ( UCPRC TM 2008 07) Report prepared by UCPRC for the Caltrans Department of Research and Innovation. • Temperature Influence on Road Traffic Noise: Californian OBSI measurement study, by Hans Bendtsen, Qing Lu, and Erwin Kohler. Draft report for Caltrans by the Danish Road Institute, Road Directorate and University of California Pavement Research Center. 2009. • Work Plan for project 4.19, “ Third Year Field Evaluation of Tire/ Pavement Noise, IRI, Macrotexture and Surface Condition of Flexible Pavements” Signatures: Qing Lu 1st Author John T. Harvey Technical Review David Spinner Editor John T. Harvey Principal Investigator T. Joseph Holland Caltrans Contract Manager UCPRC RR 2009 01 iii DISCLAIMER The contents of this report reflect the views of the authors who are responsible for the facts and accuracy of the data presented herein. The contents do not necessarily reflect the official views or policies of the State of California or the Federal Highway Administration. This report does not constitute a standard, specification, or regulation. PROJECT OBJECTIVES The research presented in this report is part of the California Department of Transportation ( Caltrans) Quieter Pavement Research ( QPR) Work Plan, whose the central purpose is to support the Caltrans Quieter Pavement Research Program. This program’s goals and objectives are to identify quieter, safer and more durable asphalt pavement surfaces. The University of California Pavement Research Center ( UCPRC) is supporting the Caltrans Quieter Pavement Research Program by performing experiments under Partnered Pavement Research Center Strategic Plan Elements ( PPRC SPEs) 4.16, 4.19, 4.27, and 4.29. The purpose of the project discussed in this report, which is part of PPRC SPE 4.19, is to perform a third year of measurement of tire/ pavement noise, surface condition, ride quality, and macrotexture of 74 flexible pavement sections to improve performance estimates for identification of the more durable, smoother, and quieter pavement types among current asphalt mixes used by Caltrans and several new types of mixes. The three years of data collected on the sections, including the first two years of data collected as part of PPRC SPE 4.16, will be used to provide a preliminary table of estimated design lives for different treatments with respect to the variables measured. PPRC SPE 4.19 has the following objectives: • Objective 1. To perform a third year of noise, smoothness, and distress monitoring of 4.16 sections; • Objective 2. To conduct noise, smoothness, and distress monitoring on field sections with new types of mixes identified as having the potential to be the smoother, quieter, and more durable, or that perform under conditions not included in the previous testing; • Objective 3. To develop pavement temperature corrections for OBSI data and upgrades to the instrumented noise car; • Objective 4. To analyze the results and model them where applicable; and • Objective 5. To develop a preliminary table of expected lives for flexible pavement surfaces; This report documents the work completed for Objectives 1, 2, 4, and 5. The work completed as part of Objective 3 is documented in a separate report. iv UCPRC RR 2009 01 UCPRC RR 2009 01 v EXECUTIVE SUMMARY Background and Purpose The smoothness and quietness of pavements are receiving increased attention and importance as they affect quality of life issues for highway users and neighboring residents. Since the California Department of Transportation ( Caltrans) employs a variety of strategies and materials for maintaining and rehabilitating the state’s highways pavements, it has sought to identify the lives of those strategies and materials, and those of new candidates, that can maintain roadway smoothness and quietness for the longest time. To accomplish this, the Department established the Quieter Pavement Research ( QPR) Program. The Caltrans QPR program is intended to examine the impact of quieter pavements on traffic noise levels and to establish which pavement characteristics have the greatest impact on tire/ pavement noise. The program also aims to identify surface treatments, materials, and construction methods that will result in quieter pavements that are also safe, durable, and cost effective. The information gathered as part of the Caltrans QPR will be used to develop quieter pavement design features and specifications for noise abatement throughout the state. The QPR program includes several studies to evaluate the acoustic properties of pavements and the role that pavement surface characteristics play relative to tire/ pavement noise levels. The research presented in this report is part of one of these studies and is an element of the Caltrans Quieter Pavements Research ( QPR) Work Plan. The QPR Work Plan includes research on both asphalt and concrete pavement surfaces. For the flexible ( asphalt surfaced) pavement part of the QPR study, Caltrans previously identified a need for research in the areas of acoustics, friction, and performance of asphalt pavement surfaces. In response to that need, Partnered Pavement Research Center Strategic Plan Element ( PPRC SPE) 4.16 was initiated in November 2004. Among its other objectives, PPRC SPE 4.16 developed preliminary performance estimates for current Caltrans asphalt surfaces— including DGAC, OGAC, RAC G, and RAC O as part of a factorial experiment— and a number of experimental asphalt surfaces with respect to tire/ pavement noise, permeability, macrotexture, microtexture, smoothness and surface distress development. ( Note that the technical names for these mixes have changed in the new Section 39 of the Standard Specifications. The names in use at the start of PPRC SPE 4.16 have been maintained in this report for consistency with vi UCPRC RR 2009 01 previous reports). Those performance estimates were based on data collected during field tests and laboratory testing of cores in the first two years of the study. PPRC SPE 4.19, titled “ Third Year Field Evaluation of Tire/ Pavement Noise, IRI, Macrotexture, and Surface Condition of Flexible Pavements,” was initiated in September 2007. The purpose of PPRC SPE 4.19 is to perform a third year of measurement of tire/ pavement noise, surface condition, ride quality and macrotexture of up to 74 flexible pavement sections to improve performance estimates for identification of the more durable, smoother, and quieter pavement types. Several new sections were also tested for the first time as part of this project. The results presented in this report are updated performance estimates from the third year of measurements on most of the sections included in the PPRC SPE 4.16 project, combined with the first two years of data. As part of this project several new sections were also tested for the first time. In addition, the three years of data collected on the sections were used to provide a preliminary table of estimated design lives for different treatments with respect to the variables measured. Objectives The objectives of PPRC SPE 4.19 are: 1. To perform a third year of noise, smoothness and condition survey monitoring of PPRC SPE 4.16 sections. Following the PPRC SPE 4.19 work plan, noise, smoothness and macrotexture, and surface condition of each section were measured using the California On board Sound Intensity ( OBSI) method, laser profilometer, and visual condition survey ( walking survey from the shoulder), respectively on the 74 sections included in PPRC SPE 4.16. ( These comprised a factorial of current Caltrans asphalt surface mixes, referred to as “ Quieter Pavement” or “ QP” sections, and a number of experimental surfaces referred to as “ Environmental” or “ ES” sections.) Following the PPRC SPE 4.19 work plan, there were neither traffic closures in the scope of the third year of data collection nor were cores take for measurement of permeability, friction and air voids. 2. To conduct noise, smoothness, and condition survey monitoring on new field sections identified as having the the potential to be more durable, smoother, and quieter, or that perform under conditions not included in the previous testing. The same methods mentioned in Objective 1 were used to evaluate sections not previously included in PPRC SPE 4.16, including asphalt and concrete surfaces. These included testing of additional bituminous wearing course ( BWC) UCPRC RR 2009 01 vii sections beyond the one ES section on State Route 138 in Los Angeles County and evaluation of the SkidabraderTM on several concrete and asphalt surfaces. 3. To develop a pavement temperature correction for OBSI data and upgrades to the instrumented noise car. This objective involved measurement of some sections at various temperatures within a short period in order to quantify the effect of pavement temperature on noise levels and to determine correction formulas for normalizing OBSI measurements. A transition from a single sound intensity probe to double probes was done as part of this project, as were software developments and updates associated with improved data collection practices. 4. To analyze results and model them where applicable. This included analyzing the results of the measurements, investigating trends, and predicting durability, smoothness, and noise performance using the models. 5. To develop preliminary tables of expected lives for flexible pavement surfaces with respect to noise, smoothness, and durability. Scope of the Report This report documents the work completed for Objectives 1, 2, 4, and 5. The work completed as part of Objective 3 is documented in a separate report. The measured results and the qualitative and statistical analyses from this testing program are documented in this report. The information is organized as follows: • Chapter 1 presents the background of the study, its objectives, and the performance parameters for pavement surfaces. • Chapter 2 provides an analysis of ride quality data in terms of the International Roughness Index ( IRI). • Chapter 3 presents an analysis of the macrotexture data in terms of Mean Profile Depth ( MPD). • Chapter 4 presents an analysis of the condition survey data for bleeding, rutting, raveling, transverse/ reflective cracking, and wheelpath cracking. • Chapter 5 presents the On board Sound Intensity ( OBSI) data collected on the test sections. • Chapter 6 presents an analysis of the third year data collected on the Environmental ( ES) sections ( same data as in Chapters 2 through 5 for the QP sections). • Chapter 7 presents the data collected on the new sections visited for the first time this year, including the BWC sections and the Skidabrader sections. viii UCPRC RR 2009 01 • Chapter 8 presents an overall evaluation of the performance models developed in this study, and an assessment of the life spans of the different surface mixes for different conditions and failure criteria based on the models. • Chapter 9 lists the conclusions from the analyses and includes preliminary recommendations. • Appendices provide additional detailed information. The data presented in this report includes the three years of data collection, and is included in a relational database that will be delivered to Caltrans separately. Specific data in the database includes: • Microtexture and macrotexture data that affect skid resistance; • Ride quality in terms of International Roughness Index ( IRI), including third year data; • On board Sound Intensity ( OBSI), a measure of tire/ pavement noise, including third year data; • Sound intensity for different frequencies, including third year data; • Surface distresses, including bleeding, rutting, raveling, transverse cracking, and cracking in the wheelpaths, including third year data; • Climate data; and • Traffic data. The analyses presented for each performance variable in Chapters 2 through 5 include a summary of the expected trends from the literature, descriptive statistics, and where the data is sufficient, statistical models. Several appendices provide the data corrections used and detailed condition survey information. . Conclusions The following conclusions were drawn from the results of analysis of the three years of data and the testing of the new sections. No new recommendations were made. Performance of Open Graded Mixes The average tire/ pavement noise level on DGAC pavements is about 101.3 dB( A) for newly paved overlays, 102.4 dB( A) for pavements between one and three years old, and between 103 and 104 dB( A) for pavements older than three years. Compared to the average noise level of a DGAC mix, the recently paved open graded mixes are quieter by about 2.5 dB( A) for OGAC and by about 3.1 dB( A) for RAC O. After the pavements are exposed to traffic, this noise benefit generally changes slightly for about five to seven years and then begins to diminish after seven years. RAC O remains quieter longer than does OGAC. UCPRC RR 2009 01 ix For recently paved overlays, open graded mixes have higher low frequency noise and lower high frequency noise than DGAC mixes. In the first three years after the open graded mixes are exposed to traffic, high frequency noise increases with age due to the reduction of air void content under traffic, while low frequency noise decreases with age, likely due to the reduction of surface roughness caused by further compaction under traffic. These opposing changes leave the overall sound intensity nearly unchanged. For open graded pavements older than three years, noise in the frequencies between 500 and 2,500 Hz increases with age, while noise in the frequencies over 2,500 Hz changes slightly or diminishes with age. Among the two open graded mixes, MPD has lower initial values and increases more slowly on RAC O pavements than on OGAC pavements. The effect of MPD on noise is complex. It appears that a higher MPD value increases noise on OGAC pavements, but it does not significantly affect the noise on RAC O pavements. Based on the condition survey for pavements less than ten years old, for recently paved overlays, transverse/ reflective cracking is less significant on open graded mixes than on dense or gap graded mixes. However, once cracking appears on open graded mixes it increases more rapidly with pavement age than it does on dense or gap graded mixes. It also appears that open graded pavements experience less raveling than dense graded mixes. There is no other significant difference between open and densegraded mixes in terms of pavement distresses. The data reveal no major difference in pavement distresses between OGAC and RAC O mixes. Performance of RAC G Mixes The newly paved RAC G mixes are quieter in terms of tire/ pavement noise by about 1.6 dB( A), compared to an average DGAC mix. Within a few years after the pavements are exposed to traffic, the tire/ pavement noise on RAC G mixes approaches the average noise level on DGAC pavements of similar ages. For newly paved overlays, RAC G mixes have higher low frequency noise and lower high frequency noise than DGAC mixes. In the first three years after the pavements are exposed to traffic, high frequency noise increases with age due to the reduction of air void content under traffic, while low frequency noise is nearly unchanged with age. For RAC G pavements older than three years, noise of all frequencies increases with age. x UCPRC RR 2009 01 The IRI value on newly paved RAC G surfaces is lower than that for DGAC mixes, and it does not increase with age. The IRI on DGAC pavements, however, increases with age. RAC G mixes have a permeability level as high as that of open graded mixes in the first three years after construction, but under traffic the permeability decreases rapidly to the level of DGAC mixes in about four years. These facts explain the reasons for the initial low noise level and the rapid loss of the noise benefit of RAC G mixes. Based on the condition survey for pavements less than ten years old, RAC G pavement is more prone than other mixes to bleeding in terms of both the time of occurrence and the extent of distress. Transverse/ reflective cracks seem to initiate earlier and propagate faster on the rubberized pavements than on the nonrubberized pavements, but this is possibly because rubberized mixes tend to be placed more frequently on pavements with greater extent of cracking, which biases the comparison. There were no other significant differences between RAC G and DGAC mixes in terms of pavement distresses. Variables Affecting Tire/ Pavement Noise The findings from this third year of the study regarding variables affecting tire/ pavement noise are generally consistent with the findings from the analysis on the two year data. That is, tire/ pavement noise is greatly influenced by surface mix type and mix properties, age, traffic volume, and the presence of distresses. Various mix types have different noise performances, and the overall noise level generally increases with traffic volume, pavement age, and the presence of pavement distresses. The overall noise level decreases with increasing surface layer thickness and permeability ( or air void content). For DGAC, RAC G, and RAC O pavements, the aggregate gradation variable ( fineness modulus) does not seem to significantly affect tire/ pavement noise. For OGAC pavements, however, a coarser gradation seems to significantly reduce tire/ pavement noise. It must be noted that the conclusion regarding aggregate gradation is drawn from a data set that only contains NMAS ranging from 9.5 mm to 19 mm, with most open graded mixes either 9.5 or 12.5 mm, and most RAC G and DGAC mixes either 12.5 or 19 mm. At frequencies below 1,000 Hz, the aggregate gradation variable ( fineness modulus) does not significantly affect the noise level for all pavements. At frequencies above 1,000 Hz, higher macrotexture ( MPD) values seem to significantly reduce the noise level on RAC O mixes. On the other hand, higher macrotexture values increase the noise level of gapgraded mixes. UCPRC RR 2009 01 xi Performance of Experimental Mixes The bituminous wearing course ( BWC) mix placed on the LA 138 sections has a noise level comparable to that of DGAC mixes, and similar distress development as current Caltrans open graded mixes. The noise levels of BWC mixes placed on the sections tested for the first time this year are similar to or lower than those of open graded mixes of similar age. This indicates that the tire/ pavement noise levels of the LA 138 BWC mix are not typical of other BWC mixes placed in the state. Based on the Fresno 33 ( Firebaugh) sections it was observed that: • RUMAC GG performed similarly to RAC G in terms of tire/ pavement noise and ride quality when placed in a thin ( 45 mm) or a thick ( 90 mm) lifts. However, RUMAC GG was more crack resistant than RAC G when placed in a thick lift ( 90 mm). • Although the Type G MB mix has higher noise levels than the RAC G mix soon after construction, the increase in noise with age is less significant on the Type G MB mix than on the RAC G mix and the Type D MB mix. • The Type G MB mix is more susceptible to bleeding than other mixes. • The Type D MB mix is more resistant to cracking than the DGAC mix but it is also more susceptible to bleeding. • The Type D MB mix has a noise level similar to the DGAC mix soon after construction, but its noise level increases with age more than the noise level of the DGAC mix. After opening to traffic for four years, none of the test mixes ( RAC G, RUMAC GG, Type G MB, and Type D MB) had noise levels as high as those of the DGAC mix. The European gap graded ( EU GG) mix placed on LA 19 has performance characteristics very similar to those of gap graded mixes ( RAC G) used in California, except it may retain its permeability longer. Old concrete surfaces with burlap drag and longitudinally tined surface textures that were then retextured with Skidabrader technology showed slight decreases in noise of  0.5 and  0.1 dB( A), respectively. The results showed increases in noise on OGAC and DGAC surfaces that were similarly retextured of 1.3 and 0.8 dB( A), respectively. xii UCPRC RR 2009 01 CONVERSION FACTORS SI* ( MODERN METRIC) CONVERSION FACTORS APPROXIMATE CONVERSIONS TO SI UNITS Symbol Convert From Multiply By Convert To Symbol LENGTH in. inches 25.4 millimeters mm ft feet 0.305 meters m AREA in. 2 square inches 645.2 square millimeters mm2 ft2 square feet 0.093 square meters m2 VOLUME ft3 cubic feet 0.028 cubic meters m3 MASS lb pounds 0.454 kilograms kg TEMPERATURE ( exact degrees) ° F Fahrenheit 5 ( F 32)/ 9 Celsius C or ( F 32)/ 1.8 FORCE and PRESSURE or STRESS lbf poundforce 4.45 newtons N lbf/ in. 2 poundforce/ square inch 6.89 kilopascals kPa APPROXIMATE CONVERSIONS FROM SI UNITS Symbol Convert From Multiply By Convert To Symbol LENGTH mm millimeters 0.039 inches in. m meters 3.28 feet ft AREA mm2 square millimeters 0.0016 square inches in. 2 m2 square meters 10.764 square feet ft2 VOLUME m3 cubic meters 35.314 cubic feet ft3 MASS kg kilograms 2.202 pounds lb TEMPERATURE ( exact degrees) C Celsius 1.8C+ 32 Fahrenheit F FORCE and PRESSURE or STRESS N newtons 0.225 poundforce lbf kPa kilopascals 0.145 poundforce/ square inch lbf/ in. 2 * SI is the symbol for the International System of Units. Appropriate rounding should be made to comply with Section 4 of ASTM E380 ( revised March 2003). UCPRC RR 2009 01 xiii TABLE OF CONTENTS PROJECT OBJECTIVES..................................................................................................................... ... iii EXECUTIVE SUMMARY ........................................................................................................................ v LIST OF FIGURES ............................................................................................................................... xvii LIST OF TABLES ............................................................................................................................... ... xxi 1. INTRODUCTION................................................................................................................... ............ 1 1.1 Project Background..................................................................................................................... .... 1 1.2 Project Purpose and Objectives ....................................................................................................... 2 1.3 Experiment Factorial for Third Year Measurements....................................................................... 3 1.4 Scope of this Report......................................................................................................................... 5 2. SURFACE PROFILE RESULTS AND ANALYSIS: IRI ................................................................ 7 2.1 Descriptive Analysis ........................................................................................................................ 7 2.2. Regression Analysis....................................................................................................................... 11 2.3 Summary of Findings..................................................................................................................... 16 3. SURFACE PROFILE RESULTS AND ANALYSIS: MEAN PROFILE DEPTH...................... 17 3.1 Descriptive Analysis ...................................................................................................................... 17 3.2 Regression Analysis....................................................................................................................... 20 3.3 Summary of Findings..................................................................................................................... 24 4. SURFACE DISTRESS RESULTS AND ANALYSIS..................................................................... 25 4.1 Bleeding ............................................................................................................................... ......... 26 4.1.1 Descriptive Analysis ...................................................................................................... 26 4.1.2 Regression Analysis....................................................................................................... 27 4.2 Rutting ............................................................................................................................... ........... 29 4.2.1 Descriptive Analysis ...................................................................................................... 29 4.2.2 Regression Analysis....................................................................................................... 31 4.3 Transverse/ Reflective Cracking..................................................................................................... 31 4.3.1 Descriptive Analysis ...................................................................................................... 31 4.3.2 Statistical Analysis......................................................................................................... 33 4.4 Raveling ............................................................................................................................... ......... 35 4.4.1 Descriptive Analysis ...................................................................................................... 35 4.4.2 Statistical Analysis......................................................................................................... 36 4.5 Wheelpath ( Fatigue) Cracking....................................................................................................... 38 4.5.1 Descriptive Analysis ...................................................................................................... 38 4.5.2 Statistical Analysis......................................................................................................... 39 4.6 Summary of Findings..................................................................................................................... 42 xiv UCPRC RR 2009 01 5. SOUND INTENSITY RESULTS AND ANALYSIS ....................................................................... 45 5.1 Conversion of Sound Intensity for Temperature, Speed, Air Density, Tire .................................. 46 5.2 Evaluation of Overall Sound Intensity........................................................................................... 47 5.2.1 Descriptive Analysis ...................................................................................................... 47 5.2.2 Regression Analysis....................................................................................................... 52 5.3 Evaluation of Sound Intensity Levels at One Third Octave Bands ............................................... 57 5.3.1 Change of OBSI Spectra with Age ................................................................................ 57 5.3.2 Descriptive Analysis of Sound Intensity Data for All One Third Octave Bands .......... 60 5.3.3 Evaluation of Sound Intensity at 500 Hz One Third Octave Band................................ 67 5.3.4 Evaluation of Sound Intensity at 1,000 Hz One Third Octave Band............................. 74 5.3.5 Evaluation of Sound Intensity at 2,000 Hz One Third Octave Band............................. 81 5.3.6 Evaluation of Sound Intensity at 4,000 Hz One Third Octave Band............................. 88 5.3.7 Sound Intensity at Other One Third Octave Bands ....................................................... 94 5.4 Summary of Findings..................................................................................................................... 95 6. ENVIRONMENTAL SECTIONS RESULTS AND ANALYSIS................................................... 99 6.1 Fresno 33 Sections ......................................................................................................................... 99 6.2 Sacramento 5 and San Mateo 280 Sections ................................................................................. 102 6.3 LA 138 Sections....................................................................................................................... ... 105 6.4 LA 19 Sections....................................................................................................................... ..... 108 6.5 Yolo 80 Section ........................................................................................................................... 109 6.6 Summary........................................................................................................................ ............. 112 7. RESULTS AND ANALYSIS FOR NEW SURFACES MEASURED FOR THE FIRST TIME IN SURVEY YEAR 3 ............................................................................................................................. 113 7.1 SemMaterial BWC Sections ........................................................................................................ 113 7.1.1 Sound Intensity Measurements .................................................................................... 114 7.1.2 International Roughness Index and Mean Profile Depth ............................................. 116 7.2 Skidabrader Retexturing Sections, Before and After................................................................... 117 7.2.1 Before Skidabrader Treatment ..................................................................................... 117 7.2.2 After Skidabrader Treatment ....................................................................................... 122 7.3 Other Testing ............................................................................................................................... 127 7.3.1 Mesa Rodeo Test Sections ........................................................................................... 127 7.3.2 Arizona I 10 ................................................................................................................. 127 7.3.3 California Highway Patrol Sections ( Profilometer Only)............................................ 128 7.4 Summary of the New Surface Testing ......................................................................................... 128 7.4.1 Testing on BWC Sections............................................................................................ 128 7.4.2 Testing on Skidabrader Sections.................................................................................. 128 7.4.3 Testing on Other Sections............................................................................................ 129 UCPRC RR 2009 01 xv 8 ESTIMATED PERFORMANCE OF DIFFERENT ASPHALT MIX TYPES BASED ON PERFORMANCE MODELS................................................................................................................. 131 8.1 Prediction of IRI .......................................................................................................................... 131 8.2 Prediction of Tire/ Pavement Noise.............................................................................................. 133 8.3 Prediction of Pavement Distresses............................................................................................... 136 8.4 Summary........................................................................................................................ ............. 139 9 CONCLUSIONS.................................................................................................................... .......... 141 9.1 Performance of Open Graded Mixes ........................................................................................... 141 9.2 Performance of RAC G Mixes .................................................................................................... 142 9.3 Variables Affecting Tire/ Pavement Noise ................................................................................... 143 9.4 Performance of Experimental Mixes ........................................................................................... 144 REFERENCES..................................................................................................................... .................. 145 APPENDICES..................................................................................................................... ................... 146 A. 1: List of Test Sections Included in the Study................................................................................ 146 A. 1.1: List of Quiet Pavement ( QP) Sections .............................................................................. 146 A. 1.2 List of Caltrans Environmental Noise Monitoring Site ( ES) Sections............................... 150 A. 2: Correlation Between Aquatred 3 Tire OBSI and SRTT OBSI................................................... 151 A. 2.1 Plots of Aquatred 3 Tire OBSI versus SRTT OBSI........................................................... 151 A. 2.2 Simple Linear Regression Results............................................................................................ 153 A. 3: Box Plots of Air Void Content, Permeability, and BPN............................................................ 154 A. 3.1 Box Plots of Air Void Content .......................................................................................... 154 A. 3.2 Box Plots of BPN............................................................................................................... 154 A. 3.3 Box Plots of Permeability .................................................................................................. 155 A. 4: Boxplots and Cumulative Distribution of Noise Reduction for Sound Intensity at Other Frequency Bands.......................................................................................................................... 155 A. 5: Sound Intensity Spectra Measured in Three Years for Each Pavement Section ........................ 163 A. 6: Close up Photos of Pavements Included in This Study.............................................................. 175 A. 7: Condition Survey of Environmental Noise Monitoring Site Sections for Three Years ............. 186 A. 8 Technical Memorandum for Sacramento I 5 sections................................................................ 188 A. 9 Photos of Skidabrader Sections .................................................................................................. 200 A. 10: Actual Values Predicted by Regression Models for Chapter 8 ................................................ 204 xvi UCPRC RR 2009 01 UCPRC RR 2009 01 xvii LIST OF FIGURES Figure 2.1: IRI trends over three years for each pavement section.............................................................. 9 Figure 2.2: Variation in IRI values for different mix types for all three years of pooled data and all initial ages. ............................................................................................................................... .... 10 Figure 2.3: Variation in IRI values for different mix types for different initial ages ( Age category in years) for all three years pooled data. ............................................................................................ 10 Figure 2.4: Comparison of IRI values for different mix types at different ages for first, second, and third years of data collection ( Phase ID showing Years 1, 2, and 3)........................................... 11 Figure 3.1: MPD trend over three years for each pavement section. ......................................................... 18 Figure 3.2: Variation in MPD values for different mix types for pooled data for all three years and all initial ages........................................................................................................................... ... 19 Figure 3.3: Comparison of MPD values for different mix types for different initial age categories ( Age Category) and for first, second, and third years of data collection ( Phase ID). ........................ 19 Figure 4.1: Bleeding development trend over three years for each pavement section................................ 26 Figure 4.2: Percentage of pavement sections of the four mix types with at least 3 percent of their area showing bleeding for each of the three measured years. ............................................................ 27 Figure 4.3: Rutting development trend in three years for each pavement section. ..................................... 30 Figure 4.4: Percentage of pavement sections with rutting of at least 3 mm on at least 25 m of a 150 m long section in the first two years of measurement for four mix types. .................................. 30 Figure 4.5: Transverse/ reflective cracking development trends in three years for each pavement section........................................................................................................................ ....... 31 Figure 4.6: Percentage of pavement sections with 5 m of transverse/ reflective cracking in 150 m section in three years for four mix types. ........................................................................................... 32 Figure 4.7: Raveling development trends over three years for each pavement section. ............................. 35 Figure 4.8: Percentage of pavement sections with at least 5 percent of area with raveling for each of three years of measurement for four mix types............................................................................. 36 Figure 4.9: Development trends for fatigue cracking over three years for each pavement section. ........... 38 Figure 4.10: Percentage of pavement sections with at least 5 percent of wheelpaths with fatigue cracking for each of the three years measured. .................................................................................. 39 Figure 5.1: Development trends of overall OBSI over three years for each pavement section. ................. 49 Figure 5.2: Comparison of overall OBSI values for different mix types for different initial age categories ( Age Category) and for first, second, and third years of data collection ( Phase ID).. 50 Figure 5.3: Cumulative distribution function of noise reduction of OGAC, RAC O, and RAC G mixes for different groups of pavement age....................................................................................... 52 Figure 5.4: Average OBSI spectra for Age Group “< 1 Year” in three survey phases ( years).................... 58 Figure 5.5: Average OBSI spectra for Age Group “ 1– 4 Years” in three survey phases ( years). ............... 59 xviii UCPRC RR 2009 01 Figure 5.6: Average OBSI spectra for Age Group “> 4 Years” in three survey phases ( years). ................. 59 Figure 5.7: Sound intensity at 500 Hz over three years for each pavement section. .................................. 62 Figure 5.8: Sound intensity at 630 Hz over three years for each pavement section. .................................. 62 Figure 5.9: Sound intensity at 800 Hz over three years for each pavement section. .................................. 63 Figure 5.10: Sound intensity at 1,000 Hz over three years for each pavement section. ............................. 63 Figure 5.11: Sound intensity at 1,250 Hz over three years for each pavement section. ............................. 64 Figure 5.12: Sound intensity at 1,600 Hz over three years for each pavement section. ............................. 64 Figure 5.13: Sound intensity at 2,000 Hz over three years for each pavement section. ............................. 65 Figure 5.14: Sound intensity at 2,500 Hz over three years for each pavement section. ............................. 65 Figure 5.15: Sound intensity at 3,150 Hz over three years for each pavement section. ............................. 66 Figure 5.16: Sound intensity at 4,000 Hz over three years for each pavement section. ............................. 66 Figure 5.17: Sound intensity at 5,000 Hz over three years for each pavement section. ............................. 67 Figure 5.18: Sound intensity at 500 Hz for different initial age categories ( Age Category) and for first, second, and third years of data collection ( Phase ID). ....................................................................... 68 Figure 5.19: Cumulative distribution function of 500 Hz noise reduction of OGAC, RAC O, and RAC G mixes for different groups of pavement age. ........................................................................ 69 Figure 5.20: Sound intensity at 1,000 Hz for different initial age categories ( Age Category) and for first, second, and third years of data collection ( Phase ID). ......................................................... 75 Figure 5.21: Cumulative distribution function of 1,000 Hz noise reduction of OGAC, RAC O, and RAC G mixes for different groups of pavement age................................................................... 76 Figure 5.22: Sound intensity at 2,000 Hz for different initial age categories ( Age Category) and for first, second, and third years of data collection ( Phase ID)................................................................ 82 Figure 5.23: Cumulative distribution function of 2,000 Hz noise reduction of OGAC, RAC O, and RAC G mixes for different groups of pavement age................................................................... 83 Figure 5.24: Sound intensity at 4,000 Hz for different initial age categories ( Age Category) and for first, second, and third years of data collection ( Phase ID). ......................................................... 89 Figure 5.25: Cumulative distribution function of 4,000 Hz noise reduction of OGAC, RAC O, and RAC G mixes for different groups of pavement age................................................................... 90 Figure 6.1: Three year MPD values for Fresno 33 sections. .................................................................... 100 Figure 6.2: Three year IRI values for Fresno 33 sections......................................................................... 100 Figure 6.3: Three year Overall OBSI values for Fresno 33 sections. ....................................................... 101 Figure 6.4: Three year IRI values for Sacramento 5 and San Mateo 280 sections................................... 103 Figure 6.5 Three year MPD values for Sacramento 5 and San Mateo 280 sections................................. 104 Figure 6.6: Three year overall OBSI values for Sacramento 5 and San Mateo 280 sections. .................. 104 Figure 6.7: Three year IRI values for the LA 138 sections. ..................................................................... 106 Figure 6.8: Three year overall OBSI values for LA 138 sections. .......................................................... 107 UCPRC RR 2009 01 xix Figure 6.9: Three year IRI values for LA 19 section................................................................................ 109 Figure 6.10: Three year MPD values for LA 19 section. ......................................................................... 109 Figure 6.11: Three year IRI values for the Yolo 80 section. .................................................................... 110 Figure 6.12: Three year MPD values for the Yolo 80 section.................................................................. 111 Figure 6.13: Three year OBSI values for the Yolo 80 section. ................................................................ 111 Figure 7.1: Overall sound intensity levels. ............................................................................................... 114 Figure 7.2: Spectral sound intensity levels. .............................................................................................. 115 Figure 7.3: Sound intensity levels of BWC compared to other pavement types. ..................................... 115 Figure 7.4: Left and right wheelpath IRI levels for each section.............................................................. 116 Figure 7.5: Mean Profile Depth. ............................................................................................................... 117 Figure 7.6: Schematic location of pavement sections ( post miles shown on left side)............................. 118 Figure 7.7: Overall OBSI levels in each section for each pavement type................................................. 119 Figure 7.8: Comparison of OBSI one third band spectra across pavement types..................................... 119 Figure 7.9: OBSI for one third band spectra for burlap drag PCC pavement ( BD) segments.................. 120 Figure 7.10: OBSI for one third band spectra for open graded asphalt pavement ( OG) segments. ......... 120 Figure 7.11: OBSI for one third band spectra for dense graded asphalt pavement ( DG) segments......... 121 Figure 7.12: OBSI for one third band spectra for longitudinally tined PCC pavement ( LT) segments. .121 Figure 7.13: Overall OBSI levels after Skidabrader. ................................................................................ 122 Figure 7.14: OBSI spectra for before and after Skidabrader for burlap drag PCC pavement ( BD) segments....................................................................................................................... ................... 124 Figure 7.15: OBSI spectra for before and after Skidabrader for open graded AC pavement ( OG) segments....................................................................................................................... ................... 125 Figure 7.16: OBSI spectra for before and after Skidabrader for dense graded AC pavement ( DG) segments....................................................................................................................... ................... 126 Figure 7.17: OBSI spectra for before and after Skidabrader for longitudinally tined PCC pavement ( LT) segments.................................................................................................................. 127 Figure A. 1.: UCPRC overall OBSI levels on monitoring section of I 5, southbound ( SB) and northbound ( NB). ...................................................................................................................... 189 Figure A. 2: Overall OBSI spectra levels by I& R and UCPRC on southbound I 5. ................................. 189 Figure A. 3: Overall OBSI spectra levels by I& R and UCPRC on northbound I 5................................... 190 Figure A. 4: Comparison of UCPRC OBSI spectra levels on the SB and NB sections in August 2008 ( SRTT)........................................................................................................................ 190 Figure A. 5: UCPRC OBSI spectra levels on the monitoring section on I 5 southbound ( SRTT) for four site visits......................................................................................................................... .... 190 Figure A. 6: UCPRC OBSI spectra levels on the monitoring section on I 5 northbound ( SRTT). .......... 191 Figure A. 7: Air void content in SB and NB directions from cores taken in February 2006. ................... 192 xx UCPRC RR 2009 01 Figure A. 8: Sound absorption measured on cores from SB section.......................................................... 192 Figure A. 9: Sound absorption measured on cores from NB section......................................................... 193 Figure A. 10: Changes in macrotexture over time in terms of MPD. ........................................................ 193 Figure A. 11: Changes in ride quality over time in terms of IRI. .............................................................. 194 Figure A. 12: Pavement profile at 1 inch intervals, NB direction. ............................................................ 194 Figure A. 13: Detail of first 100 ft of pavement elevation profile on NB direction indicating wide cracks......................................................................................................................... ............. 194 Figure A. 14: Wide reflective cracks in the monitoring section in the NB direction................................ 195 Figure A. 15: Overall 2.5 sec OBSI levels for whole length of southbound lanes ( Note: 1S is the first [ inner] southbound lane, 2S is the second southbound lane, etc)..................................................... 196 Figure A. 16: Overall 2.5 sec OBSI levels for whole length of northbound lanes ( Note: 1N is the first [ inner] northbound lane, 2N is the second northbound lane, etc). ................................................... 196 Figure A. 17: OBSI levels for each lane taking whole project length results. ........................................... 196 Figure A. 18: Images of the pavement in every lane as seen from testing car, August 2008. ................... 197 Figure A. 19: Depiction of southbound lanes tested over the whole length and the approximate location of monitoring sections ( red lines) in the northbound and southbound outer lanes............. 198 Figure B. 1. View of segments A, B, C, and D on BD pavement.............................................................. 200 Figure B. 2. View of segments A, B, C, and D on OG pavement.............................................................. 201 Figure B. 3. View of segments A, B, C, and D on DG pavement.............................................................. 202 Figure B. 4. View of segments A, B, C, and D on LT pavement. ............................................................. 203 UCPRC RR 2009 01 xxi LIST OF TABLES Table 1.1: Number of Sections with Valid Measurements in Three Years................................................... 5 Table 2.1: Regression Analysis of Single Variable Models for IRI ........................................................... 12 Table 3.1: Regression Analysis of Single Variable Models for MPD........................................................ 20 Table 4.1: Regression Analysis of Single Variable Models for Bleeding .................................................. 28 Table 4.2: Regression Analysis of Single Variable Models for Transverse/ Reflective Cracking.............. 33 Table 4.3: Regression Analysis of Single Variable Models for Raveling .................................................. 37 Table 4.4: Regression Analysis of Single Variable Models for Fatigue Cracking..................................... 40 Table 4.5: Single Variable Cox Regression Model for Wheelpath Crack Initiation .................................. 42 Table 5.1: Regression Analysis of Single Variable Models for Overall Sound Intensity .......................... 53 Table 5.2: Regression Analysis of Single Variable Models for 500 Hz Band Sound Intensity................. 70 Table 5.3: Regression Analysis of Single Variable Models for 1,000 Hz Band Sound Intensity .............. 77 Table 5.4: Regression Analysis of Single Variable Models for 2,000 Hz Band Sound Intensity .............. 84 Table 5.5: Regression Analysis of Single Variable Models for 4,000 Hz Band Sound Intensity .............. 91 Table 7.1: BWC Section Locations........................................................................................................... 113 Table 7.2: Physical Properties of BWC Sections from SemMaterial and UCPRC OBSI Measurements114 Table 7.3: Comparison of OBSI Levels Before and After Skidabrader.................................................... 123 Table 8.1: Selection of Typical Environmental Regions .......................................................................... 132 Table 8.2: Predicted Lifetime of Different Asphalt Mix Types with Respect to Roughness.................... 133 Table 8.3: Predicted Lifetime of Different Asphalt Mix Types with Respect to Noise from First Model ............................................................................................................................... ....... 135 Table 8.4: Predicted Lifetime of Different Asphalt Mix Types with Respect to Noise from Second Model.......................................................................................................................... ........ 135 Table 8.5: Predicted Age to Occurrence of Bleeding of Different Asphalt Mix Types............................ 137 Table 8.6: Predicted Age to Occurrence of Raveling of Different Asphalt Mix Types............................ 138 Table 8.7: Predicted Age to Occurrence of Transverse/ Reflective Cracking of Different Asphalt Mix Types.......................................................................................................................... ..................... 139 Table A. 1: Temperature, pressure, and relative humidity at times of UCPRC testing ............................ 191 Table A. 2: Aggregate Gradation ( percent passing each sieve by mass) for SB and NB Sections............ 192 Table A. 10.1: Predicted Lifetime of Different Asphalt Mix Types with Respect to Roughness............ 204 Table A. 10.2: Predicted Lifetime of Different Asphalt Mix Types with Respect to Noise from First Model ............................................................................................................................... ....... 204 Table A. 10.3: Predicted Lifetime of Different Asphalt Mix Types with Respect to Noise from Second Model.......................................................................................................................... ....... 205 Table A. 10.4: Predicted Age to Occurrence of Bleeding of Different Asphalt Mix Types...................... 205 Table A. 10.5: Predicted Age to Occurrence of Raveling of Different Asphalt Mix Types..................... 206 xxii UCPRC RR 2009 01 UCPRC RR 2009 01 1 1. INTRODUCTION 1.1 Project Background The smoothness and quietness of pavements are receiving increased attention and importance as they affect quality of life issues for highway users and neighboring residents. Since the California Department of Transportation ( Caltrans) employs a variety of strategies and materials for maintaining and rehabilitating the state’s highways pavements, it has sought to identify the lives of those strategies and materials, and those of new candidates, that can maintain roadway smoothness and quietness for the longest time. To accomplish this, the Department established the Quieter Pavement Research ( QPR) Program. The Caltrans QPR program is intended to examine the impact of quieter pavements on traffic noise levels and to establish which pavement characteristics have the greatest impact on tire/ pavement noise. The program also aims to identify surface treatments, materials, and construction methods that will result in quieter pavements that are also safe, durable, and cost effective. The information gathered as part of the Caltrans QPR will be used to develop quieter pavement design features and specifications for noise abatement throughout the state. The QPR program includes several studies to evaluate the acoustic properties of pavements and the role that pavement surface characteristics play relative to tire/ pavement noise levels. The research presented in this report is part of one of these studies and is an element of the Caltrans Quieter Pavements Research ( QPR) Work Plan. The QPR Work Plan includes research on both asphalt and concrete pavement surfaces. For the flexible ( asphalt surfaced) pavement part of the QPR study, Caltrans previously identified a need for research into the acoustics, friction, and performance of asphalt pavement surfaces, and in November 2004 initiated Partnered Pavement Research Center Strategic Plan Element ( PPRC SPE) 4.16 as a response. Among its other objectives, PPRC SPE 4.16 developed preliminary performance estimates for current Caltrans asphalt surfaces— including DGAC, OGAC, RAC G, and RAC O as part of a factorial experiment— and a number of experimental asphalt surfaces with respect to tire/ pavement noise, permeability, macrotexture, microtexture, smoothness, and surface distress development. ( Note that the technical names for these mixes have changed in the new Section 39 of the Standard Specifications. The names in use at the start of PPRC SPE 4.16 have been maintained in this report for consistency with previous reports). Those performance estimates were based on data collected during field tests and laboratory testing of 2 UCPRC RR 2009 01 cores in the first two years of the study. The results of the first two years of data collection, modeling, and performance predictions are summarized in Reference ( 1). PPRC SPE 4.19, titled “ Third Year Field Evaluation of Tire/ Pavement Noise, IRI, Macrotexture, and Surface Condition of Flexible Pavements,” was initiated in September 2007. The results presented in this report are updated performance estimates from the third year of measurements on most of the pavement sections included in the PPRC SPE 4.16 project, combined with the first two years of data. Several new sections were also tested for the first time as part of this project. 1.2 Project Purpose and Objectives The purpose of PPRC SPE 4.19 is to perform a third year of measurement of tire/ pavement noise, surface condition, ride quality, and macrotexture of up to 74 flexible pavement sections in order to improve performance estimates for identifying the more durable, smoother, and quieter pavement types. The three years of data collected on the sections, including two years of data collected as part of PPRC SPE 4.16, were used to provide a preliminary table of estimated design lives for different treatments with respect to the variables measured. The objectives of PPRC SPE 4.19 are: Objective 1: To perform third year of noise, smoothness, and distress monitoring of PPRC SPE 4.16 sections. In July 2007 the UCPRC completed field work on the second year surface property monitoring of the PPRC SPE 4.16 sections. There were 74 sections monitored as part of PPRC SPE 4.16, comprised of a factorial of current Caltrans asphalt surface mixes, referred to as “ Quieter Pavement” or “ QP” sections, and a number of experimental surfaces referred to as “ Environmental” or “ ES” sections. The UCPRC conducted a third year data collection campaign on these sections. Following the PPRC SPE 4.19 work plan, no cores were taken nor were there required traffic closures. Noise, smoothness and macrotexture, and surface condition of each section were measured using the California On board Sound Intensity ( OBSI) method, laser profilometer, and visual condition survey ( walking survey from the shoulder), respectively. Objective 2: To conduct noise, smoothness and distress monitoring on new field sections identified to have the potential to be more durable, smoother, and quieter, or that perform under conditions not included in the previous testing. UCPRC RR 2009 01 3 The same methods noted in Objective 1 were used to evaluate sections not previously included in PPRC SPE 4.16, including asphalt and concrete surfaces. An estimated maximum of 10 sections selected by Caltrans were to be included as part of this objective. In the case of new sections, measurements were to be conducted as much as scheduling allowed before and after construction. Objective 3: To develop pavement a temperature correction for OBSI data and upgrades to the instrumented noise car. This objective involved measuring some sections at various temperatures within a short time period in order to quantify the effect of pavement temperature on the noise levels and to determine correction formulas for normalizing OBSI measurements. The transition from a single sound intensity probes to double probes was to be done as part of this project, as well as any software development and updates associated with improved data collection practices. Objective 4: To analyze the results and to model them where applicable. Analyze results of the measurements, investigate trends, classify pavements with respect to durability, smoothness, and noise levels, and develop predictive models where possible to investigate trends and predict future performance. The database generated during PPRC SPE 4.16 was used in this part of the study, pooled with the third year measurements. Objective 5: To develop a preliminary table of expected lives for flexible pavement surfaces. Analyze the results of Objective 4, and develop a preliminary table of estimated design lives for flexible pavement surfaces tested with respect to durability, smoothness, and noise levels. Traffic and climate condition effects on life were to be included in the table where data is available. This report documents the work completed for Objectives 1, 2, 4, and 5. The work completed as part of Objective 3 is documented in a separate report. 1.3 Experiment Factorial for Third Year Measurements A factorial was developed for current Caltrans asphalt surfaces as part of PPRC SPE 4.16, including DGAC, RAC G, OGAC, and RAC O. ( As noted earlier, although the names of materials have changed in the new Standard Specifications Section 39, the earlier names are used in this report to maintain consistency with earlier reports.) That factorial includes 51 sections, referred to as the Quieter Pavement ( QP) sections, which were selected based on climate region ( rainfall), traffic ( Average Daily Truck Traffic [ ADTT]), and years since construction at the time of the initial measurement ( referred to as Age 4 UCPRC RR 2009 01 Category and grouped at the time of the first year of measurements into: less than one year, one to four years, or four to eight years). These sections have been tested for three years. The first two years of data included coring, condition survey, permeability, and friction ( microtexture) tests performed within traffic closures; profile and tire/ pavement noise measurements performed at highway speeds with the instrumented noise car, and mix property testing on cores performed in the laboratory. In addition, several sections identified in other projects and 23 sections with new materials and control sections, referred to as the Environmental Sections ( ES) were also tested. Appendix A. 1: List of Test Sections Included in the Study shows specific test section information. Detailed project background for PPRC SPE 4.16— literature survey, experimental design, and data collection methodologies— can be found in the two year noise study report, “ Investigation of Noise, Durability, Permeability, and Friction Performance Trends for Asphaltic Pavement Surface Types: Firstand Second Year Results.” ( 2) Most of the same data collection methodologies were continued in the third year but on a smaller scale, and coring, permeability, and friction tests were not conducted. Also, in the third year a Standard Reference Test Tire ( SRTT) was used for all noise measurements rather than the AquaTred tire used for the first two years of measurement. All measurements from the first two years with the AquaTred tire were converted to equivalent noise levels using the SRTT tire using a correlation developed by the UCPRC as part of this project. The details of the correlation are shown in Appendix A. 2: Correlation Between Aquatred 3 Tire OBSI and SRTT OBSI. Air density adjustments were applied to all data from all three years. Some pavement sections had failed by the third year and were dropped out from the survey. Table 1.1 shows the number of sections surveyed for various performance measures in the three years. A similar collection of data for the fourth year is scheduled for spring 2009. UCPRC RR 2009 01 5 Table 1.1: Number of Sections with Valid Measurements in Three Years Year 1 ( Phase 1) Year 2 ( Phase 2) Year 3 ( Phase 3) Tire/ Pavement Noise ( OBSI California)* 76 71 65 Roughness ( ASTM E 1926) 78 71 69 Macrotexture ( ASTM E 1845) 77 72 60 Friction ( ASTM E 303) 83 73 0 Air void Content/ Aggregate Gradation** 83 73 0 Permeability ( NCAT falling head) 78 73 0 Pavement Distresses** 84 84 73 * ASTM and AASHTO methods currently being standardized based on California experience. ** See Reference ( 2) for method description. 1.4 Scope of this Report Chapters 2, 3, 4, and 5 present results and analysis for the current Caltrans asphalt surfaces: DGAC, OGAC, RAC G, and RAC O. Chapters 2 present results for the International Roughness Index ( IRI). Chapter 3 presents results for Mean Profile Depth ( MPD), which is a measure of surface macrotexture related to high speed skid resistance and also an indicator of raveling and bleeding. Chapter 4 presents the results and analysis of measurements of surface distresses, including bleeding, rutting, transverse cracking, raveling, and wheelpath cracking. Chapter 5 presents results and analysis of On Board Sound Intensity ( OBSI) measurements of tire/ pavement noise. Findings are summarized at the end of each chapter. Chapter 6 presents an update of performance measures on the experimental test sections referred to as “ Environmental Sections.” Chapter 7 presents results and analysis from OBSI and other performance measurements on asphalt and concrete surfaces included in the study for the first time in Year 3. Chapter 8 presents an update of the PPRC SPE 4.16 estimates of pavement life based on new regression equations for each of the performance measures presented in Chapters 2, 3, 4, and 5. A summary of conclusions and recommendations appears in Chapter 9. 6 UCPRC RR 2009 01 UCPRC RR 2009 01 7 2. SURFACE PROFILE RESULTS AND ANALYSIS: IRI International Roughness Index ( IRI) was measured in the third year to evaluate the change in surface roughness of asphalt pavements. The IRI measurements were collected every meter in both the left and right wheelpaths. The average of the two wheelpath measurements along the whole length of each pavement section was used in the analysis. The analysis of the IRI answers two questions: • What pavement characteristics affect IRI? o Are initial IRI and IRI changes with time different for rubberized and nonrubberized mixes? o Are initial IRI and IRI changes with time different for open graded and dense graded mixes? • How do traffic and climate affect IRI? Hypotheses regarding the effects of the explanatory variables on IRI are discussed in Reference ( 2), and will be revisited in more detail at the conclusion of the fourth year of measurement, analysis, and modeling. 2.1 Descriptive Analysis Figure 2.1 shows the average IRI measured in three consecutive years for individual pavement sections of four mix types: DGAC, OGAC, RAC G, and RAC O. The first data point for each section is shown at the age of the section when the first measurement was taken, with Year One defined as the year of construction. It should be noted that the IRI values at the time the overlays were constructed or soon thereafter is unknown except for those sections that were tested very soon after construction. It should also be noted that the current condition of the pavement layers beneath the overlays is not known. Section IDs are listed in the figure legends. Some sections showed a decrease of IRI in the second or third survey year. Small reductions in IRI with age can be attributed to measurement errors. However, a couple of sections show a significant decrease in IRI, specifically QP 09 ( DGAC) and QP 20 ( OGAC). Section QP 09 has a large patch in the middle and section QP 20 is located on a steep hill. It is uncertain why the IRI decreased on these sections, either due to difficulty in measurement such as retracing the same 8 UCPRC RR 2009 01 wheelpath, or road maintenance. These two sections are treated as outliers and will be removed from the subsequent analysis. It can be seen from Figure 2.1 that IRI increased with age for many pavement sections. This is expected because pavement conditions deteriorate with age due to traffic and environmental effects. However, some sections, particularly OGAC sections, showed little change in IRI in the three year survey period. Figure 2.2 is a box plot that shows the variation in IRI values for different mix types, including two Fmixes, across all three years of measurement. In all of the box plots shown in this report the white bar is the median value, the “ x” is the mean value, the upper and lower edges of the purple box are the 75th and 25th percentiles respectively, and the upper and lower brackets are the upper and lower extreme values respectively. According to the plot, except for the OGAC F mixes, the average IRI values of the different mixes are close to each other, and most of the sections have acceptable IRI values based on the FHWA criteria of 170 in./ mi ( 2.4 m/ km) ( 2). However, one DGAC pavement shows high IRI values (> 3.6 m/ km) that would trigger Caltrans maintenance action. From Figure 2.1 it can be seen that this is an old pavement that was 14 years old at the beginning of the survey. Figure 2.3 shows the IRI values for different mix types for the three initial age categories of less than one year, one to four years, and greater than four years. This plot is similar to the plot based on the first two years’ data ( 2). That is, IRI values increase with age for RAC O and DGAC mixes but show no trend for OGAC and RAC G mixes. Figure 2.4 shows the time trend of IRI across the three years of data collection, with each year of measurement identified as “ Phase ID,” for different mix types for three age categories. As the figure shows, IRI generally increases with time. For newly paved mixes ( Age Category “< 1 year”), IRI varied insignificantly for DGAC, OGAC, and RAC O in the first three years. On the other hand, RAC G showed a significant increase in IRI in the first three years after construction. From Figure 2.1 it can be seen that this is due to the rapid increase in IRI on one pavement section. This section is QP 26, which is located on Highway 280 in Santa Clara County in Caltrans District 4. The reason for the rapid increase in IRI at this section is unknown. This section also showed a rapid increase in macrotexture ( Mean Profile Depth [ MPD] increased from 800 microns in the first year to 2,150 microns in the third year after construction) and the distresses raveling and segregation in the third year. Cores from this section taken within a year of UCPRC RR 2009 01 9 construction showed measured air void contents of approximately 9 percent, which indicates that insufficient compaction might have caused the rapid IRI increase. If QP 26 is excluded, IRI also varied insignificantly for RAC G in the first three years. ( Note: IRI values have been reported in m/ km since data collection began. For reference, some critical IRI values are shown below in inches per mile ( 3): Criteria in./ mim/ km FHWA “ very good” maximum value 60 0.95 FHWA “ good” maximum value 94 1.48 FHWA “ fair” for Interstates maximum value 119 1.88 FHWA “ fair” for non Interstates and “ mediocre” for Interstate maximum values 170 2.68 FHWA “ mediocre” for non Interstate maximum value 220 3.47 Caltrans rigid pavement PMS prioritization trigger 213 3.36 Caltrans flexible pavement PMS prioritization trigger 224 3.54 Age ( year) IRI ( m/ km) 0 5 10 15 20 0 1 2 3 4 5 DGAC 06 N434 ES 20 QP 06 QP 09 QP 15 QP 21 QP 30 QP 40 Age ( year) IRI ( m/ km) 0 5 10 15 20 0 1 2 3 4 5 OGAC ES 11 QP 03 QP 04 QP 13 QP 20 QP 23 QP 28 QP 29 QP 44 QP 45 Age ( year) IRI ( m/ km) 0 5 10 15 20 0 1 2 3 4 5 RAC G ES 12 QP 02 QP 05 QP 14 QP 46 Age ( year) IRI ( m/ km) 0 5 10 15 20 0 1 2 3 4 5 RAC O ES 21 ES 22 QP 01 QP 12 QP 24 QP 34 QP 36 QP 51 Figure 2.1: IRI trends over three years for each pavement section. 10 UCPRC RR 2009 01 1 2 3 4 5 6 IRI ( m/ km) x x x x x x DGAC OGAC OGAC F mix RAC G RAC O RAC O F mix Mix type Figure 2.2: Variation in IRI values for different mix types for all three years of pooled data and all initial ages. 1 2 3 4 IRI ( m/ km) x x x x x x x x x x x x 2 1 2 2 2 3 4 1 4 2 4 3 6 1 6 2 6 3 7 1 7 2 7 3 Age Category, Mix type Figure 2.3: Variation in IRI values for different mix types for different initial ages ( Age category in years) for all three years pooled data. Age Category < 1 1 4 > 4 < 1 1 4 > 4 < 1 1 4 > 4 < 1 1 4 > 4 Mix Type DGAC OGAC RAC G RAC O UCPRC RR 2009 01 11 1 2 3 4 IRI ( m/ km) x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x 2 1 1 2 1 2 2 1 3 2 2 1 2 2 2 2 2 3 2 3 1 2 3 2 2 3 3 4 1 1 4 1 2 4 1 3 4 2 1 4 2 2 4 2 3 4 3 1 4 3 2 4 3 3 6 1 1 6 1 2 6 1 3 6 2 1 6 2 2 6 2 3 6 3 1 6 3 2 6 3 3 7 1 1 7 1 2 7 1 3 7 2 1 7 2 2 7 2 3 7 3 1 7 3 2 7 3 3 Phase ID, Age Category, Mix type DGAC OGAC RAC G RAC O Phase ID 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 Age Category < 1 1 4 > 4 < 1 1 4 > 4 < 1 1 4 > 4 < 1 1 4 > 4 Figure 2.4: Comparison of IRI values for different mix types at different ages for first, second, and third years of data collection ( Phase ID showing Years 1, 2, and 3). 2.2. Regression Analysis Regression analysis was performed to evaluate the effects of traffic, climate, distresses, and pavement materials on IRI values. First, a single variable regression analysis was conducted to prescreen significant factors to be included in a multiple regression model. Estimates of the coefficient of the explanatory variable and the constant term along with their P values and the coefficient of determination ( R2) for each model are given in Table 2.1. The P values less than 0.05, indicating highly significant variables, are shown in bold. The results in Table 2.1 show that IRI tends to be significantly affected by presence of distresses and environmental factors. The signs of the estimated coefficients indicate that the greater the distresses ( fatigue cracking, raveling, rutting, and bleeding) and rainfall, the higher the IRI. These are expected. High temperature days, on the other hand, seem to reduce IRI. This may be due to higher temperatures making it easier to obtain smoothness at the time of construction. Table 2.1 also shows that the inclusion of rubber tends to reduce IRI. 12 UCPRC RR 2009 01 Table 2.1: Regression Analysis of Single Variable Models for IRI Model Number Variable Name Coefficient P value Constant Term R2 1 Age ( year) 0.113 < 0.001 1.172 0.144 2 Air void Content (%)  0.00823 0.757 1.555 0.001 3 Mix Type  0.387 0.076 1.783 0.074 4 Rubber Inclusion  0.244 0.018 1.643 0.033 5 MPD ( micron) 0.000285 0.003 1.057 0.054 6 Presence of Fatigue Cracking 0.441 0.026 1.473 0.031 7 Presence of Raveling 0.299 0.013 1.454 0.038 8 Presence of Rutting 0.911 < 0.001 1.442 0.100 9 Presence of Transverse Cracking 0.188 0.546 1.497 0.002 10 Presence of Bleeding 0.439 0.015 1.472 0.036 11 Average Annual Rainfall ( mm) 0.000131 0.051 1.397 0.023 12 Age* Average Annual Rainfall ( mm) 0.000198 < 0.001 1.151 0.259 13 Average Annual Wet Days 0.000862 0.040 1.371 0.025 14 Age* Average Annual Wet Days 0.00123 < 0.001 1.219 0.180 15 Average Annual Max. Daily Air Temp ( º C)  0.0841 < 0.001 3.735 0.155 16 Annual Number of Days > 30 º C  0.00409 < 0.001 1.879 0.141 17 Annual Degree Days > 30 º C  0.000116 < 0.001 1.870 0.142 18 Annual FT Cycles  0.00600 0.034 1.622 0.027 19 Annual AADTT per Coring Lane  2.23e 5 0.297 1.563 0.007 20 Annual ESALs per Coring Lane  6.91e 8 0.123 1.572 0.014 Based on the results in Table 2.1, multiple regression analysis was conducted to account for the effect of various factors simultaneously. First a pair wise correlation analysis was performed to avoid highlycorrelated variables in the same model. It was found that air void content and MPD are highly correlated. MPD is also partly determined by the maximum aggregate size in the mix. Average Annual Maximum Daily Air Temperature is highly correlated with Annual Number of Days > 30 º C and Annual Degree Days > 30 º C. AADTT per Coring Lane is highly correlated with Annual ESALs per Coring Lane. In the multiple regression analysis, only one variable in each highly correlated variable pair will be considered. Preliminary analysis revealed that the error terms from multiple regression have nonconstant variance, so a reciprocal square root transformation ( Y' = 1/ IRI ) was applied to the dependent variable, IRI, to stabilize the variance of the error terms. Because mix properties are highly affected by mix types ( e. g., higher air void contents in OGAC mixes than in DGAC mixes), it is not appropriate to incorporate both mix property variables ( e. g., air void UCPRC RR 2009 01 13 content) and mix type in the same model. To determine the effects of mix type and mix properties on IRI, separate regression models were proposed. In the first model, only the mix type ( categorical variable) and environmental and traffic factors are included as the independent variables, while mix property variables are excluded. The regression equation, Equation 2.1, is 1 ( / ) 0.889612 0.021589 ( ) 0.056035 ( ) 0.037902 ( ) 0.102960 ( ) 0.000074 ( ) 0.000603 30 0.000012 IRI m km Age year ind MixTypeOGAC ind MixTypeRAC G ind MixTypeRAC O AverageAnnualRainfall mm NumberOfDays C AADTTinCori = − × + × + × − + × − − × + × > − × ngLane+ 0.001576×AnnualFTCycles ( 2.1) where ind(⋅) is an indicator function, 1 if the variable in the parentheses is true and 0 if false. The coefficient of the ind(⋅) function represents the difference in the effects of other mix types and DGAC. The estimated values and P values of the parameters are shown below, with variables that are significant at the 95 percent confidence interval shown in bold type. Value Std. Error t value P value ( Intercept) 0.889612 0.043695 20.3594 < 0.0001 Age  0.021589 0.003540  6.0980 < 0.0001 MixTypeOGAC 0.056035 0.028193 1.9875 0.0486 MixTypeRAC G 0.037902 0.030027 1.2623 0.2087 MixTypeRAC O 0.102960 0.026666 3.8611 0.0002 AvgAnnualRainfall  0.000074 0.000028  2.6733 0.0083 NoDaysTempGT30 0.000603 0.000218 2.7692 0.0063 AADTTCoringLane  0.000012 0.000007  1.7690 0.0788 AnnualFTCycles 0.001576 0.000819 1.9235 0.0562 Residual standard error: 0.1236 on 157 degrees of freedom; Multiple R Squared: 0.38. It can be seen that at the 95 percent confidence level, age, mix type, average annual rainfall, and number of days > 30 º C significantly affect IRI. IRI increases with Age and Average Annual Rainfall, but decreases with the Number of Days > 30 º C. Among the three pavement types, OGAC, RAC G, and RACO, all have lower initial IRI than DGAC, but only OGAC and RAC O are statistically significantly different from DGAC. Initially the interaction terms between Age and Mix Type were included in the model, but none of them were statistically significant, which indicates that the growth rate of IRI is not statistically different among the four pavement types. In the second model, Mix Type variable is replaced with Mix Property variables and the model is estimated for each Mix Type separately. The regression equations, Equation 2.2 through Equation 2.5, are 14 UCPRC RR 2009 01 For DGAC pavements: 1 ( / ) 0.888563 0.01644 ( ) 0.000262 0.014248 log( )( / sec) 0.000064 ( ) 0.000718 30 0.0000033 0.003385 IRI m km Age year MPD Permeability cm AverageAnnualRainfall mm NumberOfDays C AADTTinCoringLane AnnualFTCycles = − × − × − × − × + × > + × + × ( 2.2) Value Std. Error t value P value ( Intercept) 0.888563 0.108166 8.2148 < 0.0001 Age  0.016440 0.006102  2.6940 0.0116 MPD  0.000262 0.000128  2.0384 0.0507 logPerm  0.014248 0.011623  1.2259 0.2301 AvgAnnualRainfall  0.000064 0.000038  1.6820 0.1033 NoDaysTempGT30 0.000718 0.000396 1.8153 0.0798 AADTTCoringLane 0.0000033 0.000010 0.3254 0.7472 AnnualFTCycles 0.003385 0.001813 1.8674 0.0720 Residual standard error: 0.0959 on 29 degrees of freedom; Multiple R Squared: 0.71. For OGAC pavements: 1 ( / ) 0.834436 0.022964 ( ) 0.000304 ( ) 0.006099 log( )( / sec) 0.000231 ( ) 0.001301 30 0.0000029 0.003270 IRI m km Age year MPD micron Permeability cm AverageAnnualRainfall mm NumberOfDays C AADTTinCoringLane AnnualF = + × − × − × + × + × > + × + × TCycles ( 2.3) Value Std. Error t value P value ( Intercept) 0.834436 0.155224 5.3757 < 0.0001 Age 0.022964 0.013217 1.7375 0.0925 MPD  0.000304 0.000101  3.0149 0.0052 logPerm  0.006099 0.008093  0.7536 0.4570 AvgAnnualRainfall 0.000231 0.000137 1.6831 0.1027 NoDaysTempGT30 0.001301 0.000558 2.3303 0.0267 AADTTCoringLane 0.0000029 0.000019 0.1512 0.8808 AnnualFTCycles 0.003270 0.002053 1.5930 0.1216 Residual standard error: 0.1058 on 30 degrees of freedom; Multiple R Squared: 0.49. UCPRC RR 2009 01 15 For RAC G pavements: 1 ( / ) 1.165986 0.018908 ( ) 0.000178 ( ) 0.009595 log( )( / sec) 0.000083 ( ) 0.00037 30 0.0000697 0.001622 IRI m km Age year MPD micron Permeability cm AverageAnnualRainfall mm NumberOfDays C AADTTinCoringLane AnnualFT = − × − × − × − × − × > − × − × Cycles ( 2.4) Value Std. Error t value P value ( Intercept) 1.165986 0.090730 12.8511 < 0.0001 Age  0.018908 0.010672  1.7717 0.0897 MPD  0.000178 0.000097  1.8360 0.0793 logPerm  0.009595 0.008499  1.1289 0.2706 AvgAnnualRainfall  0.000083 0.000056  1.4912 0.1495 NoDaysTempGT30  0.000037 0.000476  0.0769 0.9393 AADTTCoringLane  0.0000697 0.000021  3.3738 0.0026 AnnualFTCycles  0.001622 0.001841  0.8815 0.3872 Residual standard error: 0.08480 on 23 degrees of freedom; Multiple R Squared: 0.67. For RAC O pavements: 1 ( / ) 0.698788 0.036292 ( ) 0.000139 ( ) 0.012359 log( )( / sec) 0.000051 ( ) 0.001275 30 0.0000024 0.000269 IRI m km Age year MPD micron Permeability cm AverageAnnualRainfall mm NumberOfDays C AADTTinCoringLane AnnualF = − × + × − × + × + × > − × + × TCycles ( 2.5) Value Std. Error t value P value ( Intercept) 0.698788 0.151179 4.6223 < 0.0001 Age  0.036292 0.009227  3.9331 0.0003 MPD 0.000139 0.000103 1.3496 0.1846 logPerm  0.012359 0.010380  1.1907 0.2406 AvgAnnualRainfall 0.000051 0.000061 0.8365 0.4077 NoDaysTempGT30 0.001275 0.000506 2.5199 0.0157 AADTTCoringLane  0.0000024 0.000012  0.1947 0.8466 AnnualFTCycles 0.000269 0.001433 0.1878 0.8520 Residual standard error: 0.1317 on 41 degrees of freedom; Multiple R Squared: 0.38. The results show that for DGAC pavements, only age is significant at the 95 percent confidence level, while none of the mix, traffic, and environmental variables is significant. For RAC O pavements, in addition to Age, Number of Days > 30 º C is also significant. For OGAC pavements, IRI increases with MPD, but does not change significantly with Age. IRI on open graded pavements ( OGAC and RAC O) decreases with the Number of Days > 30 º C, indicating that open graded pavements are smoother in high temperature regions than in low temperature regions. Traffic volume is a significant variable for RAC G pavements. Higher traffic volume leads to higher IRI values. 16 UCPRC RR 2009 01 2.3 Summary of Findings The following findings were obtained regarding roughness: 1. Except for an old DGAC pavement, all sections are smoother than the Caltrans Pavement Management System IRI trigger criterion of 3.6 m/ km ( 224 in./ mi). 2. Rubberized open graded mixes have lower initial IRI values than nonrubberized open graded mixes; rubberized gap graded mixes have lower initial IRI values than nonrubberized dense graded mixes. 3. The surface types OGAC, RAC G, and RAC O all have lower initial IRI than DGAC, but only OGAC and RAC O are statistically significantly different from DGAC. Monitoring over three years indicates that IRI increases with age on DGAC, RAC G, and RAC O pavements, but that age does not have a statistically significant effect on increasing IRI on OGAC pavements. 4. Open graded pavements ( OGAC and RAC O) are smoother in high temperature regions than in low temperature regions. 5. The IRI of OGAC pavements increases with increasing MPD. The monitoring performed to date shows that traffic volume significantly affects IRI only on RAC G pavements, with higher traffic volumes showing higher IRI values. UCPRC RR 2009 01 17 3. SURFACE PROFILE RESULTS AND ANALYSIS: MEAN PROFILE DEPTH Macrotexture was measured in the third year, but microtexture was not because during the third year survey time traffic was not closed. Macrotexture was measured by UCPRC using the same profilometer used in the previous two years, and it was reported in terms of mean profile depth ( MPD) and root mean square ( RMS) of profile deviations ( RMS). Because MPD and RMS are highly correlated, only analysis of the MPD is presented in this report. The analysis of the MPD answers these questions: • What pavement characteristics affect MPD? o Are initial MPD and change of MPD with time different for rubberized and nonrubberized mixes? o Are the initial MPD and MPD progression different for open graded and dense graded mixes? • How do traffic and climate affect MPD? The hypotheses regarding the effects of the explanatory variables on MPD are discussed in Reference ( 1) and will be revisited in more detail at the conclusion of the fourth year of measurement, analysis, and modeling. 3.1 Descriptive Analysis Figure 3.1 shows the average MPD measured in three consecutive years for individual pavement sections of four mix types: DGAC, OGAC, RAC G, and RAC O. It was expected that MPD would increase with pavement age, as pavements deteriorate with time, particularly in the form of increased raveling. The plots in Figure 3.1 confirmed this expectation. Some of the sections, whose numbers are listed in the legend, showed lower MPDs in the later years but the differences were small and can be attributed to measurement errors or other random variations. A few sections, however, show significantly different MPD values. These sections include the three newly paved OGAC pavements: QP 20, QP 44, and QP 45, and a RAC G pavement ( QP 26). The three newly paved OGAC sections all showed significantly high initial MPD values. As noted earlier, Section QP 20 is located on a steep hill and may have experienced compaction problems during construction that led to the high MPD. QP 44 is on I 80, in District 3 in 18 UCPRC RR 2009 01 Placer County, where both annual rainfall and traffic volume are very high. A pavement condition survey conducted one year after construction revealed a very rough texture with only angular coarse aggregates exposed on the surface. Although QP 45, which is on I 80 in District 3 in Yolo County, also has high traffic volume the reason for the high initial MPD values remains unclear. Lastly, QP 26 showed a rapid increase in macrotexture ( MPD increased from 800 microns in the first year after construction to 2,150 microns in the third year) and the distresses raveling and segregation in the third year. As discussed earlier, the mix design and/ or compaction for this section might not have been sufficient. Consequently, these four sections are treated as outliers and will be removed from the statistical analysis. Age ( year) MPD ( m/ km) 0 5 10 15 20 0 500 1000 2000 DGAC QP 07 QP 16 Age ( year) MPD ( m/ km) 0 5 10 15 20 0 500 1000 2000 OGAC QP 13 QP 22 QP 29 QP 44 QP 45 Age ( year) MPD ( m/ km) 0 5 10 15 20 0 500 1000 2000 RAC G QP 05 QP 14 Age ( year) MPD ( m/ km) 0 5 10 15 20 0 500 1000 2000 RAC O ES 23 QP 01 QP 17 QP 34 QP 51 Figure 3.1: MPD trend over three years for each pavement section. Figure 3.2 shows the variation in MPD values for different mix types, including two F mixes, based on the three year survey data. The information conveyed in the plots is the same as that in the plot based on the first two years’ survey data ( 2). That is, the two F mixes have the highest MPD. The RAC G mixes have higher MPD values than the dense graded mixes, while the open graded mixes have higher MPD values than the RAC G mixes. Among the two open graded mixes, RAC O mixes have lower MPD values than OGAC mixes. UCPRC RR 2009 01 19 Figure 3.3 shows the time trend of MPD in three years for different mix types for three age categories. As the figure shows, MPD generally increases with pavement age for the same pavement section. Except for the four outlier pavement sections, this increase trend is also obvious among different pavement sections of the same mix type. Phase ID in the figure is the year of data collection, either 1, 2 or 3. 5 00 1 000 1 500 2 000 MP D ( m ic ron) x x x x x x DGAC OGAC OGAC F mix RAC G RAC O RAC O F mix Mix type Figure 3.2: Variation in MPD values for different mix types for pooled data for all three years and all initial ages. 500 1000 1500 2000 M PD ( micron) x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x 2 1 1 2 1 2 2 1 3 2 2 1 2 2 2 2 2 3 2 3 1 2 3 2 2 3 3 4 1 1 4 1 2 4 1 3 4 2 1 4 2 2 4 2 3 4 3 1 4 3 2 4 3 3 6 1 1 6 1 2 6 1 3 6 2 1 6 2 2 6 2 3 6 3 1 6 3 2 6 3 3 7 1 1 7 1 2 7 1 3 7 2 1 7 2 2 7 2 3 7 3 1 7 3 2 7 3 3 Phase ID Age Category Mix type DGAC OGAC RAC G RAC O Phase ID 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 Age Category < 1 1 4 > 4 < 1 1 4 > 4 < 1 1 4 > 4 < 1 1 4 > 4 Figure 3.3: Comparison of MPD values for different mix types for different initial age categories ( Age Category) and for first, second, and third years of data collection ( Phase ID). 20 UCPRC RR 2009 01 3.2 Regression Analysis Regression analysis was performed to evaluate the effects of traffic, climate, distresses, and pavement materials on MPD values. First, a single variable regression analysis was conducted to prescreen significant factors to be included in a multiple regression model. Estimates of the coefficient of the explanatory variable and the constant term along with their P values and the coefficient of determination ( R2) for each model are given in Table 3.1. The P values less than 0.05 are shown in bold. Descriptions of the variables are provided in Reference ( 2). A few of the less common variables are described below. Cc is the Coefficient of curvature. Cc = D30/ D10 * D60, where D10 is the sieve size through which 10 percent of the aggregate passes ( mm), D30 is the sieve size through which 30 percent of the aggregate passes ( mm), and D60 is the sieve size through which 60 percent of the material passes ( mm). Cu is the Coefficient of uniformity: Cu = D60/ D10. Fineness modulus is a measure of the uniformity of the aggregate gradation. The higher the fineness modulus, the coarser the asphalt mix ( a higher percentage of coarse material) and the more uniform the gradation. Fineness Modulus is calculated as F. M. = ( Σ percent material retained on each sieve) / 100. Table 3.1: Regression Analysis of Single Variable Models for MPD Model Number Variable Name Coefficient P value Constant Term R2 1 Age ( year) 38.073 < 0.001 897.950 0.108 2 Air void Content (%) 40.398 < 0.001 576.863 0.473 3 Mix Type 572.389 < 0.001 741.798 0.453 4 Rubber Inclusion  17.816 0.732 1064.270 0.001 5 Fineness Modulus 446.849 < 0.001  1173.064 0.379 6 NMAS ( mm)  47.519 < 0.001 1670.500 0.156 7 Cu  12.334 < 0.001 1310.232 0.361 8 Cc 7.839 0.564 1031.587 0.002 9 BPN  1.482 0.587 1146.537 0.002 10 Surface Thickness ( mm)  7.935 < 0.001 1360.557 0.173 11 IRI ( m/ km) 124.881 0.019 875.503 0.037 12 Presence of Rutting 156.453 0.061 1035.488 0.025 13 Presence of Bleeding 142.468 0.061 1033.051 0.025 14 Average Annual Rainfall ( mm) 0.069 0.208 1012.951 0.011 15 Average Annual Wet Days 0.882 0.087 989.715 0.020 16 Average Annual Max. Daily Air Temp ( º C)  21.335 0.042 1546.721 0.028 17 Annual Number of Days > 30 º C  1.046 0.048 1138.271 0.027 18 Annual Degree Days > 30 º C  0.029 0.054 1133.514 0.025 19 Annual FT Cycles 0.712 0.696 1044.804 0.001 20 Annual AADTT per Coring Lane 0.00144 0.681 1046.206 0.001 UCPRC RR 2009 01 21 The results in Table 3.1 show that MPD tends to be significantly affected by mix property variables, including air void content, fineness modulus, nominal maximum aggregate size ( NMAS), and aggregate coefficient of uniformity ( Cu). According to the estimated coefficients, increasing air void content and fineness modulus increases macrotexture, and increasing NMAS and Cu reduces macrotexture. An increase of macrotexture with an increase of NMAS is unexpected. This is likely due to pooling of denseand open graded mixes and the effect of other uncontrolled factors in the single variable model. Also, macrotexture seems to be smaller on thicker surface layers, probably due to better compaction of thicker layers. Higher temperature ( in terms of both maximum daily air temperature and the number of days with air temperature greater than 30 º C) tends to reduce macrotexture, which likely is due to easier aggregate reorientation and further mix compaction at high temperatures. Heavier daily traffic volume tends to increase macrotexture, which is most likely due to removal of fines around the larger stones in the surface. Based on the results in Table 3.1, multiple regression analysis was conducted to account for the effect of various factors simultaneously. Highly correlated independent variables are mutually excluded from the modeling. Two separate regression models were proposed to determine the effects of mix type and mix properties on MPD. In the first model, only the mix type ( categorical variable) and environmental and traffic factors are included as the independent variables, while mix property variables are excluded. The regression equation, Equation 3.1, is ( ) 838.2085 29.4579 ( ) 58.6352 ( ) 221.8027 ( ) 337.4369 ( ) 6.1771 ( ) 0.6911 ( ) 1.0294 30 0.0042 MPD micron Age year ind MixTypeOGAC ind MixTypeRAC G ind MixTypeRAC O NMAS mm Thickness mm NumberOfDays C AADTTinCoringL = + × + × + × − + × − − × − × − × > + × 68.0467 ( ) 19.0678 ( ) 8.6665 ( ) ane Age ind MixTypeOGAC Age ind MixTypeRAC G Age ind MixTypeRAC O + × × − × × − + × × − ( 3.1) where ind(⋅) is an indicator function, 1 if the variable in the parentheses is true and 0 if false. The estimated values and P values of the parameters are shown below. 22 UCPRC RR 2009 01 Value Std. Error t value P value ( Intercept) 838.2085 152.0913 5.5112 0.0000 Age 29.4279 14.1577 2.0786 0.0396 MixTypeOGAC 58.6352 126.1990 0.4646 0.6430 MixTypeRAC G 221.8027 91.8216 2.4156 0.0171 MixTypeRAC O 337.4369 87.7395 3.8459 0.0002 NMAS  6.1771 7.7526  0.7968 0.4270 Thickness  0.6911 1.2638  0.5469 0.5854 NoDaysTempGT30  1.0294 0.3550  2.8995 0.0044 AADTTCoringLane 0.0042 0.0109 0.3880 0.6987 AgeMixTypeOGAC 68.0467 23.0274 2.9550 0.0037 AgeMixTypeRAC G  19.0678 19.1255  0.9970 0.3206 AgeMixTypeRAC O 8.6665 18.4019 0.4710 0.6385 Residual standard error: 193.1 on 130 degrees of freedom; Multiple R Squared: 0.6325. It can be seen that at the 95 percent confidence level, age, mix type, and number of days > 30 º C significantly affect macrotexture. MPD increases with age, but decreases with the number of days > 30 º C. Among the three pavement types, OGAC, RAC G, and RAC O, all have higher initial MPD than DGAC, but OGAC is statistically insignificantly different from DGAC. This is likely due to the removal of the three newly paved OGAC pavement sections from the analysis. P values for the interaction terms between Age and Mix Type showed that the growth rate ( with age) of MPD of OGAC pavements is significantly higher than that of DGAC pavements. The growth rates of MPD of RAC G and RAC O pavements are not statistically different from those of DGAC pavements. In the second model, Mix Type variable is replaced with Mix Property variables and the model is estimated for each mix type separately. The regression equations, Equation 3.2 through Equation 3.5, are: For DGAC pavements: ( ) 93.7089 4.2910 (%) 47.8933 ( ) 283.2136 9.9487 ( ) 5.4209 ( ) 0.7087 30 0.0402 MPD micron AirVoid Age year FinenessModulus NMAS mm Thickness mm NumberOfDays C AADTTinCoringLane = − − × + × + × − × − × − × > − × ( 3.2) Value Std. Error t value P value ( Intercept)  93.7089 529.8210  0.1769 0.8612 AirVoid  4.2910 15.7801  0.2719 0.7882 Age 47.8933 13.0899 3.6588 0.0014 FinenessModulus 283.2136 156.2116 1.8130 0.0835 NMAS  9.9487 10.1549  0.9797 0.3379 Thickness  5.4209 1.8722  2.8955 0.0084 NoDaysTempGT30  0.7087 0.6382  1.1105 0.2788 AADTTCoringLane  0.0402 0.0177  2.2674 0.0335 Residual standard error: 133.1 on 22 degrees of freedom; Multiple R Squared: 0.601. UCPRC RR 2009 01 23 For OGAC pavements: ( ) 645.6240 0.4917 (%) 103.6224 ( ) 274.1456 1.9169 ( ) 0.457 ( ) 0.5966 30 0.0089 MPD micron AirVoid Age year FinenessModulus NMAS mm Thickness mm NumberOfDays C AADTTinCoringLane = − − × + × + × − × − × − × > − × ( 3.3) Value Std. Error t value P value ( Intercept)  645.6240 338.4451  1.9076 0.0675 AirVoid  0.4917 10.0302  0.0490 0.9613 Age 103.6224 10.5024 9.8666 0.0000 FinenessModulus 274.1456 93.6918 2.9260 0.0070 NMAS  1.9169 15.5844  0.1230 0.9031 Thickness  0.4570 1.5415  0.2965 0.7692 NoDaysTempGT30  0.5966 0.3698  1.6131 0.1188 AADTTCoringLane  0.0089 0.0171  0.5201 0.6074 Residual standard error: 88.19 on 26 degrees of freedom; Multiple R Squared: 0.9143. For RAC G pavements: ( ) 622.7423 9.1326 (%) 14.3359 ( ) 403.7994 28.119 ( ) 2.6337 ( ) 0.7899 30 0.0348 MPD micron AirVoid Age year FinenessModulus NMAS mm Thickness mm NumberOfDays C AADTTinCoringLane = − − × + × + × − × − × − × > − × ( 3.4) Value Std. Error t value P value ( Intercept)  622.7423 1241.1985  0.5017 0.6206 AirVoid  9.1326 17.1338  0.5330 0.5991 Age 14.3359 19.8725 0.7214 0.4779 FinenessModulus 403.7994 306.2677 1.3185 0.2003 NMAS  28.1190 25.1487  1.1181 0.2751 Thickness  2.6337 3.1514  0.8357 0.4119 NoDaysTempGT30 0.7899 0.9248 0.8541 0.4018 AADTTCoringLane  0.0348 0.0442  0.7874 0.4391 Residual standard error: 205.9 on 23 degrees of freedom; Multiple R Squared: 0.2231. For RAC O pavements: ( ) 358.6533 1.4151 (%) 18.9136 ( ) 476.3388 145.9686 ( ) 5.2328 ( ) 1.7772 30 0.0048 MPD micron AirVoid Age year FinenessModulus NMAS mm Thickness mm NumberOfDays C AADTTinCoringLane = − × + × + × − × + × − × > + × ( 3.5) Value Std. Error t value P value ( Intercept) 358.6533 827.2495 0.4335 0.6671 AirVoid  1.4151 10.8988  0.1298 0.8974 Age 18.9136 12.2301 1.5465 0.1303 FinenessModulus 476.3388 171.6864 2.7745 0.0085 NMAS  145.9686 30.3248  4.8135 < 0.0001 Thickness 5.2328 3.8549 1.3574 0.1826 NoDaysTempGT30  1.7772 0.6327  2.8089 0.0078 AADTTCoringLane 0.0048 0.0145 0.3298 0.7434 Residual standard error: 167 on 38 degrees of freedom; Multiple R Squared: 0.6447. 24 UCPRC RR 2009 01 The results show that within each mix type, air void content has no significant effect on the value of MPD. Fineness modulus is significant in affecting the macrotexture of open graded pavements, including both OGAC and RAC O, marginally significant in affecting the macrotexture of DGAC pavements, and insignificant for RAC G pavements. Generally, macrotexture increases with fineness modulus, with increasing fineness modulus indicating a coarser gradation. Layer thickness is only significant on DGAC pavements. Thicker DGAC layers have lower macrotexture, probably due to better compaction of thicker layers. Higher temperature duration, in terms of number of days with air temperature greater than 30 º C, is a significant factor on RAC O pavements but not on other types of pavement. The effect of pavement age on macrotexture is much more prominent ( in terms of both statistical significance and practical significance) on nonrubberized pavements ( DGAC and OGAC) than on rubberized pavements ( RAC G, and RAC O). 3.3 Summary of Findings The following findings were obtained regarding macrotexture: 1. Among all mixes investigated, F mixes have the highest MPD. RAC G mixes have higher MPD values than the dense graded mixes, while open graded mixes have higher MPD values than RAC G mixes. Among the two open graded mixes, RAC O mixes have lower MPD values than OGAC mixes. 2. MPD generally increases with pavement age. The age effect on macrotexture is much more prominent ( in terms of both statistical significance and practical significance) on nonrubberized pavements ( DGAC and OGAC) than on rubberized pavements ( RAC G, and RAC O). The growth rate ( with age) of MPD is significantly higher on OGAC pavements than on DGAC pavements. The growth rates of MPD of RAC G and RAC O pavements are not statistically different from those of DGAC pavements. 3. Within each mix type, air void content has no significant effect on the value of MPD. 4. Fineness modulus is significant in affecting the macrotexture of open graded pavements, including both OGAC and RAC O, marginally significant in affecting the macrotexture of DGAC pavements, and insignificant for RAC G pavements. Generally the coarser the mix gradation is ( i. e., higher fineness modulus), the larger the MPD. 5. Layer thickness is only significant on DGAC pavements. Thicker DGAC layers have lower macrotexture, probably due to better compaction of thicker layers. 6. The macrotexture of RAC O pavements decreases with the number of high temperature days. UCPRC RR 2009 01 25 4. SURFACE DISTRESS RESULTS AND ANALYSIS Traffic closures were not included in the scope of the the third year survey. Therefore, pavement conditions were evaluated using a method different from the one used the previous two years. In the first two years’ surveys, the truck lane was temporarily closed and pavement conditions were measured, visually assessed, and recorded on site during the traffic closure. During the third year survey, highresolution digital photos were taken from the shoulder along the whole length of each section, and pavement conditions were assessed afterwards, based on pavement surface images. A variety of flexible pavement distresses, consistent with the descriptions in the Caltrans Office Manual ( part of the Guide to the Investigation and Remediation of Distress in Flexible Pavements [ 4]), were recorded. It must be noted that some distresses such as rutting could not be evaluated accurately solely with surface images. Because of the differences in distress assessment in the first two years and the third year, some distresses were recorded as less severe in the third year than in the previous years. A basic assumption was made in post processing the distress data that the third year distress was no less than the second year. In this report, six major distress types, including bleeding, rutting, transverse/ reflective cracking, raveling, and wheelpath cracking, were analyzed for four pavement types: DGAC, OGAC, RAC G, and RAC O. The numbers of sections included in the survey are 16, 18, 11, and 20 for DGAC, OGAC, RAC G, and RAC O pavements, respectively. The evaluation of distresses answers these questions: • Do the initiation and progression of distresses differ for different mixes? • How do traffic and climate affect distress initiation and progression? The hypotheses regarding the effects of the explanatory variables on distress development are discussed in Reference ( 1), and will be revisited in more detail at the conclusion of the fourth year of measurement, analysis and modeling. The distresses present on the pavement surface at the time of construction of the overlays is not known. The current condition of the pavement layers beneath the overlays is also not known. 26 UCPRC RR 2009 01 4.1 Bleeding In the survey, bleeding is reported in terms of severity— low, medium, and high— and extent, expressed as the percentage of the total area with bleeding. In the analysis for this study, 3 percent of the test section area with bleeding was selected as the threshold for the start of bleeding. 4.1.1 Descriptive Analysis Figure 4.1 shows the percentage of bleeding area measured in three consecutive years for individual pavement sections of four mix types: DGAC, OGAC, RAC G, and RAC O. In this figure, bleeding includes all three severity levels ( low, medium, and high). The figure shows that bleeding may appear two to four years after construction on all pavement types, and it tends to appear earlier on rubberized pavements than on nonrubberized ones. Among the four mix types, RAC G pavements seem to be most susceptible to bleeding in terms of both the time of occurrence and the extent of distress. Age ( year) Bleed ingAll (%) 0 5 10 15 20 0 20 40 60 DGAC Age ( year) Bleed ingAll (% ) 0 5 10 15 20 0 20 40 60 OGAC 01 N104 01 N105 Age ( year) Blee d ingAll (%) 0 5 10 15 20 0 20 40 60 RAC G QP 19 QP 39 QP 46 Age ( year) Blee d ingAll (%) 0 5 10 15 20 0 20 40 60 RAC O QP 24 Figure 4.1: Bleeding development trend over three years for each pavement section. Figure 4.2 shows the percentage of sections with bleeding over three consecutive years for the four pavement types: DGAC, OGAC, RAC G, and RAC O. It can be seen that bleeding develops with UCPRC RR 2009 01 27 pavement age, and RAC G pavements show the most bleeding in all three years among the four pavement types. 0 5 10 15 20 25 30 35 40 45 50 55 60 1 2 3 1 2 3 1 2 3 1 2 3 DGAC OGAC RAC G RAC O Sections with Bleeding (%) Figure 4.2: Percentage of pavement sections of the four mix types with at least 3 percent of their area showing bleeding for each of the three measured years. 4.1.2 Regression Analysis Regression analysis was performed to evaluate the effects of traffic, climate, and mix type on bleeding. The percentage of pavement surface area with bleeding is selected as the response variable. Table 4.1 shows the results of the single variable regression analysis. Based on a 95 percent confidence level, Age, Cc( coefficient of curvature), annual average rainfall, cumulative wet days, and annual freeze thaw cycles are significant factors. Mix type, air void content and other mix properties, and traffic volume are all insignificant. The R2 value, however, is very small for every model, indicating a poor fitting of the singlevariable regression model. Based on the results in Table 4.1, multiple regression analysis was conducted to account for the effect of various factors simultaneously. The regression equation, Equation 4.1, is (%) 8.31833 1.34027 ( ) 3.05324 ( ) 12.74202 ( ) 2.3931 ( ) 1.1134 0.00261 ( ) 0.04448 Bleeding Age year ind MixTypeOGAC ind MixTypeRAC G ind MixTypeRAC O FinenessModulus AverageAnnualRainfall mm AverageAnnualWetDay = − + × + × + × − + × − − × + × + × 0.06624 30 0.20956 331.3915 ( 10 6) s NumberOfDays C AnnualFTCycles CumulativeAADTTinCoringLane e + × > − × + × ( 4.1) 28 UCPRC RR 2009 01 where ind(⋅) is an indicator function, 1 if the variable in the parentheses is true and 0 if false. Table 4.1: Regression Analysis of Single Variable Models for Bleeding Model Number Variable Name Coefficient P value Constant Term R2 1 Age ( year) 1.1707131 < 0.001  0.277 0.080 2 Air void Content (%) 0.0097543 0.956 4.969 < 0.001 3 Mix Type 2.1498328 0.399 2.601 0.074 4 Rubber Inclusion 2.7343317 0.148 3.710 0.012 5 Fineness Modulus 0.8046441 0.714 1.244 0.001 6 NMAS ( mm)  0.0514452 0.888 5.686 < 0.001 7 Cu  0.0155074 0.810 5.556 < 0.001 8 Cc 1.9569111 < 0.001  1.458 0.090 9 Surface Thickness ( mm)  0.0644396 0.233 7.529 0.008 10 Average Annual Rainfall ( mm)  0.0042234 0.042 7.613 0.023 11 Age * Average Annual Rainfall ( mm) 0.0005775 0.192 3.601 0.010 12 Average Annual Wet Days  0.0077203 0.672 5.618 0.001 13 Age * Average Annual Wet Days 0.0108951 0.002 1.604 0.051 14 Average Annual Max. Daily Air Temp ( º C) 0.5352416 0.150  7.258 0.012 15 Annual Number of Days > 30 º C 0.0277209 0.139 2.884 0.012 16 Annual Degree Days > 30 º C 0.0007627 0.147 2.993 0.012 17 Annual FT Cycles  0.1649969 0.023 7.203 0.029 18 Annual AADTT per Coring Lane 0.0000142 0.185 4.037 0.010 The estimated coefficients of the independent variables and corresponding P values are shown below: Value Std. Error t value P value ( Intercept)  8.31833 15.57583  0.5341 0.5940 Age 1.34027 0.31703 4.2276 < 0.0000 PvmntTypeOGAC 3.05324 3.99935 0.7634 0.4463 PvmntTypeRAC G 12.74202 3.67548 3.4668 0.0007 PvmntTypeRAC O 2.39310 3.87593 0.6174 0.5378 FinenessModulus  1.11340 3.42440  0.3251 0.7455 AvgAnnualRainfall 0.00261 0.00253 1.0319 0.3037 AvgAnnualWetDays 0.04448 0.01987 2.2388 0.0265 NoDaysTempGT30 0.06624 0.02138 3.0981 0.0023 AnnualFTCycles  0.20956 0.07501  2.7936 0.0058 Age* AADTTCoringLane 331.39150 124.13478 2.6696 0.0084 Residual standard error: 11.21 on 160 degrees of freedom; Multiple R Squared: 0.28. The results show that at the 95 percent confidence level, age, pavement type, average annual wet days, number of days with temperature greater than 30 º C, annual freeze thaw cycles, and cumulative truck traffic are significant in affecting bleeding. Bleeding area increases with age, number of wet days, number of high temperature days, and cumulative truck traffic, but decreases with the number of freeze thaw UCPRC RR 2009 01 29 cycles. Higher freeze thaw cycles indicate that the pavement is in a colder region, where bleeding is less likely to occur. Among the four pavement types, OGAC and RAC O pavements are not significantly different from DGAC pavement, but RAC G pavement is significantly ( statistically) more prone to bleeding. 4.2 Rutting In the first two year survey, the maximum rut depth at every 25 m of the test section was recorded in millimeters following the 2000 Pavement Condition Survey ( PCS), and rut depth was measured across the wheelpaths with a straight edge ruler. In the third year survey, there was an unsuccessful attempt to assess the rut depth from photographs of the surface taken from the shoulder. For this reason, it is assumed that the rut depth in the third survey year was no less than those in the previous survey years. In the analysis, a maximum of a 3 mm rut present on at least 25 m of the total section ( 125 or 150 m) was assumed as the threshold for the occurrence of rutting. 4.2.1 Descriptive Analysis Figure 4.3 shows the rut depths measured in three consecutive years ( essentially the first two years of measurement) for individual pavement sections of four mix types: DGAC, OGAC, RAC G, and RAC O. The figure shows that rutting may appear four to six years after construction on all pavement types, but it only appeared on a few pavement sections. Because OGAC, RAC G, and RAC O are typically constructed as thin overlays rutting on these pavements is significantly affected by the mix properties of the underlying layers. Therefore, comparison of the rutting resistance of the four mixes cannot be made without knowledge of the underlying layers. Figure 4.4 shows the percentage of sections with rutting in three consecutive survey years for the four pavement types: DGAC, OGAC, RAC G, and RAC O. It can be seen that rutting develops with pavement age, and that DGAC pavements show more rutting than other pavement types in all three years. 30 UCPRC RR 2009 01 Figure 4.3: Rutting development trend in three years for each pavement section. 0 5 10 15 20 25 30 35 40 45 50 1 2 3 1 2 3 1 2 3 1 2 3 DGAC OGAC RAC G RAC O Sections with Rutting (%) Figure 4.4: Percentage of pavement sections with rutting of at least 3 mm on at least 25 m of a 150 m long section in the first two years of measurement for four mix types. UCPRC RR 2009 01 31 4.2.2 Regression Analysis Because the number of sections with rutting is small and the third year data are rough estimates, no regression analysis was performed on the rutting data. 4.3 Transverse/ Reflective Cracking Because all the sections investigated in this study are overlays of AC or PCC and it is difficult to distinguish the thermal and reflective cracking mechanisms based only on surface condition observations, the analysis in this study combines thermal cracking and reflective cracking as one distress type. 4.3.1 Descriptive Analysis In the condition survey, the number and length of transverse/ reflective cracks were recorded for each of three severity levels ( low, medium, and high) for each 25 m subsection. The average length of transverse/ reflective cracking ( at all severity levels) per unit length of pavement is shown in Figure 4.5 for three survey years for four pavement types. Age ( year) Transverse and Reflective Cracking ( m/ m) 0 5 10 15 20 0.0 0.2 0.4 0.6 0.8 1.0 DGAC QP 09 Age ( year) Transverse and Reflective Cracking ( m/ m) 0 5 10 15 20 0.0 0.2 0.4 0.6 0.8 1.0 OGAC QP 22 Age ( year) Transverse and Reflective Cracking ( m/ m) 0 5 10 15 20 0.0 0.2 0.4 0.6 0.8 1.0 RAC G QP 05 QP 14 QP 46 Age ( year) Transverse and Reflective Cracking ( m/ m) 0 5 10 15 20 0.0 0.2 0.4 0.6 0.8 1.0 RAC O Figure 4.5: Transverse/ reflective cracking development trends in three years for each pavement section. 32 UCPRC RR 2009 01 It can be seen that transverse/ reflective cracking generally propagates with pavement age. The transverse/ reflective cracks seem to initiate earlier and propagate faster on the rubberized asphalt pavements ( RAC G and RAC O) than on the nonrubberized pavements ( DGAC and OGAC). As pointed out in the two year noise study report ( 2), the increased cracking in the rubber mixes may be biased by the condition of the underlying pavements because RAC G and RAC O mixes tend to be placed more on pavements with a greater extent of existing cracking. A 5 m total transverse crack length out of 125 or 150 m was assumed as the threshold of transverse/ reflective cracking. With this threshold, Figure 4.6 shows the percentage of sections with transverse and reflective cracking in three consecutive survey years for the four pavement types: DGAC, OGAC, RAC G, and RAC O. It can be seen that the percentage of sections with transverse/ reflective cracking increased significantly from the first survey year to the second survey year for pavements overlaid with open graded mixes ( OGAC and RAC O), but stayed relatively stable for pavements overlaid with DGAC and RAC G mixes. From the second survey year to the third survey year, the percentage of cracked sections does not change for any pavement type. 0 5 10 15 20 25 30 35 40 45 50 1 2 3 1 2 3 1 2 3 1 2 3 DGAC OGAC RAC G RAC O Sections with Transverse/ Reflective Cracking (%) Figure 4.6: Percentage of pavement sections with 5 m of transverse/ reflective cracking in 150 m section in three years for four mix types. UCPRC RR 2009 01 33 4.3.2 Statistical Analysis Regression analysis was performed to evaluate the effects of traffic, climate, and mix properties on transverse/ reflective cracking. The total length of the cracks ( at all severity levels) was selected as the response variable. A single variable regression analysis was first conducted to check the correlation between the dependent variable and each independent variable, and then a multiple regression model was estimated to consider the effects of various variables simultaneously. Results of the single variable regression analysis are given in Table 4.2. To account for the effects of underlying layers, the following variables were included in the analysis: the presence of a PCC underlayer ( determined from coring), thickness of the layer underneath the surface, and the presence of cracking in the layer underneath the surface. The P values less than 0.05 are shown in bold, indicating statistical significance at the 95 percent confidence interval. Table 4.2: Regression Analysis of Single Variable Models for Transverse/ Reflective Cracking Model Number Variable Name Coefficient P value Constant Term R2 1 Age ( year) 0.0118358 0.009 0.043 0.037 2 Air void Content (%)  0.0031251 0.228 0.133 0.008 3 Mix Type  0.0586000 0.128 0.101 0.038 4 Rubber Inclusion 0.0531253 0.057 0.071 0.020 5 Fineness Modulus  0.0766643 0.017 0.479 0.033 6 PCC Below ( 1  yes) 0.1147345 0.025 0.052 0.043 7 Underneath Layer Thickness ( mm)  0.0002376 0.392 0.103 0.006 8 Cracking in Underneath Layer ( 1  yes)  0.0165455 0.575 0.073 0.003 9 Surface Thickness ( mm)  0.0002858 0.721 0.107 0.001 10 Average Annual Rainfall ( mm)  0.0000898 0.003 0.151 0.048 11 Age * Average Annual Rainfall ( mm) 0.0000030 0.649 0.089 0.001 12 Average Annual Wet Days  0.0008404 0.002 0.161 0.055 13 Age* Average Annual Wet Days 0.0000450 0.399 0.082 0.004 14 Average Annual Max. Daily Air Temp ( º C) 0.0136164 0.013  0.216 0.034 15 Annual Number of Days > 30 º C 0.0007965 0.004 0.035 0.047 16 Annual Degree Days > 30 º C 0.0000221 0.004 0.038 0.045 17 Annual FT Cycles  0.0025364 0.018 0.130 0.031 18 Annual AADTT per Coring Lane 0.0000146 0.126 0.079 0.013 Results of the single variable regression analysis indicate that transverse/ reflective cracking may be significantly affected by pavement age, aggregate gradation ( in terms of Fineness Modulus), the existence of underlying PCC slabs, rainfall, high temperature days, and freeze thaw cycles. Based on the results in Table 4.2, multiple regression analysis was conducted to account for the effect of various factors simultaneously. The regression equation, Equation 4.2, is 34 UCPRC RR 2009 01 / Re ( / ) 0.271686 0.004845 (%) 0.018047 ( ) 0.188134 ( ) 0.054069 ( ) 0.136324 ( ) 0.025383 ( ) 0.018369 ( Transverse flectiveCracking m m AirVoid Age year ind MixTypeOGAC ind MixTypeRAC G ind MixTypeRAC O ind PCCBelow ind C = + × + × − × − × − − × − − × + × ) 0.003510 ( ) 0.000447 ( ) 0.000014 ( ) 0.000224 0.001113 30 0.000585 8.170241 rackBelow SurfaceThickness mm UnderlyingThickness mm AverageAnnualRainfall mm AverageAnnualWetDays NumberOfDays C AnnualFTCycles Cu − × − × + × − × − × > − × + × mulativeAADTTinCoringLane( 10e6) ( 4.2) where ind(⋅) is an indicator function, 1 if the variable in the parentheses is true and 0 if false. The estimated coefficients of the independent variables and corresponding P values are shown below: Value Std. Error t value P value ( Intercept) 0.271686 0.104323 2.6043 0.0107 AirVoid 0.004845 0.003805 1.2734 0.2059 Age 0.018047 0.004194 4.3028 0.0000 PvmntTypeOGAC  0.188134 0.054370  3.4602 0.0008 PvmntTypeRAC G  0.054069 0.037564  1.4394 0.1533 PvmntTypeRAC O  0.136324 0.047260  2.8846 0.0048 PCCBelow  0.025383 0.046622  0.5445 0.5874 CrackBelow 0.018369 0.031515 0.5829 0.5613 Thickness  0.003510 0.001063  3.3007 0.0014 UnderlyingThickness  0.000447 0.000325  1.3771 0.1717 AvgAnnualRainfall 0.000014 0.000030 0.4716 0.6383 AvgAnnualWetDays  0.000224 0.000230  0.9762 0.3314 NoDaysTempGT30  0.001113 0.000351  3.1712 0.0020 AnnualFTCycles  0.000585 0.000999  0.5855 0.5596 Age* AADTTCoringLane 8.170241 3.549995 2.3015 0.0235 Residual standard error: 0.1153 on 97 degrees of freedom; Multiple R Squared: 0.49. The results show that at the 95 percent confidence level, age, pavement type, overlay thickness, number of days with temperature greater than 30 º C, and cumulative truck traffic are significant in affecting transverse/ reflective cracking. The crack length increases with age and cumulative truck traffic, but d
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Title  Investigation of noise and durability trends for asphaltic pavement surface types threeyear results 
Subject  Tire/pavement noiseCalifornia.; Pavements, AsphaltPerformanceCalifornia. 
Description  Text document (PDF).; Title from PDF title page (viewed on May 27, 2010).; "Partnered Pavement Research Program (PPRC) Contract Strategic Plan Element 4.19: Third Year Field Evaluation of Tire/Pavement Noise, IRI, Macrotexture and Surface Condition of Flexible Pavements."; "January 2009."; "Version no: May 5, 2010"Document retrieval page.; Includes bibliographical references (p. 145).; Performed for California Dept. of Transportation, Division of Research and Innovation, Office of Roadway Research. 
Creator  Lu, Qing. 
Publisher  University of California Pavement Research Center 
Contributors  Kohler, Erwin.; Harvey, John T.; Ongel, Aybike.; California Dept. of Transportation. Division of Research and Innovation. Office of Roadway Research.; University of California. Pavement Research Center. 
Type  Text 
Identifier  http://www.ucprc.ucdavis.edu/pdf/UCPRCRR200901.pdf 
Language  eng 
Relation  http://worldcat.org/oclc/631940972/viewonline 
DateIssued  [2010] 
FormatExtent  xxii, 206 p. : digital, PDF file (4.3 MB) with col. ill., col. charts. 
RelationRequires  Mode of access: World Wide Web. 
RelationIs Part Of  Research report ; UCPRCRR200901; Research report (University of California. Pavement Research Center) ; UCPRCRR200901. 
Transcript  January 2009 Research Report: UCPRC RR 2009 01 IInnvveessttiiggaattiioonn ooff Nooiissee aanndd Duurraabbiilliittyy Peerrffoorrmaannccee Trreennddss ffoorr Asspphhaallttiicc Paavveemeenntt SSuurrffaaccee Tyyppeess:: Thhrreeee Yeeaarr Reessuullttss Authors: Qing Lu, Erwin Kohler, John T. Harvey, and Aybike Ongel Partnered Pavement Research Program ( PPRC) Contract Strategic Plan Element 4.19: Third Year Field Evaluation of Tire/ Pavement Noise, IRI, Macrotexture and Surface Condition of Flexible Pavements PREPARED FOR: California Department of Transportation Division of Research and Innovation Office of Roadway Research PREPARED BY: University of California Pavement Research Center UC Davis, UC Berkeley ii UCPRC RR 2009 01 DOCUMENT RETRIEVAL PAGE Research Report UCPRC RR 2009 01 Title: Investigation of Noise and Durability Performance Trends for Asphaltic Pavement Surface Types: Three Year Results Author: Q. Lu, E. Kohler, J. Harvey, and A. Ongel Prepared for: Caltrans FHWA No.: CA101881A Work Submitted: March 26, 2009 Date: January 2009 Strategic Plan No: 4.19 Status: Final Version No: May 5, 2010 Abstract: The work presented in this report is part of an on going research project, whose central purpose is to support the Caltrans Quieter Pavement Research Program, that has as its goals and objectives the identification of quieter, smoother, safer and more durable pavement surfaces. The research has been carried out as part of Partnered Pavement Research Center Strategic Plan Element 4.19 ( PPRC SPE 4.19). In the study documented in this report, field data regarding tire/ pavement noise, surface condition, ride quality, and macrotexture were collected over three consecutive years from pavements in California placed with open graded and other asphaltic mixes. The three year data were analyzed to evaluate the durability and effectiveness of open graded mixes in reducing noise compared to other asphalt surfaces, including dense and gap graded mixes, and to evaluate the pavement characteristics that affect tire/ pavement noise. The analysis in this report is a supplement and update to a previous study on the first two years of data collected, which is detailed in a separate report prepared as part of PPRC SPE 4.16, the previous phase of the Quieter Pavement Research Program. Conclusions are made regarding the performance of open graded mixes and rubberized mixes ( RAC G), comparisons are made with dense graded mixes ( DGAC); and the effects of variables affecting tire/ pavement noise are examined. The report presents interim results that will be finalized after supplementation with data collected in 2009 as part of the fourth year ( PPRC SPE 4.27) of the study. Keywords: asphalt concrete, decibel ( dB), noise, absorption, macrotexture, microtexture, open graded, gap graded, densegraded, onboard sound intensity, permeability, flexible pavement Proposals for implementation: No proposals for implementation are presented in this report. Related documents: • Investigation of Noise, Durability, Permeability, and Friction Performance Trends for Asphaltic Pavement Surface Types: First and Second Year Results, by A. Ongel, J. Harvey, E. Kohler, Q. Lu, and B. Steven. February 2008. ( UCPRC RR 2007 03). Report prepared by UCPRC for the Caltrans Department of Research and Innovation. • Summary Report: Investigation of Noise, Durability, Permeability, and Friction Performance Trends for Asphalt Pavement Surface Types: First and Second Year Results, by Aybike Ongel, John T. Harvey, Erwin Kohler, Qing Lu, Bruce D. Steven and Carl L. Monismith. August 2008. ( UCPRC SR 2008 01). Report prepared by UCPRC for the Caltrans Department of Research and Innovation. • Acoustical Absorption of Open Graded, Gap Graded, and Dense Graded Asphalt Pavements, by A. Ongel and E. Kohler. July 2007. ( UCPRC TM 2007 13) Report prepared by UCPRC for the Caltrans Department of Research and Innovation. • State of the Practice in 2006 for Open Graded Asphalt Mix Design, by A. Ongel, J. Harvey, and E. Kohler. December 2007. ( UCPRC TM 2008 07) Report prepared by UCPRC for the Caltrans Department of Research and Innovation. • Temperature Influence on Road Traffic Noise: Californian OBSI measurement study, by Hans Bendtsen, Qing Lu, and Erwin Kohler. Draft report for Caltrans by the Danish Road Institute, Road Directorate and University of California Pavement Research Center. 2009. • Work Plan for project 4.19, “ Third Year Field Evaluation of Tire/ Pavement Noise, IRI, Macrotexture and Surface Condition of Flexible Pavements” Signatures: Qing Lu 1st Author John T. Harvey Technical Review David Spinner Editor John T. Harvey Principal Investigator T. Joseph Holland Caltrans Contract Manager UCPRC RR 2009 01 iii DISCLAIMER The contents of this report reflect the views of the authors who are responsible for the facts and accuracy of the data presented herein. The contents do not necessarily reflect the official views or policies of the State of California or the Federal Highway Administration. This report does not constitute a standard, specification, or regulation. PROJECT OBJECTIVES The research presented in this report is part of the California Department of Transportation ( Caltrans) Quieter Pavement Research ( QPR) Work Plan, whose the central purpose is to support the Caltrans Quieter Pavement Research Program. This program’s goals and objectives are to identify quieter, safer and more durable asphalt pavement surfaces. The University of California Pavement Research Center ( UCPRC) is supporting the Caltrans Quieter Pavement Research Program by performing experiments under Partnered Pavement Research Center Strategic Plan Elements ( PPRC SPEs) 4.16, 4.19, 4.27, and 4.29. The purpose of the project discussed in this report, which is part of PPRC SPE 4.19, is to perform a third year of measurement of tire/ pavement noise, surface condition, ride quality, and macrotexture of 74 flexible pavement sections to improve performance estimates for identification of the more durable, smoother, and quieter pavement types among current asphalt mixes used by Caltrans and several new types of mixes. The three years of data collected on the sections, including the first two years of data collected as part of PPRC SPE 4.16, will be used to provide a preliminary table of estimated design lives for different treatments with respect to the variables measured. PPRC SPE 4.19 has the following objectives: • Objective 1. To perform a third year of noise, smoothness, and distress monitoring of 4.16 sections; • Objective 2. To conduct noise, smoothness, and distress monitoring on field sections with new types of mixes identified as having the potential to be the smoother, quieter, and more durable, or that perform under conditions not included in the previous testing; • Objective 3. To develop pavement temperature corrections for OBSI data and upgrades to the instrumented noise car; • Objective 4. To analyze the results and model them where applicable; and • Objective 5. To develop a preliminary table of expected lives for flexible pavement surfaces; This report documents the work completed for Objectives 1, 2, 4, and 5. The work completed as part of Objective 3 is documented in a separate report. iv UCPRC RR 2009 01 UCPRC RR 2009 01 v EXECUTIVE SUMMARY Background and Purpose The smoothness and quietness of pavements are receiving increased attention and importance as they affect quality of life issues for highway users and neighboring residents. Since the California Department of Transportation ( Caltrans) employs a variety of strategies and materials for maintaining and rehabilitating the state’s highways pavements, it has sought to identify the lives of those strategies and materials, and those of new candidates, that can maintain roadway smoothness and quietness for the longest time. To accomplish this, the Department established the Quieter Pavement Research ( QPR) Program. The Caltrans QPR program is intended to examine the impact of quieter pavements on traffic noise levels and to establish which pavement characteristics have the greatest impact on tire/ pavement noise. The program also aims to identify surface treatments, materials, and construction methods that will result in quieter pavements that are also safe, durable, and cost effective. The information gathered as part of the Caltrans QPR will be used to develop quieter pavement design features and specifications for noise abatement throughout the state. The QPR program includes several studies to evaluate the acoustic properties of pavements and the role that pavement surface characteristics play relative to tire/ pavement noise levels. The research presented in this report is part of one of these studies and is an element of the Caltrans Quieter Pavements Research ( QPR) Work Plan. The QPR Work Plan includes research on both asphalt and concrete pavement surfaces. For the flexible ( asphalt surfaced) pavement part of the QPR study, Caltrans previously identified a need for research in the areas of acoustics, friction, and performance of asphalt pavement surfaces. In response to that need, Partnered Pavement Research Center Strategic Plan Element ( PPRC SPE) 4.16 was initiated in November 2004. Among its other objectives, PPRC SPE 4.16 developed preliminary performance estimates for current Caltrans asphalt surfaces— including DGAC, OGAC, RAC G, and RAC O as part of a factorial experiment— and a number of experimental asphalt surfaces with respect to tire/ pavement noise, permeability, macrotexture, microtexture, smoothness and surface distress development. ( Note that the technical names for these mixes have changed in the new Section 39 of the Standard Specifications. The names in use at the start of PPRC SPE 4.16 have been maintained in this report for consistency with vi UCPRC RR 2009 01 previous reports). Those performance estimates were based on data collected during field tests and laboratory testing of cores in the first two years of the study. PPRC SPE 4.19, titled “ Third Year Field Evaluation of Tire/ Pavement Noise, IRI, Macrotexture, and Surface Condition of Flexible Pavements,” was initiated in September 2007. The purpose of PPRC SPE 4.19 is to perform a third year of measurement of tire/ pavement noise, surface condition, ride quality and macrotexture of up to 74 flexible pavement sections to improve performance estimates for identification of the more durable, smoother, and quieter pavement types. Several new sections were also tested for the first time as part of this project. The results presented in this report are updated performance estimates from the third year of measurements on most of the sections included in the PPRC SPE 4.16 project, combined with the first two years of data. As part of this project several new sections were also tested for the first time. In addition, the three years of data collected on the sections were used to provide a preliminary table of estimated design lives for different treatments with respect to the variables measured. Objectives The objectives of PPRC SPE 4.19 are: 1. To perform a third year of noise, smoothness and condition survey monitoring of PPRC SPE 4.16 sections. Following the PPRC SPE 4.19 work plan, noise, smoothness and macrotexture, and surface condition of each section were measured using the California On board Sound Intensity ( OBSI) method, laser profilometer, and visual condition survey ( walking survey from the shoulder), respectively on the 74 sections included in PPRC SPE 4.16. ( These comprised a factorial of current Caltrans asphalt surface mixes, referred to as “ Quieter Pavement” or “ QP” sections, and a number of experimental surfaces referred to as “ Environmental” or “ ES” sections.) Following the PPRC SPE 4.19 work plan, there were neither traffic closures in the scope of the third year of data collection nor were cores take for measurement of permeability, friction and air voids. 2. To conduct noise, smoothness, and condition survey monitoring on new field sections identified as having the the potential to be more durable, smoother, and quieter, or that perform under conditions not included in the previous testing. The same methods mentioned in Objective 1 were used to evaluate sections not previously included in PPRC SPE 4.16, including asphalt and concrete surfaces. These included testing of additional bituminous wearing course ( BWC) UCPRC RR 2009 01 vii sections beyond the one ES section on State Route 138 in Los Angeles County and evaluation of the SkidabraderTM on several concrete and asphalt surfaces. 3. To develop a pavement temperature correction for OBSI data and upgrades to the instrumented noise car. This objective involved measurement of some sections at various temperatures within a short period in order to quantify the effect of pavement temperature on noise levels and to determine correction formulas for normalizing OBSI measurements. A transition from a single sound intensity probe to double probes was done as part of this project, as were software developments and updates associated with improved data collection practices. 4. To analyze results and model them where applicable. This included analyzing the results of the measurements, investigating trends, and predicting durability, smoothness, and noise performance using the models. 5. To develop preliminary tables of expected lives for flexible pavement surfaces with respect to noise, smoothness, and durability. Scope of the Report This report documents the work completed for Objectives 1, 2, 4, and 5. The work completed as part of Objective 3 is documented in a separate report. The measured results and the qualitative and statistical analyses from this testing program are documented in this report. The information is organized as follows: • Chapter 1 presents the background of the study, its objectives, and the performance parameters for pavement surfaces. • Chapter 2 provides an analysis of ride quality data in terms of the International Roughness Index ( IRI). • Chapter 3 presents an analysis of the macrotexture data in terms of Mean Profile Depth ( MPD). • Chapter 4 presents an analysis of the condition survey data for bleeding, rutting, raveling, transverse/ reflective cracking, and wheelpath cracking. • Chapter 5 presents the On board Sound Intensity ( OBSI) data collected on the test sections. • Chapter 6 presents an analysis of the third year data collected on the Environmental ( ES) sections ( same data as in Chapters 2 through 5 for the QP sections). • Chapter 7 presents the data collected on the new sections visited for the first time this year, including the BWC sections and the Skidabrader sections. viii UCPRC RR 2009 01 • Chapter 8 presents an overall evaluation of the performance models developed in this study, and an assessment of the life spans of the different surface mixes for different conditions and failure criteria based on the models. • Chapter 9 lists the conclusions from the analyses and includes preliminary recommendations. • Appendices provide additional detailed information. The data presented in this report includes the three years of data collection, and is included in a relational database that will be delivered to Caltrans separately. Specific data in the database includes: • Microtexture and macrotexture data that affect skid resistance; • Ride quality in terms of International Roughness Index ( IRI), including third year data; • On board Sound Intensity ( OBSI), a measure of tire/ pavement noise, including third year data; • Sound intensity for different frequencies, including third year data; • Surface distresses, including bleeding, rutting, raveling, transverse cracking, and cracking in the wheelpaths, including third year data; • Climate data; and • Traffic data. The analyses presented for each performance variable in Chapters 2 through 5 include a summary of the expected trends from the literature, descriptive statistics, and where the data is sufficient, statistical models. Several appendices provide the data corrections used and detailed condition survey information. . Conclusions The following conclusions were drawn from the results of analysis of the three years of data and the testing of the new sections. No new recommendations were made. Performance of Open Graded Mixes The average tire/ pavement noise level on DGAC pavements is about 101.3 dB( A) for newly paved overlays, 102.4 dB( A) for pavements between one and three years old, and between 103 and 104 dB( A) for pavements older than three years. Compared to the average noise level of a DGAC mix, the recently paved open graded mixes are quieter by about 2.5 dB( A) for OGAC and by about 3.1 dB( A) for RAC O. After the pavements are exposed to traffic, this noise benefit generally changes slightly for about five to seven years and then begins to diminish after seven years. RAC O remains quieter longer than does OGAC. UCPRC RR 2009 01 ix For recently paved overlays, open graded mixes have higher low frequency noise and lower high frequency noise than DGAC mixes. In the first three years after the open graded mixes are exposed to traffic, high frequency noise increases with age due to the reduction of air void content under traffic, while low frequency noise decreases with age, likely due to the reduction of surface roughness caused by further compaction under traffic. These opposing changes leave the overall sound intensity nearly unchanged. For open graded pavements older than three years, noise in the frequencies between 500 and 2,500 Hz increases with age, while noise in the frequencies over 2,500 Hz changes slightly or diminishes with age. Among the two open graded mixes, MPD has lower initial values and increases more slowly on RAC O pavements than on OGAC pavements. The effect of MPD on noise is complex. It appears that a higher MPD value increases noise on OGAC pavements, but it does not significantly affect the noise on RAC O pavements. Based on the condition survey for pavements less than ten years old, for recently paved overlays, transverse/ reflective cracking is less significant on open graded mixes than on dense or gap graded mixes. However, once cracking appears on open graded mixes it increases more rapidly with pavement age than it does on dense or gap graded mixes. It also appears that open graded pavements experience less raveling than dense graded mixes. There is no other significant difference between open and densegraded mixes in terms of pavement distresses. The data reveal no major difference in pavement distresses between OGAC and RAC O mixes. Performance of RAC G Mixes The newly paved RAC G mixes are quieter in terms of tire/ pavement noise by about 1.6 dB( A), compared to an average DGAC mix. Within a few years after the pavements are exposed to traffic, the tire/ pavement noise on RAC G mixes approaches the average noise level on DGAC pavements of similar ages. For newly paved overlays, RAC G mixes have higher low frequency noise and lower high frequency noise than DGAC mixes. In the first three years after the pavements are exposed to traffic, high frequency noise increases with age due to the reduction of air void content under traffic, while low frequency noise is nearly unchanged with age. For RAC G pavements older than three years, noise of all frequencies increases with age. x UCPRC RR 2009 01 The IRI value on newly paved RAC G surfaces is lower than that for DGAC mixes, and it does not increase with age. The IRI on DGAC pavements, however, increases with age. RAC G mixes have a permeability level as high as that of open graded mixes in the first three years after construction, but under traffic the permeability decreases rapidly to the level of DGAC mixes in about four years. These facts explain the reasons for the initial low noise level and the rapid loss of the noise benefit of RAC G mixes. Based on the condition survey for pavements less than ten years old, RAC G pavement is more prone than other mixes to bleeding in terms of both the time of occurrence and the extent of distress. Transverse/ reflective cracks seem to initiate earlier and propagate faster on the rubberized pavements than on the nonrubberized pavements, but this is possibly because rubberized mixes tend to be placed more frequently on pavements with greater extent of cracking, which biases the comparison. There were no other significant differences between RAC G and DGAC mixes in terms of pavement distresses. Variables Affecting Tire/ Pavement Noise The findings from this third year of the study regarding variables affecting tire/ pavement noise are generally consistent with the findings from the analysis on the two year data. That is, tire/ pavement noise is greatly influenced by surface mix type and mix properties, age, traffic volume, and the presence of distresses. Various mix types have different noise performances, and the overall noise level generally increases with traffic volume, pavement age, and the presence of pavement distresses. The overall noise level decreases with increasing surface layer thickness and permeability ( or air void content). For DGAC, RAC G, and RAC O pavements, the aggregate gradation variable ( fineness modulus) does not seem to significantly affect tire/ pavement noise. For OGAC pavements, however, a coarser gradation seems to significantly reduce tire/ pavement noise. It must be noted that the conclusion regarding aggregate gradation is drawn from a data set that only contains NMAS ranging from 9.5 mm to 19 mm, with most open graded mixes either 9.5 or 12.5 mm, and most RAC G and DGAC mixes either 12.5 or 19 mm. At frequencies below 1,000 Hz, the aggregate gradation variable ( fineness modulus) does not significantly affect the noise level for all pavements. At frequencies above 1,000 Hz, higher macrotexture ( MPD) values seem to significantly reduce the noise level on RAC O mixes. On the other hand, higher macrotexture values increase the noise level of gapgraded mixes. UCPRC RR 2009 01 xi Performance of Experimental Mixes The bituminous wearing course ( BWC) mix placed on the LA 138 sections has a noise level comparable to that of DGAC mixes, and similar distress development as current Caltrans open graded mixes. The noise levels of BWC mixes placed on the sections tested for the first time this year are similar to or lower than those of open graded mixes of similar age. This indicates that the tire/ pavement noise levels of the LA 138 BWC mix are not typical of other BWC mixes placed in the state. Based on the Fresno 33 ( Firebaugh) sections it was observed that: • RUMAC GG performed similarly to RAC G in terms of tire/ pavement noise and ride quality when placed in a thin ( 45 mm) or a thick ( 90 mm) lifts. However, RUMAC GG was more crack resistant than RAC G when placed in a thick lift ( 90 mm). • Although the Type G MB mix has higher noise levels than the RAC G mix soon after construction, the increase in noise with age is less significant on the Type G MB mix than on the RAC G mix and the Type D MB mix. • The Type G MB mix is more susceptible to bleeding than other mixes. • The Type D MB mix is more resistant to cracking than the DGAC mix but it is also more susceptible to bleeding. • The Type D MB mix has a noise level similar to the DGAC mix soon after construction, but its noise level increases with age more than the noise level of the DGAC mix. After opening to traffic for four years, none of the test mixes ( RAC G, RUMAC GG, Type G MB, and Type D MB) had noise levels as high as those of the DGAC mix. The European gap graded ( EU GG) mix placed on LA 19 has performance characteristics very similar to those of gap graded mixes ( RAC G) used in California, except it may retain its permeability longer. Old concrete surfaces with burlap drag and longitudinally tined surface textures that were then retextured with Skidabrader technology showed slight decreases in noise of  0.5 and  0.1 dB( A), respectively. The results showed increases in noise on OGAC and DGAC surfaces that were similarly retextured of 1.3 and 0.8 dB( A), respectively. xii UCPRC RR 2009 01 CONVERSION FACTORS SI* ( MODERN METRIC) CONVERSION FACTORS APPROXIMATE CONVERSIONS TO SI UNITS Symbol Convert From Multiply By Convert To Symbol LENGTH in. inches 25.4 millimeters mm ft feet 0.305 meters m AREA in. 2 square inches 645.2 square millimeters mm2 ft2 square feet 0.093 square meters m2 VOLUME ft3 cubic feet 0.028 cubic meters m3 MASS lb pounds 0.454 kilograms kg TEMPERATURE ( exact degrees) ° F Fahrenheit 5 ( F 32)/ 9 Celsius C or ( F 32)/ 1.8 FORCE and PRESSURE or STRESS lbf poundforce 4.45 newtons N lbf/ in. 2 poundforce/ square inch 6.89 kilopascals kPa APPROXIMATE CONVERSIONS FROM SI UNITS Symbol Convert From Multiply By Convert To Symbol LENGTH mm millimeters 0.039 inches in. m meters 3.28 feet ft AREA mm2 square millimeters 0.0016 square inches in. 2 m2 square meters 10.764 square feet ft2 VOLUME m3 cubic meters 35.314 cubic feet ft3 MASS kg kilograms 2.202 pounds lb TEMPERATURE ( exact degrees) C Celsius 1.8C+ 32 Fahrenheit F FORCE and PRESSURE or STRESS N newtons 0.225 poundforce lbf kPa kilopascals 0.145 poundforce/ square inch lbf/ in. 2 * SI is the symbol for the International System of Units. Appropriate rounding should be made to comply with Section 4 of ASTM E380 ( revised March 2003). UCPRC RR 2009 01 xiii TABLE OF CONTENTS PROJECT OBJECTIVES..................................................................................................................... ... iii EXECUTIVE SUMMARY ........................................................................................................................ v LIST OF FIGURES ............................................................................................................................... xvii LIST OF TABLES ............................................................................................................................... ... xxi 1. INTRODUCTION................................................................................................................... ............ 1 1.1 Project Background..................................................................................................................... .... 1 1.2 Project Purpose and Objectives ....................................................................................................... 2 1.3 Experiment Factorial for Third Year Measurements....................................................................... 3 1.4 Scope of this Report......................................................................................................................... 5 2. SURFACE PROFILE RESULTS AND ANALYSIS: IRI ................................................................ 7 2.1 Descriptive Analysis ........................................................................................................................ 7 2.2. Regression Analysis....................................................................................................................... 11 2.3 Summary of Findings..................................................................................................................... 16 3. SURFACE PROFILE RESULTS AND ANALYSIS: MEAN PROFILE DEPTH...................... 17 3.1 Descriptive Analysis ...................................................................................................................... 17 3.2 Regression Analysis....................................................................................................................... 20 3.3 Summary of Findings..................................................................................................................... 24 4. SURFACE DISTRESS RESULTS AND ANALYSIS..................................................................... 25 4.1 Bleeding ............................................................................................................................... ......... 26 4.1.1 Descriptive Analysis ...................................................................................................... 26 4.1.2 Regression Analysis....................................................................................................... 27 4.2 Rutting ............................................................................................................................... ........... 29 4.2.1 Descriptive Analysis ...................................................................................................... 29 4.2.2 Regression Analysis....................................................................................................... 31 4.3 Transverse/ Reflective Cracking..................................................................................................... 31 4.3.1 Descriptive Analysis ...................................................................................................... 31 4.3.2 Statistical Analysis......................................................................................................... 33 4.4 Raveling ............................................................................................................................... ......... 35 4.4.1 Descriptive Analysis ...................................................................................................... 35 4.4.2 Statistical Analysis......................................................................................................... 36 4.5 Wheelpath ( Fatigue) Cracking....................................................................................................... 38 4.5.1 Descriptive Analysis ...................................................................................................... 38 4.5.2 Statistical Analysis......................................................................................................... 39 4.6 Summary of Findings..................................................................................................................... 42 xiv UCPRC RR 2009 01 5. SOUND INTENSITY RESULTS AND ANALYSIS ....................................................................... 45 5.1 Conversion of Sound Intensity for Temperature, Speed, Air Density, Tire .................................. 46 5.2 Evaluation of Overall Sound Intensity........................................................................................... 47 5.2.1 Descriptive Analysis ...................................................................................................... 47 5.2.2 Regression Analysis....................................................................................................... 52 5.3 Evaluation of Sound Intensity Levels at One Third Octave Bands ............................................... 57 5.3.1 Change of OBSI Spectra with Age ................................................................................ 57 5.3.2 Descriptive Analysis of Sound Intensity Data for All One Third Octave Bands .......... 60 5.3.3 Evaluation of Sound Intensity at 500 Hz One Third Octave Band................................ 67 5.3.4 Evaluation of Sound Intensity at 1,000 Hz One Third Octave Band............................. 74 5.3.5 Evaluation of Sound Intensity at 2,000 Hz One Third Octave Band............................. 81 5.3.6 Evaluation of Sound Intensity at 4,000 Hz One Third Octave Band............................. 88 5.3.7 Sound Intensity at Other One Third Octave Bands ....................................................... 94 5.4 Summary of Findings..................................................................................................................... 95 6. ENVIRONMENTAL SECTIONS RESULTS AND ANALYSIS................................................... 99 6.1 Fresno 33 Sections ......................................................................................................................... 99 6.2 Sacramento 5 and San Mateo 280 Sections ................................................................................. 102 6.3 LA 138 Sections....................................................................................................................... ... 105 6.4 LA 19 Sections....................................................................................................................... ..... 108 6.5 Yolo 80 Section ........................................................................................................................... 109 6.6 Summary........................................................................................................................ ............. 112 7. RESULTS AND ANALYSIS FOR NEW SURFACES MEASURED FOR THE FIRST TIME IN SURVEY YEAR 3 ............................................................................................................................. 113 7.1 SemMaterial BWC Sections ........................................................................................................ 113 7.1.1 Sound Intensity Measurements .................................................................................... 114 7.1.2 International Roughness Index and Mean Profile Depth ............................................. 116 7.2 Skidabrader Retexturing Sections, Before and After................................................................... 117 7.2.1 Before Skidabrader Treatment ..................................................................................... 117 7.2.2 After Skidabrader Treatment ....................................................................................... 122 7.3 Other Testing ............................................................................................................................... 127 7.3.1 Mesa Rodeo Test Sections ........................................................................................... 127 7.3.2 Arizona I 10 ................................................................................................................. 127 7.3.3 California Highway Patrol Sections ( Profilometer Only)............................................ 128 7.4 Summary of the New Surface Testing ......................................................................................... 128 7.4.1 Testing on BWC Sections............................................................................................ 128 7.4.2 Testing on Skidabrader Sections.................................................................................. 128 7.4.3 Testing on Other Sections............................................................................................ 129 UCPRC RR 2009 01 xv 8 ESTIMATED PERFORMANCE OF DIFFERENT ASPHALT MIX TYPES BASED ON PERFORMANCE MODELS................................................................................................................. 131 8.1 Prediction of IRI .......................................................................................................................... 131 8.2 Prediction of Tire/ Pavement Noise.............................................................................................. 133 8.3 Prediction of Pavement Distresses............................................................................................... 136 8.4 Summary........................................................................................................................ ............. 139 9 CONCLUSIONS.................................................................................................................... .......... 141 9.1 Performance of Open Graded Mixes ........................................................................................... 141 9.2 Performance of RAC G Mixes .................................................................................................... 142 9.3 Variables Affecting Tire/ Pavement Noise ................................................................................... 143 9.4 Performance of Experimental Mixes ........................................................................................... 144 REFERENCES..................................................................................................................... .................. 145 APPENDICES..................................................................................................................... ................... 146 A. 1: List of Test Sections Included in the Study................................................................................ 146 A. 1.1: List of Quiet Pavement ( QP) Sections .............................................................................. 146 A. 1.2 List of Caltrans Environmental Noise Monitoring Site ( ES) Sections............................... 150 A. 2: Correlation Between Aquatred 3 Tire OBSI and SRTT OBSI................................................... 151 A. 2.1 Plots of Aquatred 3 Tire OBSI versus SRTT OBSI........................................................... 151 A. 2.2 Simple Linear Regression Results............................................................................................ 153 A. 3: Box Plots of Air Void Content, Permeability, and BPN............................................................ 154 A. 3.1 Box Plots of Air Void Content .......................................................................................... 154 A. 3.2 Box Plots of BPN............................................................................................................... 154 A. 3.3 Box Plots of Permeability .................................................................................................. 155 A. 4: Boxplots and Cumulative Distribution of Noise Reduction for Sound Intensity at Other Frequency Bands.......................................................................................................................... 155 A. 5: Sound Intensity Spectra Measured in Three Years for Each Pavement Section ........................ 163 A. 6: Close up Photos of Pavements Included in This Study.............................................................. 175 A. 7: Condition Survey of Environmental Noise Monitoring Site Sections for Three Years ............. 186 A. 8 Technical Memorandum for Sacramento I 5 sections................................................................ 188 A. 9 Photos of Skidabrader Sections .................................................................................................. 200 A. 10: Actual Values Predicted by Regression Models for Chapter 8 ................................................ 204 xvi UCPRC RR 2009 01 UCPRC RR 2009 01 xvii LIST OF FIGURES Figure 2.1: IRI trends over three years for each pavement section.............................................................. 9 Figure 2.2: Variation in IRI values for different mix types for all three years of pooled data and all initial ages. ............................................................................................................................... .... 10 Figure 2.3: Variation in IRI values for different mix types for different initial ages ( Age category in years) for all three years pooled data. ............................................................................................ 10 Figure 2.4: Comparison of IRI values for different mix types at different ages for first, second, and third years of data collection ( Phase ID showing Years 1, 2, and 3)........................................... 11 Figure 3.1: MPD trend over three years for each pavement section. ......................................................... 18 Figure 3.2: Variation in MPD values for different mix types for pooled data for all three years and all initial ages........................................................................................................................... ... 19 Figure 3.3: Comparison of MPD values for different mix types for different initial age categories ( Age Category) and for first, second, and third years of data collection ( Phase ID). ........................ 19 Figure 4.1: Bleeding development trend over three years for each pavement section................................ 26 Figure 4.2: Percentage of pavement sections of the four mix types with at least 3 percent of their area showing bleeding for each of the three measured years. ............................................................ 27 Figure 4.3: Rutting development trend in three years for each pavement section. ..................................... 30 Figure 4.4: Percentage of pavement sections with rutting of at least 3 mm on at least 25 m of a 150 m long section in the first two years of measurement for four mix types. .................................. 30 Figure 4.5: Transverse/ reflective cracking development trends in three years for each pavement section........................................................................................................................ ....... 31 Figure 4.6: Percentage of pavement sections with 5 m of transverse/ reflective cracking in 150 m section in three years for four mix types. ........................................................................................... 32 Figure 4.7: Raveling development trends over three years for each pavement section. ............................. 35 Figure 4.8: Percentage of pavement sections with at least 5 percent of area with raveling for each of three years of measurement for four mix types............................................................................. 36 Figure 4.9: Development trends for fatigue cracking over three years for each pavement section. ........... 38 Figure 4.10: Percentage of pavement sections with at least 5 percent of wheelpaths with fatigue cracking for each of the three years measured. .................................................................................. 39 Figure 5.1: Development trends of overall OBSI over three years for each pavement section. ................. 49 Figure 5.2: Comparison of overall OBSI values for different mix types for different initial age categories ( Age Category) and for first, second, and third years of data collection ( Phase ID).. 50 Figure 5.3: Cumulative distribution function of noise reduction of OGAC, RAC O, and RAC G mixes for different groups of pavement age....................................................................................... 52 Figure 5.4: Average OBSI spectra for Age Group “< 1 Year” in three survey phases ( years).................... 58 Figure 5.5: Average OBSI spectra for Age Group “ 1– 4 Years” in three survey phases ( years). ............... 59 xviii UCPRC RR 2009 01 Figure 5.6: Average OBSI spectra for Age Group “> 4 Years” in three survey phases ( years). ................. 59 Figure 5.7: Sound intensity at 500 Hz over three years for each pavement section. .................................. 62 Figure 5.8: Sound intensity at 630 Hz over three years for each pavement section. .................................. 62 Figure 5.9: Sound intensity at 800 Hz over three years for each pavement section. .................................. 63 Figure 5.10: Sound intensity at 1,000 Hz over three years for each pavement section. ............................. 63 Figure 5.11: Sound intensity at 1,250 Hz over three years for each pavement section. ............................. 64 Figure 5.12: Sound intensity at 1,600 Hz over three years for each pavement section. ............................. 64 Figure 5.13: Sound intensity at 2,000 Hz over three years for each pavement section. ............................. 65 Figure 5.14: Sound intensity at 2,500 Hz over three years for each pavement section. ............................. 65 Figure 5.15: Sound intensity at 3,150 Hz over three years for each pavement section. ............................. 66 Figure 5.16: Sound intensity at 4,000 Hz over three years for each pavement section. ............................. 66 Figure 5.17: Sound intensity at 5,000 Hz over three years for each pavement section. ............................. 67 Figure 5.18: Sound intensity at 500 Hz for different initial age categories ( Age Category) and for first, second, and third years of data collection ( Phase ID). ....................................................................... 68 Figure 5.19: Cumulative distribution function of 500 Hz noise reduction of OGAC, RAC O, and RAC G mixes for different groups of pavement age. ........................................................................ 69 Figure 5.20: Sound intensity at 1,000 Hz for different initial age categories ( Age Category) and for first, second, and third years of data collection ( Phase ID). ......................................................... 75 Figure 5.21: Cumulative distribution function of 1,000 Hz noise reduction of OGAC, RAC O, and RAC G mixes for different groups of pavement age................................................................... 76 Figure 5.22: Sound intensity at 2,000 Hz for different initial age categories ( Age Category) and for first, second, and third years of data collection ( Phase ID)................................................................ 82 Figure 5.23: Cumulative distribution function of 2,000 Hz noise reduction of OGAC, RAC O, and RAC G mixes for different groups of pavement age................................................................... 83 Figure 5.24: Sound intensity at 4,000 Hz for different initial age categories ( Age Category) and for first, second, and third years of data collection ( Phase ID). ......................................................... 89 Figure 5.25: Cumulative distribution function of 4,000 Hz noise reduction of OGAC, RAC O, and RAC G mixes for different groups of pavement age................................................................... 90 Figure 6.1: Three year MPD values for Fresno 33 sections. .................................................................... 100 Figure 6.2: Three year IRI values for Fresno 33 sections......................................................................... 100 Figure 6.3: Three year Overall OBSI values for Fresno 33 sections. ....................................................... 101 Figure 6.4: Three year IRI values for Sacramento 5 and San Mateo 280 sections................................... 103 Figure 6.5 Three year MPD values for Sacramento 5 and San Mateo 280 sections................................. 104 Figure 6.6: Three year overall OBSI values for Sacramento 5 and San Mateo 280 sections. .................. 104 Figure 6.7: Three year IRI values for the LA 138 sections. ..................................................................... 106 Figure 6.8: Three year overall OBSI values for LA 138 sections. .......................................................... 107 UCPRC RR 2009 01 xix Figure 6.9: Three year IRI values for LA 19 section................................................................................ 109 Figure 6.10: Three year MPD values for LA 19 section. ......................................................................... 109 Figure 6.11: Three year IRI values for the Yolo 80 section. .................................................................... 110 Figure 6.12: Three year MPD values for the Yolo 80 section.................................................................. 111 Figure 6.13: Three year OBSI values for the Yolo 80 section. ................................................................ 111 Figure 7.1: Overall sound intensity levels. ............................................................................................... 114 Figure 7.2: Spectral sound intensity levels. .............................................................................................. 115 Figure 7.3: Sound intensity levels of BWC compared to other pavement types. ..................................... 115 Figure 7.4: Left and right wheelpath IRI levels for each section.............................................................. 116 Figure 7.5: Mean Profile Depth. ............................................................................................................... 117 Figure 7.6: Schematic location of pavement sections ( post miles shown on left side)............................. 118 Figure 7.7: Overall OBSI levels in each section for each pavement type................................................. 119 Figure 7.8: Comparison of OBSI one third band spectra across pavement types..................................... 119 Figure 7.9: OBSI for one third band spectra for burlap drag PCC pavement ( BD) segments.................. 120 Figure 7.10: OBSI for one third band spectra for open graded asphalt pavement ( OG) segments. ......... 120 Figure 7.11: OBSI for one third band spectra for dense graded asphalt pavement ( DG) segments......... 121 Figure 7.12: OBSI for one third band spectra for longitudinally tined PCC pavement ( LT) segments. .121 Figure 7.13: Overall OBSI levels after Skidabrader. ................................................................................ 122 Figure 7.14: OBSI spectra for before and after Skidabrader for burlap drag PCC pavement ( BD) segments....................................................................................................................... ................... 124 Figure 7.15: OBSI spectra for before and after Skidabrader for open graded AC pavement ( OG) segments....................................................................................................................... ................... 125 Figure 7.16: OBSI spectra for before and after Skidabrader for dense graded AC pavement ( DG) segments....................................................................................................................... ................... 126 Figure 7.17: OBSI spectra for before and after Skidabrader for longitudinally tined PCC pavement ( LT) segments.................................................................................................................. 127 Figure A. 1.: UCPRC overall OBSI levels on monitoring section of I 5, southbound ( SB) and northbound ( NB). ...................................................................................................................... 189 Figure A. 2: Overall OBSI spectra levels by I& R and UCPRC on southbound I 5. ................................. 189 Figure A. 3: Overall OBSI spectra levels by I& R and UCPRC on northbound I 5................................... 190 Figure A. 4: Comparison of UCPRC OBSI spectra levels on the SB and NB sections in August 2008 ( SRTT)........................................................................................................................ 190 Figure A. 5: UCPRC OBSI spectra levels on the monitoring section on I 5 southbound ( SRTT) for four site visits......................................................................................................................... .... 190 Figure A. 6: UCPRC OBSI spectra levels on the monitoring section on I 5 northbound ( SRTT). .......... 191 Figure A. 7: Air void content in SB and NB directions from cores taken in February 2006. ................... 192 xx UCPRC RR 2009 01 Figure A. 8: Sound absorption measured on cores from SB section.......................................................... 192 Figure A. 9: Sound absorption measured on cores from NB section......................................................... 193 Figure A. 10: Changes in macrotexture over time in terms of MPD. ........................................................ 193 Figure A. 11: Changes in ride quality over time in terms of IRI. .............................................................. 194 Figure A. 12: Pavement profile at 1 inch intervals, NB direction. ............................................................ 194 Figure A. 13: Detail of first 100 ft of pavement elevation profile on NB direction indicating wide cracks......................................................................................................................... ............. 194 Figure A. 14: Wide reflective cracks in the monitoring section in the NB direction................................ 195 Figure A. 15: Overall 2.5 sec OBSI levels for whole length of southbound lanes ( Note: 1S is the first [ inner] southbound lane, 2S is the second southbound lane, etc)..................................................... 196 Figure A. 16: Overall 2.5 sec OBSI levels for whole length of northbound lanes ( Note: 1N is the first [ inner] northbound lane, 2N is the second northbound lane, etc). ................................................... 196 Figure A. 17: OBSI levels for each lane taking whole project length results. ........................................... 196 Figure A. 18: Images of the pavement in every lane as seen from testing car, August 2008. ................... 197 Figure A. 19: Depiction of southbound lanes tested over the whole length and the approximate location of monitoring sections ( red lines) in the northbound and southbound outer lanes............. 198 Figure B. 1. View of segments A, B, C, and D on BD pavement.............................................................. 200 Figure B. 2. View of segments A, B, C, and D on OG pavement.............................................................. 201 Figure B. 3. View of segments A, B, C, and D on DG pavement.............................................................. 202 Figure B. 4. View of segments A, B, C, and D on LT pavement. ............................................................. 203 UCPRC RR 2009 01 xxi LIST OF TABLES Table 1.1: Number of Sections with Valid Measurements in Three Years................................................... 5 Table 2.1: Regression Analysis of Single Variable Models for IRI ........................................................... 12 Table 3.1: Regression Analysis of Single Variable Models for MPD........................................................ 20 Table 4.1: Regression Analysis of Single Variable Models for Bleeding .................................................. 28 Table 4.2: Regression Analysis of Single Variable Models for Transverse/ Reflective Cracking.............. 33 Table 4.3: Regression Analysis of Single Variable Models for Raveling .................................................. 37 Table 4.4: Regression Analysis of Single Variable Models for Fatigue Cracking..................................... 40 Table 4.5: Single Variable Cox Regression Model for Wheelpath Crack Initiation .................................. 42 Table 5.1: Regression Analysis of Single Variable Models for Overall Sound Intensity .......................... 53 Table 5.2: Regression Analysis of Single Variable Models for 500 Hz Band Sound Intensity................. 70 Table 5.3: Regression Analysis of Single Variable Models for 1,000 Hz Band Sound Intensity .............. 77 Table 5.4: Regression Analysis of Single Variable Models for 2,000 Hz Band Sound Intensity .............. 84 Table 5.5: Regression Analysis of Single Variable Models for 4,000 Hz Band Sound Intensity .............. 91 Table 7.1: BWC Section Locations........................................................................................................... 113 Table 7.2: Physical Properties of BWC Sections from SemMaterial and UCPRC OBSI Measurements114 Table 7.3: Comparison of OBSI Levels Before and After Skidabrader.................................................... 123 Table 8.1: Selection of Typical Environmental Regions .......................................................................... 132 Table 8.2: Predicted Lifetime of Different Asphalt Mix Types with Respect to Roughness.................... 133 Table 8.3: Predicted Lifetime of Different Asphalt Mix Types with Respect to Noise from First Model ............................................................................................................................... ....... 135 Table 8.4: Predicted Lifetime of Different Asphalt Mix Types with Respect to Noise from Second Model.......................................................................................................................... ........ 135 Table 8.5: Predicted Age to Occurrence of Bleeding of Different Asphalt Mix Types............................ 137 Table 8.6: Predicted Age to Occurrence of Raveling of Different Asphalt Mix Types............................ 138 Table 8.7: Predicted Age to Occurrence of Transverse/ Reflective Cracking of Different Asphalt Mix Types.......................................................................................................................... ..................... 139 Table A. 1: Temperature, pressure, and relative humidity at times of UCPRC testing ............................ 191 Table A. 2: Aggregate Gradation ( percent passing each sieve by mass) for SB and NB Sections............ 192 Table A. 10.1: Predicted Lifetime of Different Asphalt Mix Types with Respect to Roughness............ 204 Table A. 10.2: Predicted Lifetime of Different Asphalt Mix Types with Respect to Noise from First Model ............................................................................................................................... ....... 204 Table A. 10.3: Predicted Lifetime of Different Asphalt Mix Types with Respect to Noise from Second Model.......................................................................................................................... ....... 205 Table A. 10.4: Predicted Age to Occurrence of Bleeding of Different Asphalt Mix Types...................... 205 Table A. 10.5: Predicted Age to Occurrence of Raveling of Different Asphalt Mix Types..................... 206 xxii UCPRC RR 2009 01 UCPRC RR 2009 01 1 1. INTRODUCTION 1.1 Project Background The smoothness and quietness of pavements are receiving increased attention and importance as they affect quality of life issues for highway users and neighboring residents. Since the California Department of Transportation ( Caltrans) employs a variety of strategies and materials for maintaining and rehabilitating the state’s highways pavements, it has sought to identify the lives of those strategies and materials, and those of new candidates, that can maintain roadway smoothness and quietness for the longest time. To accomplish this, the Department established the Quieter Pavement Research ( QPR) Program. The Caltrans QPR program is intended to examine the impact of quieter pavements on traffic noise levels and to establish which pavement characteristics have the greatest impact on tire/ pavement noise. The program also aims to identify surface treatments, materials, and construction methods that will result in quieter pavements that are also safe, durable, and cost effective. The information gathered as part of the Caltrans QPR will be used to develop quieter pavement design features and specifications for noise abatement throughout the state. The QPR program includes several studies to evaluate the acoustic properties of pavements and the role that pavement surface characteristics play relative to tire/ pavement noise levels. The research presented in this report is part of one of these studies and is an element of the Caltrans Quieter Pavements Research ( QPR) Work Plan. The QPR Work Plan includes research on both asphalt and concrete pavement surfaces. For the flexible ( asphalt surfaced) pavement part of the QPR study, Caltrans previously identified a need for research into the acoustics, friction, and performance of asphalt pavement surfaces, and in November 2004 initiated Partnered Pavement Research Center Strategic Plan Element ( PPRC SPE) 4.16 as a response. Among its other objectives, PPRC SPE 4.16 developed preliminary performance estimates for current Caltrans asphalt surfaces— including DGAC, OGAC, RAC G, and RAC O as part of a factorial experiment— and a number of experimental asphalt surfaces with respect to tire/ pavement noise, permeability, macrotexture, microtexture, smoothness, and surface distress development. ( Note that the technical names for these mixes have changed in the new Section 39 of the Standard Specifications. The names in use at the start of PPRC SPE 4.16 have been maintained in this report for consistency with previous reports). Those performance estimates were based on data collected during field tests and laboratory testing of 2 UCPRC RR 2009 01 cores in the first two years of the study. The results of the first two years of data collection, modeling, and performance predictions are summarized in Reference ( 1). PPRC SPE 4.19, titled “ Third Year Field Evaluation of Tire/ Pavement Noise, IRI, Macrotexture, and Surface Condition of Flexible Pavements,” was initiated in September 2007. The results presented in this report are updated performance estimates from the third year of measurements on most of the pavement sections included in the PPRC SPE 4.16 project, combined with the first two years of data. Several new sections were also tested for the first time as part of this project. 1.2 Project Purpose and Objectives The purpose of PPRC SPE 4.19 is to perform a third year of measurement of tire/ pavement noise, surface condition, ride quality, and macrotexture of up to 74 flexible pavement sections in order to improve performance estimates for identifying the more durable, smoother, and quieter pavement types. The three years of data collected on the sections, including two years of data collected as part of PPRC SPE 4.16, were used to provide a preliminary table of estimated design lives for different treatments with respect to the variables measured. The objectives of PPRC SPE 4.19 are: Objective 1: To perform third year of noise, smoothness, and distress monitoring of PPRC SPE 4.16 sections. In July 2007 the UCPRC completed field work on the second year surface property monitoring of the PPRC SPE 4.16 sections. There were 74 sections monitored as part of PPRC SPE 4.16, comprised of a factorial of current Caltrans asphalt surface mixes, referred to as “ Quieter Pavement” or “ QP” sections, and a number of experimental surfaces referred to as “ Environmental” or “ ES” sections. The UCPRC conducted a third year data collection campaign on these sections. Following the PPRC SPE 4.19 work plan, no cores were taken nor were there required traffic closures. Noise, smoothness and macrotexture, and surface condition of each section were measured using the California On board Sound Intensity ( OBSI) method, laser profilometer, and visual condition survey ( walking survey from the shoulder), respectively. Objective 2: To conduct noise, smoothness and distress monitoring on new field sections identified to have the potential to be more durable, smoother, and quieter, or that perform under conditions not included in the previous testing. UCPRC RR 2009 01 3 The same methods noted in Objective 1 were used to evaluate sections not previously included in PPRC SPE 4.16, including asphalt and concrete surfaces. An estimated maximum of 10 sections selected by Caltrans were to be included as part of this objective. In the case of new sections, measurements were to be conducted as much as scheduling allowed before and after construction. Objective 3: To develop pavement a temperature correction for OBSI data and upgrades to the instrumented noise car. This objective involved measuring some sections at various temperatures within a short time period in order to quantify the effect of pavement temperature on the noise levels and to determine correction formulas for normalizing OBSI measurements. The transition from a single sound intensity probes to double probes was to be done as part of this project, as well as any software development and updates associated with improved data collection practices. Objective 4: To analyze the results and to model them where applicable. Analyze results of the measurements, investigate trends, classify pavements with respect to durability, smoothness, and noise levels, and develop predictive models where possible to investigate trends and predict future performance. The database generated during PPRC SPE 4.16 was used in this part of the study, pooled with the third year measurements. Objective 5: To develop a preliminary table of expected lives for flexible pavement surfaces. Analyze the results of Objective 4, and develop a preliminary table of estimated design lives for flexible pavement surfaces tested with respect to durability, smoothness, and noise levels. Traffic and climate condition effects on life were to be included in the table where data is available. This report documents the work completed for Objectives 1, 2, 4, and 5. The work completed as part of Objective 3 is documented in a separate report. 1.3 Experiment Factorial for Third Year Measurements A factorial was developed for current Caltrans asphalt surfaces as part of PPRC SPE 4.16, including DGAC, RAC G, OGAC, and RAC O. ( As noted earlier, although the names of materials have changed in the new Standard Specifications Section 39, the earlier names are used in this report to maintain consistency with earlier reports.) That factorial includes 51 sections, referred to as the Quieter Pavement ( QP) sections, which were selected based on climate region ( rainfall), traffic ( Average Daily Truck Traffic [ ADTT]), and years since construction at the time of the initial measurement ( referred to as Age 4 UCPRC RR 2009 01 Category and grouped at the time of the first year of measurements into: less than one year, one to four years, or four to eight years). These sections have been tested for three years. The first two years of data included coring, condition survey, permeability, and friction ( microtexture) tests performed within traffic closures; profile and tire/ pavement noise measurements performed at highway speeds with the instrumented noise car, and mix property testing on cores performed in the laboratory. In addition, several sections identified in other projects and 23 sections with new materials and control sections, referred to as the Environmental Sections ( ES) were also tested. Appendix A. 1: List of Test Sections Included in the Study shows specific test section information. Detailed project background for PPRC SPE 4.16— literature survey, experimental design, and data collection methodologies— can be found in the two year noise study report, “ Investigation of Noise, Durability, Permeability, and Friction Performance Trends for Asphaltic Pavement Surface Types: Firstand Second Year Results.” ( 2) Most of the same data collection methodologies were continued in the third year but on a smaller scale, and coring, permeability, and friction tests were not conducted. Also, in the third year a Standard Reference Test Tire ( SRTT) was used for all noise measurements rather than the AquaTred tire used for the first two years of measurement. All measurements from the first two years with the AquaTred tire were converted to equivalent noise levels using the SRTT tire using a correlation developed by the UCPRC as part of this project. The details of the correlation are shown in Appendix A. 2: Correlation Between Aquatred 3 Tire OBSI and SRTT OBSI. Air density adjustments were applied to all data from all three years. Some pavement sections had failed by the third year and were dropped out from the survey. Table 1.1 shows the number of sections surveyed for various performance measures in the three years. A similar collection of data for the fourth year is scheduled for spring 2009. UCPRC RR 2009 01 5 Table 1.1: Number of Sections with Valid Measurements in Three Years Year 1 ( Phase 1) Year 2 ( Phase 2) Year 3 ( Phase 3) Tire/ Pavement Noise ( OBSI California)* 76 71 65 Roughness ( ASTM E 1926) 78 71 69 Macrotexture ( ASTM E 1845) 77 72 60 Friction ( ASTM E 303) 83 73 0 Air void Content/ Aggregate Gradation** 83 73 0 Permeability ( NCAT falling head) 78 73 0 Pavement Distresses** 84 84 73 * ASTM and AASHTO methods currently being standardized based on California experience. ** See Reference ( 2) for method description. 1.4 Scope of this Report Chapters 2, 3, 4, and 5 present results and analysis for the current Caltrans asphalt surfaces: DGAC, OGAC, RAC G, and RAC O. Chapters 2 present results for the International Roughness Index ( IRI). Chapter 3 presents results for Mean Profile Depth ( MPD), which is a measure of surface macrotexture related to high speed skid resistance and also an indicator of raveling and bleeding. Chapter 4 presents the results and analysis of measurements of surface distresses, including bleeding, rutting, transverse cracking, raveling, and wheelpath cracking. Chapter 5 presents results and analysis of On Board Sound Intensity ( OBSI) measurements of tire/ pavement noise. Findings are summarized at the end of each chapter. Chapter 6 presents an update of performance measures on the experimental test sections referred to as “ Environmental Sections.” Chapter 7 presents results and analysis from OBSI and other performance measurements on asphalt and concrete surfaces included in the study for the first time in Year 3. Chapter 8 presents an update of the PPRC SPE 4.16 estimates of pavement life based on new regression equations for each of the performance measures presented in Chapters 2, 3, 4, and 5. A summary of conclusions and recommendations appears in Chapter 9. 6 UCPRC RR 2009 01 UCPRC RR 2009 01 7 2. SURFACE PROFILE RESULTS AND ANALYSIS: IRI International Roughness Index ( IRI) was measured in the third year to evaluate the change in surface roughness of asphalt pavements. The IRI measurements were collected every meter in both the left and right wheelpaths. The average of the two wheelpath measurements along the whole length of each pavement section was used in the analysis. The analysis of the IRI answers two questions: • What pavement characteristics affect IRI? o Are initial IRI and IRI changes with time different for rubberized and nonrubberized mixes? o Are initial IRI and IRI changes with time different for open graded and dense graded mixes? • How do traffic and climate affect IRI? Hypotheses regarding the effects of the explanatory variables on IRI are discussed in Reference ( 2), and will be revisited in more detail at the conclusion of the fourth year of measurement, analysis, and modeling. 2.1 Descriptive Analysis Figure 2.1 shows the average IRI measured in three consecutive years for individual pavement sections of four mix types: DGAC, OGAC, RAC G, and RAC O. The first data point for each section is shown at the age of the section when the first measurement was taken, with Year One defined as the year of construction. It should be noted that the IRI values at the time the overlays were constructed or soon thereafter is unknown except for those sections that were tested very soon after construction. It should also be noted that the current condition of the pavement layers beneath the overlays is not known. Section IDs are listed in the figure legends. Some sections showed a decrease of IRI in the second or third survey year. Small reductions in IRI with age can be attributed to measurement errors. However, a couple of sections show a significant decrease in IRI, specifically QP 09 ( DGAC) and QP 20 ( OGAC). Section QP 09 has a large patch in the middle and section QP 20 is located on a steep hill. It is uncertain why the IRI decreased on these sections, either due to difficulty in measurement such as retracing the same 8 UCPRC RR 2009 01 wheelpath, or road maintenance. These two sections are treated as outliers and will be removed from the subsequent analysis. It can be seen from Figure 2.1 that IRI increased with age for many pavement sections. This is expected because pavement conditions deteriorate with age due to traffic and environmental effects. However, some sections, particularly OGAC sections, showed little change in IRI in the three year survey period. Figure 2.2 is a box plot that shows the variation in IRI values for different mix types, including two Fmixes, across all three years of measurement. In all of the box plots shown in this report the white bar is the median value, the “ x” is the mean value, the upper and lower edges of the purple box are the 75th and 25th percentiles respectively, and the upper and lower brackets are the upper and lower extreme values respectively. According to the plot, except for the OGAC F mixes, the average IRI values of the different mixes are close to each other, and most of the sections have acceptable IRI values based on the FHWA criteria of 170 in./ mi ( 2.4 m/ km) ( 2). However, one DGAC pavement shows high IRI values (> 3.6 m/ km) that would trigger Caltrans maintenance action. From Figure 2.1 it can be seen that this is an old pavement that was 14 years old at the beginning of the survey. Figure 2.3 shows the IRI values for different mix types for the three initial age categories of less than one year, one to four years, and greater than four years. This plot is similar to the plot based on the first two years’ data ( 2). That is, IRI values increase with age for RAC O and DGAC mixes but show no trend for OGAC and RAC G mixes. Figure 2.4 shows the time trend of IRI across the three years of data collection, with each year of measurement identified as “ Phase ID,” for different mix types for three age categories. As the figure shows, IRI generally increases with time. For newly paved mixes ( Age Category “< 1 year”), IRI varied insignificantly for DGAC, OGAC, and RAC O in the first three years. On the other hand, RAC G showed a significant increase in IRI in the first three years after construction. From Figure 2.1 it can be seen that this is due to the rapid increase in IRI on one pavement section. This section is QP 26, which is located on Highway 280 in Santa Clara County in Caltrans District 4. The reason for the rapid increase in IRI at this section is unknown. This section also showed a rapid increase in macrotexture ( Mean Profile Depth [ MPD] increased from 800 microns in the first year to 2,150 microns in the third year after construction) and the distresses raveling and segregation in the third year. Cores from this section taken within a year of UCPRC RR 2009 01 9 construction showed measured air void contents of approximately 9 percent, which indicates that insufficient compaction might have caused the rapid IRI increase. If QP 26 is excluded, IRI also varied insignificantly for RAC G in the first three years. ( Note: IRI values have been reported in m/ km since data collection began. For reference, some critical IRI values are shown below in inches per mile ( 3): Criteria in./ mim/ km FHWA “ very good” maximum value 60 0.95 FHWA “ good” maximum value 94 1.48 FHWA “ fair” for Interstates maximum value 119 1.88 FHWA “ fair” for non Interstates and “ mediocre” for Interstate maximum values 170 2.68 FHWA “ mediocre” for non Interstate maximum value 220 3.47 Caltrans rigid pavement PMS prioritization trigger 213 3.36 Caltrans flexible pavement PMS prioritization trigger 224 3.54 Age ( year) IRI ( m/ km) 0 5 10 15 20 0 1 2 3 4 5 DGAC 06 N434 ES 20 QP 06 QP 09 QP 15 QP 21 QP 30 QP 40 Age ( year) IRI ( m/ km) 0 5 10 15 20 0 1 2 3 4 5 OGAC ES 11 QP 03 QP 04 QP 13 QP 20 QP 23 QP 28 QP 29 QP 44 QP 45 Age ( year) IRI ( m/ km) 0 5 10 15 20 0 1 2 3 4 5 RAC G ES 12 QP 02 QP 05 QP 14 QP 46 Age ( year) IRI ( m/ km) 0 5 10 15 20 0 1 2 3 4 5 RAC O ES 21 ES 22 QP 01 QP 12 QP 24 QP 34 QP 36 QP 51 Figure 2.1: IRI trends over three years for each pavement section. 10 UCPRC RR 2009 01 1 2 3 4 5 6 IRI ( m/ km) x x x x x x DGAC OGAC OGAC F mix RAC G RAC O RAC O F mix Mix type Figure 2.2: Variation in IRI values for different mix types for all three years of pooled data and all initial ages. 1 2 3 4 IRI ( m/ km) x x x x x x x x x x x x 2 1 2 2 2 3 4 1 4 2 4 3 6 1 6 2 6 3 7 1 7 2 7 3 Age Category, Mix type Figure 2.3: Variation in IRI values for different mix types for different initial ages ( Age category in years) for all three years pooled data. Age Category < 1 1 4 > 4 < 1 1 4 > 4 < 1 1 4 > 4 < 1 1 4 > 4 Mix Type DGAC OGAC RAC G RAC O UCPRC RR 2009 01 11 1 2 3 4 IRI ( m/ km) x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x 2 1 1 2 1 2 2 1 3 2 2 1 2 2 2 2 2 3 2 3 1 2 3 2 2 3 3 4 1 1 4 1 2 4 1 3 4 2 1 4 2 2 4 2 3 4 3 1 4 3 2 4 3 3 6 1 1 6 1 2 6 1 3 6 2 1 6 2 2 6 2 3 6 3 1 6 3 2 6 3 3 7 1 1 7 1 2 7 1 3 7 2 1 7 2 2 7 2 3 7 3 1 7 3 2 7 3 3 Phase ID, Age Category, Mix type DGAC OGAC RAC G RAC O Phase ID 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 Age Category < 1 1 4 > 4 < 1 1 4 > 4 < 1 1 4 > 4 < 1 1 4 > 4 Figure 2.4: Comparison of IRI values for different mix types at different ages for first, second, and third years of data collection ( Phase ID showing Years 1, 2, and 3). 2.2. Regression Analysis Regression analysis was performed to evaluate the effects of traffic, climate, distresses, and pavement materials on IRI values. First, a single variable regression analysis was conducted to prescreen significant factors to be included in a multiple regression model. Estimates of the coefficient of the explanatory variable and the constant term along with their P values and the coefficient of determination ( R2) for each model are given in Table 2.1. The P values less than 0.05, indicating highly significant variables, are shown in bold. The results in Table 2.1 show that IRI tends to be significantly affected by presence of distresses and environmental factors. The signs of the estimated coefficients indicate that the greater the distresses ( fatigue cracking, raveling, rutting, and bleeding) and rainfall, the higher the IRI. These are expected. High temperature days, on the other hand, seem to reduce IRI. This may be due to higher temperatures making it easier to obtain smoothness at the time of construction. Table 2.1 also shows that the inclusion of rubber tends to reduce IRI. 12 UCPRC RR 2009 01 Table 2.1: Regression Analysis of Single Variable Models for IRI Model Number Variable Name Coefficient P value Constant Term R2 1 Age ( year) 0.113 < 0.001 1.172 0.144 2 Air void Content (%)  0.00823 0.757 1.555 0.001 3 Mix Type  0.387 0.076 1.783 0.074 4 Rubber Inclusion  0.244 0.018 1.643 0.033 5 MPD ( micron) 0.000285 0.003 1.057 0.054 6 Presence of Fatigue Cracking 0.441 0.026 1.473 0.031 7 Presence of Raveling 0.299 0.013 1.454 0.038 8 Presence of Rutting 0.911 < 0.001 1.442 0.100 9 Presence of Transverse Cracking 0.188 0.546 1.497 0.002 10 Presence of Bleeding 0.439 0.015 1.472 0.036 11 Average Annual Rainfall ( mm) 0.000131 0.051 1.397 0.023 12 Age* Average Annual Rainfall ( mm) 0.000198 < 0.001 1.151 0.259 13 Average Annual Wet Days 0.000862 0.040 1.371 0.025 14 Age* Average Annual Wet Days 0.00123 < 0.001 1.219 0.180 15 Average Annual Max. Daily Air Temp ( º C)  0.0841 < 0.001 3.735 0.155 16 Annual Number of Days > 30 º C  0.00409 < 0.001 1.879 0.141 17 Annual Degree Days > 30 º C  0.000116 < 0.001 1.870 0.142 18 Annual FT Cycles  0.00600 0.034 1.622 0.027 19 Annual AADTT per Coring Lane  2.23e 5 0.297 1.563 0.007 20 Annual ESALs per Coring Lane  6.91e 8 0.123 1.572 0.014 Based on the results in Table 2.1, multiple regression analysis was conducted to account for the effect of various factors simultaneously. First a pair wise correlation analysis was performed to avoid highlycorrelated variables in the same model. It was found that air void content and MPD are highly correlated. MPD is also partly determined by the maximum aggregate size in the mix. Average Annual Maximum Daily Air Temperature is highly correlated with Annual Number of Days > 30 º C and Annual Degree Days > 30 º C. AADTT per Coring Lane is highly correlated with Annual ESALs per Coring Lane. In the multiple regression analysis, only one variable in each highly correlated variable pair will be considered. Preliminary analysis revealed that the error terms from multiple regression have nonconstant variance, so a reciprocal square root transformation ( Y' = 1/ IRI ) was applied to the dependent variable, IRI, to stabilize the variance of the error terms. Because mix properties are highly affected by mix types ( e. g., higher air void contents in OGAC mixes than in DGAC mixes), it is not appropriate to incorporate both mix property variables ( e. g., air void UCPRC RR 2009 01 13 content) and mix type in the same model. To determine the effects of mix type and mix properties on IRI, separate regression models were proposed. In the first model, only the mix type ( categorical variable) and environmental and traffic factors are included as the independent variables, while mix property variables are excluded. The regression equation, Equation 2.1, is 1 ( / ) 0.889612 0.021589 ( ) 0.056035 ( ) 0.037902 ( ) 0.102960 ( ) 0.000074 ( ) 0.000603 30 0.000012 IRI m km Age year ind MixTypeOGAC ind MixTypeRAC G ind MixTypeRAC O AverageAnnualRainfall mm NumberOfDays C AADTTinCori = − × + × + × − + × − − × + × > − × ngLane+ 0.001576×AnnualFTCycles ( 2.1) where ind(⋅) is an indicator function, 1 if the variable in the parentheses is true and 0 if false. The coefficient of the ind(⋅) function represents the difference in the effects of other mix types and DGAC. The estimated values and P values of the parameters are shown below, with variables that are significant at the 95 percent confidence interval shown in bold type. Value Std. Error t value P value ( Intercept) 0.889612 0.043695 20.3594 < 0.0001 Age  0.021589 0.003540  6.0980 < 0.0001 MixTypeOGAC 0.056035 0.028193 1.9875 0.0486 MixTypeRAC G 0.037902 0.030027 1.2623 0.2087 MixTypeRAC O 0.102960 0.026666 3.8611 0.0002 AvgAnnualRainfall  0.000074 0.000028  2.6733 0.0083 NoDaysTempGT30 0.000603 0.000218 2.7692 0.0063 AADTTCoringLane  0.000012 0.000007  1.7690 0.0788 AnnualFTCycles 0.001576 0.000819 1.9235 0.0562 Residual standard error: 0.1236 on 157 degrees of freedom; Multiple R Squared: 0.38. It can be seen that at the 95 percent confidence level, age, mix type, average annual rainfall, and number of days > 30 º C significantly affect IRI. IRI increases with Age and Average Annual Rainfall, but decreases with the Number of Days > 30 º C. Among the three pavement types, OGAC, RAC G, and RACO, all have lower initial IRI than DGAC, but only OGAC and RAC O are statistically significantly different from DGAC. Initially the interaction terms between Age and Mix Type were included in the model, but none of them were statistically significant, which indicates that the growth rate of IRI is not statistically different among the four pavement types. In the second model, Mix Type variable is replaced with Mix Property variables and the model is estimated for each Mix Type separately. The regression equations, Equation 2.2 through Equation 2.5, are 14 UCPRC RR 2009 01 For DGAC pavements: 1 ( / ) 0.888563 0.01644 ( ) 0.000262 0.014248 log( )( / sec) 0.000064 ( ) 0.000718 30 0.0000033 0.003385 IRI m km Age year MPD Permeability cm AverageAnnualRainfall mm NumberOfDays C AADTTinCoringLane AnnualFTCycles = − × − × − × − × + × > + × + × ( 2.2) Value Std. Error t value P value ( Intercept) 0.888563 0.108166 8.2148 < 0.0001 Age  0.016440 0.006102  2.6940 0.0116 MPD  0.000262 0.000128  2.0384 0.0507 logPerm  0.014248 0.011623  1.2259 0.2301 AvgAnnualRainfall  0.000064 0.000038  1.6820 0.1033 NoDaysTempGT30 0.000718 0.000396 1.8153 0.0798 AADTTCoringLane 0.0000033 0.000010 0.3254 0.7472 AnnualFTCycles 0.003385 0.001813 1.8674 0.0720 Residual standard error: 0.0959 on 29 degrees of freedom; Multiple R Squared: 0.71. For OGAC pavements: 1 ( / ) 0.834436 0.022964 ( ) 0.000304 ( ) 0.006099 log( )( / sec) 0.000231 ( ) 0.001301 30 0.0000029 0.003270 IRI m km Age year MPD micron Permeability cm AverageAnnualRainfall mm NumberOfDays C AADTTinCoringLane AnnualF = + × − × − × + × + × > + × + × TCycles ( 2.3) Value Std. Error t value P value ( Intercept) 0.834436 0.155224 5.3757 < 0.0001 Age 0.022964 0.013217 1.7375 0.0925 MPD  0.000304 0.000101  3.0149 0.0052 logPerm  0.006099 0.008093  0.7536 0.4570 AvgAnnualRainfall 0.000231 0.000137 1.6831 0.1027 NoDaysTempGT30 0.001301 0.000558 2.3303 0.0267 AADTTCoringLane 0.0000029 0.000019 0.1512 0.8808 AnnualFTCycles 0.003270 0.002053 1.5930 0.1216 Residual standard error: 0.1058 on 30 degrees of freedom; Multiple R Squared: 0.49. UCPRC RR 2009 01 15 For RAC G pavements: 1 ( / ) 1.165986 0.018908 ( ) 0.000178 ( ) 0.009595 log( )( / sec) 0.000083 ( ) 0.00037 30 0.0000697 0.001622 IRI m km Age year MPD micron Permeability cm AverageAnnualRainfall mm NumberOfDays C AADTTinCoringLane AnnualFT = − × − × − × − × − × > − × − × Cycles ( 2.4) Value Std. Error t value P value ( Intercept) 1.165986 0.090730 12.8511 < 0.0001 Age  0.018908 0.010672  1.7717 0.0897 MPD  0.000178 0.000097  1.8360 0.0793 logPerm  0.009595 0.008499  1.1289 0.2706 AvgAnnualRainfall  0.000083 0.000056  1.4912 0.1495 NoDaysTempGT30  0.000037 0.000476  0.0769 0.9393 AADTTCoringLane  0.0000697 0.000021  3.3738 0.0026 AnnualFTCycles  0.001622 0.001841  0.8815 0.3872 Residual standard error: 0.08480 on 23 degrees of freedom; Multiple R Squared: 0.67. For RAC O pavements: 1 ( / ) 0.698788 0.036292 ( ) 0.000139 ( ) 0.012359 log( )( / sec) 0.000051 ( ) 0.001275 30 0.0000024 0.000269 IRI m km Age year MPD micron Permeability cm AverageAnnualRainfall mm NumberOfDays C AADTTinCoringLane AnnualF = − × + × − × + × + × > − × + × TCycles ( 2.5) Value Std. Error t value P value ( Intercept) 0.698788 0.151179 4.6223 < 0.0001 Age  0.036292 0.009227  3.9331 0.0003 MPD 0.000139 0.000103 1.3496 0.1846 logPerm  0.012359 0.010380  1.1907 0.2406 AvgAnnualRainfall 0.000051 0.000061 0.8365 0.4077 NoDaysTempGT30 0.001275 0.000506 2.5199 0.0157 AADTTCoringLane  0.0000024 0.000012  0.1947 0.8466 AnnualFTCycles 0.000269 0.001433 0.1878 0.8520 Residual standard error: 0.1317 on 41 degrees of freedom; Multiple R Squared: 0.38. The results show that for DGAC pavements, only age is significant at the 95 percent confidence level, while none of the mix, traffic, and environmental variables is significant. For RAC O pavements, in addition to Age, Number of Days > 30 º C is also significant. For OGAC pavements, IRI increases with MPD, but does not change significantly with Age. IRI on open graded pavements ( OGAC and RAC O) decreases with the Number of Days > 30 º C, indicating that open graded pavements are smoother in high temperature regions than in low temperature regions. Traffic volume is a significant variable for RAC G pavements. Higher traffic volume leads to higher IRI values. 16 UCPRC RR 2009 01 2.3 Summary of Findings The following findings were obtained regarding roughness: 1. Except for an old DGAC pavement, all sections are smoother than the Caltrans Pavement Management System IRI trigger criterion of 3.6 m/ km ( 224 in./ mi). 2. Rubberized open graded mixes have lower initial IRI values than nonrubberized open graded mixes; rubberized gap graded mixes have lower initial IRI values than nonrubberized dense graded mixes. 3. The surface types OGAC, RAC G, and RAC O all have lower initial IRI than DGAC, but only OGAC and RAC O are statistically significantly different from DGAC. Monitoring over three years indicates that IRI increases with age on DGAC, RAC G, and RAC O pavements, but that age does not have a statistically significant effect on increasing IRI on OGAC pavements. 4. Open graded pavements ( OGAC and RAC O) are smoother in high temperature regions than in low temperature regions. 5. The IRI of OGAC pavements increases with increasing MPD. The monitoring performed to date shows that traffic volume significantly affects IRI only on RAC G pavements, with higher traffic volumes showing higher IRI values. UCPRC RR 2009 01 17 3. SURFACE PROFILE RESULTS AND ANALYSIS: MEAN PROFILE DEPTH Macrotexture was measured in the third year, but microtexture was not because during the third year survey time traffic was not closed. Macrotexture was measured by UCPRC using the same profilometer used in the previous two years, and it was reported in terms of mean profile depth ( MPD) and root mean square ( RMS) of profile deviations ( RMS). Because MPD and RMS are highly correlated, only analysis of the MPD is presented in this report. The analysis of the MPD answers these questions: • What pavement characteristics affect MPD? o Are initial MPD and change of MPD with time different for rubberized and nonrubberized mixes? o Are the initial MPD and MPD progression different for open graded and dense graded mixes? • How do traffic and climate affect MPD? The hypotheses regarding the effects of the explanatory variables on MPD are discussed in Reference ( 1) and will be revisited in more detail at the conclusion of the fourth year of measurement, analysis, and modeling. 3.1 Descriptive Analysis Figure 3.1 shows the average MPD measured in three consecutive years for individual pavement sections of four mix types: DGAC, OGAC, RAC G, and RAC O. It was expected that MPD would increase with pavement age, as pavements deteriorate with time, particularly in the form of increased raveling. The plots in Figure 3.1 confirmed this expectation. Some of the sections, whose numbers are listed in the legend, showed lower MPDs in the later years but the differences were small and can be attributed to measurement errors or other random variations. A few sections, however, show significantly different MPD values. These sections include the three newly paved OGAC pavements: QP 20, QP 44, and QP 45, and a RAC G pavement ( QP 26). The three newly paved OGAC sections all showed significantly high initial MPD values. As noted earlier, Section QP 20 is located on a steep hill and may have experienced compaction problems during construction that led to the high MPD. QP 44 is on I 80, in District 3 in 18 UCPRC RR 2009 01 Placer County, where both annual rainfall and traffic volume are very high. A pavement condition survey conducted one year after construction revealed a very rough texture with only angular coarse aggregates exposed on the surface. Although QP 45, which is on I 80 in District 3 in Yolo County, also has high traffic volume the reason for the high initial MPD values remains unclear. Lastly, QP 26 showed a rapid increase in macrotexture ( MPD increased from 800 microns in the first year after construction to 2,150 microns in the third year) and the distresses raveling and segregation in the third year. As discussed earlier, the mix design and/ or compaction for this section might not have been sufficient. Consequently, these four sections are treated as outliers and will be removed from the statistical analysis. Age ( year) MPD ( m/ km) 0 5 10 15 20 0 500 1000 2000 DGAC QP 07 QP 16 Age ( year) MPD ( m/ km) 0 5 10 15 20 0 500 1000 2000 OGAC QP 13 QP 22 QP 29 QP 44 QP 45 Age ( year) MPD ( m/ km) 0 5 10 15 20 0 500 1000 2000 RAC G QP 05 QP 14 Age ( year) MPD ( m/ km) 0 5 10 15 20 0 500 1000 2000 RAC O ES 23 QP 01 QP 17 QP 34 QP 51 Figure 3.1: MPD trend over three years for each pavement section. Figure 3.2 shows the variation in MPD values for different mix types, including two F mixes, based on the three year survey data. The information conveyed in the plots is the same as that in the plot based on the first two years’ survey data ( 2). That is, the two F mixes have the highest MPD. The RAC G mixes have higher MPD values than the dense graded mixes, while the open graded mixes have higher MPD values than the RAC G mixes. Among the two open graded mixes, RAC O mixes have lower MPD values than OGAC mixes. UCPRC RR 2009 01 19 Figure 3.3 shows the time trend of MPD in three years for different mix types for three age categories. As the figure shows, MPD generally increases with pavement age for the same pavement section. Except for the four outlier pavement sections, this increase trend is also obvious among different pavement sections of the same mix type. Phase ID in the figure is the year of data collection, either 1, 2 or 3. 5 00 1 000 1 500 2 000 MP D ( m ic ron) x x x x x x DGAC OGAC OGAC F mix RAC G RAC O RAC O F mix Mix type Figure 3.2: Variation in MPD values for different mix types for pooled data for all three years and all initial ages. 500 1000 1500 2000 M PD ( micron) x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x 2 1 1 2 1 2 2 1 3 2 2 1 2 2 2 2 2 3 2 3 1 2 3 2 2 3 3 4 1 1 4 1 2 4 1 3 4 2 1 4 2 2 4 2 3 4 3 1 4 3 2 4 3 3 6 1 1 6 1 2 6 1 3 6 2 1 6 2 2 6 2 3 6 3 1 6 3 2 6 3 3 7 1 1 7 1 2 7 1 3 7 2 1 7 2 2 7 2 3 7 3 1 7 3 2 7 3 3 Phase ID Age Category Mix type DGAC OGAC RAC G RAC O Phase ID 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 Age Category < 1 1 4 > 4 < 1 1 4 > 4 < 1 1 4 > 4 < 1 1 4 > 4 Figure 3.3: Comparison of MPD values for different mix types for different initial age categories ( Age Category) and for first, second, and third years of data collection ( Phase ID). 20 UCPRC RR 2009 01 3.2 Regression Analysis Regression analysis was performed to evaluate the effects of traffic, climate, distresses, and pavement materials on MPD values. First, a single variable regression analysis was conducted to prescreen significant factors to be included in a multiple regression model. Estimates of the coefficient of the explanatory variable and the constant term along with their P values and the coefficient of determination ( R2) for each model are given in Table 3.1. The P values less than 0.05 are shown in bold. Descriptions of the variables are provided in Reference ( 2). A few of the less common variables are described below. Cc is the Coefficient of curvature. Cc = D30/ D10 * D60, where D10 is the sieve size through which 10 percent of the aggregate passes ( mm), D30 is the sieve size through which 30 percent of the aggregate passes ( mm), and D60 is the sieve size through which 60 percent of the material passes ( mm). Cu is the Coefficient of uniformity: Cu = D60/ D10. Fineness modulus is a measure of the uniformity of the aggregate gradation. The higher the fineness modulus, the coarser the asphalt mix ( a higher percentage of coarse material) and the more uniform the gradation. Fineness Modulus is calculated as F. M. = ( Σ percent material retained on each sieve) / 100. Table 3.1: Regression Analysis of Single Variable Models for MPD Model Number Variable Name Coefficient P value Constant Term R2 1 Age ( year) 38.073 < 0.001 897.950 0.108 2 Air void Content (%) 40.398 < 0.001 576.863 0.473 3 Mix Type 572.389 < 0.001 741.798 0.453 4 Rubber Inclusion  17.816 0.732 1064.270 0.001 5 Fineness Modulus 446.849 < 0.001  1173.064 0.379 6 NMAS ( mm)  47.519 < 0.001 1670.500 0.156 7 Cu  12.334 < 0.001 1310.232 0.361 8 Cc 7.839 0.564 1031.587 0.002 9 BPN  1.482 0.587 1146.537 0.002 10 Surface Thickness ( mm)  7.935 < 0.001 1360.557 0.173 11 IRI ( m/ km) 124.881 0.019 875.503 0.037 12 Presence of Rutting 156.453 0.061 1035.488 0.025 13 Presence of Bleeding 142.468 0.061 1033.051 0.025 14 Average Annual Rainfall ( mm) 0.069 0.208 1012.951 0.011 15 Average Annual Wet Days 0.882 0.087 989.715 0.020 16 Average Annual Max. Daily Air Temp ( º C)  21.335 0.042 1546.721 0.028 17 Annual Number of Days > 30 º C  1.046 0.048 1138.271 0.027 18 Annual Degree Days > 30 º C  0.029 0.054 1133.514 0.025 19 Annual FT Cycles 0.712 0.696 1044.804 0.001 20 Annual AADTT per Coring Lane 0.00144 0.681 1046.206 0.001 UCPRC RR 2009 01 21 The results in Table 3.1 show that MPD tends to be significantly affected by mix property variables, including air void content, fineness modulus, nominal maximum aggregate size ( NMAS), and aggregate coefficient of uniformity ( Cu). According to the estimated coefficients, increasing air void content and fineness modulus increases macrotexture, and increasing NMAS and Cu reduces macrotexture. An increase of macrotexture with an increase of NMAS is unexpected. This is likely due to pooling of denseand open graded mixes and the effect of other uncontrolled factors in the single variable model. Also, macrotexture seems to be smaller on thicker surface layers, probably due to better compaction of thicker layers. Higher temperature ( in terms of both maximum daily air temperature and the number of days with air temperature greater than 30 º C) tends to reduce macrotexture, which likely is due to easier aggregate reorientation and further mix compaction at high temperatures. Heavier daily traffic volume tends to increase macrotexture, which is most likely due to removal of fines around the larger stones in the surface. Based on the results in Table 3.1, multiple regression analysis was conducted to account for the effect of various factors simultaneously. Highly correlated independent variables are mutually excluded from the modeling. Two separate regression models were proposed to determine the effects of mix type and mix properties on MPD. In the first model, only the mix type ( categorical variable) and environmental and traffic factors are included as the independent variables, while mix property variables are excluded. The regression equation, Equation 3.1, is ( ) 838.2085 29.4579 ( ) 58.6352 ( ) 221.8027 ( ) 337.4369 ( ) 6.1771 ( ) 0.6911 ( ) 1.0294 30 0.0042 MPD micron Age year ind MixTypeOGAC ind MixTypeRAC G ind MixTypeRAC O NMAS mm Thickness mm NumberOfDays C AADTTinCoringL = + × + × + × − + × − − × − × − × > + × 68.0467 ( ) 19.0678 ( ) 8.6665 ( ) ane Age ind MixTypeOGAC Age ind MixTypeRAC G Age ind MixTypeRAC O + × × − × × − + × × − ( 3.1) where ind(⋅) is an indicator function, 1 if the variable in the parentheses is true and 0 if false. The estimated values and P values of the parameters are shown below. 22 UCPRC RR 2009 01 Value Std. Error t value P value ( Intercept) 838.2085 152.0913 5.5112 0.0000 Age 29.4279 14.1577 2.0786 0.0396 MixTypeOGAC 58.6352 126.1990 0.4646 0.6430 MixTypeRAC G 221.8027 91.8216 2.4156 0.0171 MixTypeRAC O 337.4369 87.7395 3.8459 0.0002 NMAS  6.1771 7.7526  0.7968 0.4270 Thickness  0.6911 1.2638  0.5469 0.5854 NoDaysTempGT30  1.0294 0.3550  2.8995 0.0044 AADTTCoringLane 0.0042 0.0109 0.3880 0.6987 AgeMixTypeOGAC 68.0467 23.0274 2.9550 0.0037 AgeMixTypeRAC G  19.0678 19.1255  0.9970 0.3206 AgeMixTypeRAC O 8.6665 18.4019 0.4710 0.6385 Residual standard error: 193.1 on 130 degrees of freedom; Multiple R Squared: 0.6325. It can be seen that at the 95 percent confidence level, age, mix type, and number of days > 30 º C significantly affect macrotexture. MPD increases with age, but decreases with the number of days > 30 º C. Among the three pavement types, OGAC, RAC G, and RAC O, all have higher initial MPD than DGAC, but OGAC is statistically insignificantly different from DGAC. This is likely due to the removal of the three newly paved OGAC pavement sections from the analysis. P values for the interaction terms between Age and Mix Type showed that the growth rate ( with age) of MPD of OGAC pavements is significantly higher than that of DGAC pavements. The growth rates of MPD of RAC G and RAC O pavements are not statistically different from those of DGAC pavements. In the second model, Mix Type variable is replaced with Mix Property variables and the model is estimated for each mix type separately. The regression equations, Equation 3.2 through Equation 3.5, are: For DGAC pavements: ( ) 93.7089 4.2910 (%) 47.8933 ( ) 283.2136 9.9487 ( ) 5.4209 ( ) 0.7087 30 0.0402 MPD micron AirVoid Age year FinenessModulus NMAS mm Thickness mm NumberOfDays C AADTTinCoringLane = − − × + × + × − × − × − × > − × ( 3.2) Value Std. Error t value P value ( Intercept)  93.7089 529.8210  0.1769 0.8612 AirVoid  4.2910 15.7801  0.2719 0.7882 Age 47.8933 13.0899 3.6588 0.0014 FinenessModulus 283.2136 156.2116 1.8130 0.0835 NMAS  9.9487 10.1549  0.9797 0.3379 Thickness  5.4209 1.8722  2.8955 0.0084 NoDaysTempGT30  0.7087 0.6382  1.1105 0.2788 AADTTCoringLane  0.0402 0.0177  2.2674 0.0335 Residual standard error: 133.1 on 22 degrees of freedom; Multiple R Squared: 0.601. UCPRC RR 2009 01 23 For OGAC pavements: ( ) 645.6240 0.4917 (%) 103.6224 ( ) 274.1456 1.9169 ( ) 0.457 ( ) 0.5966 30 0.0089 MPD micron AirVoid Age year FinenessModulus NMAS mm Thickness mm NumberOfDays C AADTTinCoringLane = − − × + × + × − × − × − × > − × ( 3.3) Value Std. Error t value P value ( Intercept)  645.6240 338.4451  1.9076 0.0675 AirVoid  0.4917 10.0302  0.0490 0.9613 Age 103.6224 10.5024 9.8666 0.0000 FinenessModulus 274.1456 93.6918 2.9260 0.0070 NMAS  1.9169 15.5844  0.1230 0.9031 Thickness  0.4570 1.5415  0.2965 0.7692 NoDaysTempGT30  0.5966 0.3698  1.6131 0.1188 AADTTCoringLane  0.0089 0.0171  0.5201 0.6074 Residual standard error: 88.19 on 26 degrees of freedom; Multiple R Squared: 0.9143. For RAC G pavements: ( ) 622.7423 9.1326 (%) 14.3359 ( ) 403.7994 28.119 ( ) 2.6337 ( ) 0.7899 30 0.0348 MPD micron AirVoid Age year FinenessModulus NMAS mm Thickness mm NumberOfDays C AADTTinCoringLane = − − × + × + × − × − × − × > − × ( 3.4) Value Std. Error t value P value ( Intercept)  622.7423 1241.1985  0.5017 0.6206 AirVoid  9.1326 17.1338  0.5330 0.5991 Age 14.3359 19.8725 0.7214 0.4779 FinenessModulus 403.7994 306.2677 1.3185 0.2003 NMAS  28.1190 25.1487  1.1181 0.2751 Thickness  2.6337 3.1514  0.8357 0.4119 NoDaysTempGT30 0.7899 0.9248 0.8541 0.4018 AADTTCoringLane  0.0348 0.0442  0.7874 0.4391 Residual standard error: 205.9 on 23 degrees of freedom; Multiple R Squared: 0.2231. For RAC O pavements: ( ) 358.6533 1.4151 (%) 18.9136 ( ) 476.3388 145.9686 ( ) 5.2328 ( ) 1.7772 30 0.0048 MPD micron AirVoid Age year FinenessModulus NMAS mm Thickness mm NumberOfDays C AADTTinCoringLane = − × + × + × − × + × − × > + × ( 3.5) Value Std. Error t value P value ( Intercept) 358.6533 827.2495 0.4335 0.6671 AirVoid  1.4151 10.8988  0.1298 0.8974 Age 18.9136 12.2301 1.5465 0.1303 FinenessModulus 476.3388 171.6864 2.7745 0.0085 NMAS  145.9686 30.3248  4.8135 < 0.0001 Thickness 5.2328 3.8549 1.3574 0.1826 NoDaysTempGT30  1.7772 0.6327  2.8089 0.0078 AADTTCoringLane 0.0048 0.0145 0.3298 0.7434 Residual standard error: 167 on 38 degrees of freedom; Multiple R Squared: 0.6447. 24 UCPRC RR 2009 01 The results show that within each mix type, air void content has no significant effect on the value of MPD. Fineness modulus is significant in affecting the macrotexture of open graded pavements, including both OGAC and RAC O, marginally significant in affecting the macrotexture of DGAC pavements, and insignificant for RAC G pavements. Generally, macrotexture increases with fineness modulus, with increasing fineness modulus indicating a coarser gradation. Layer thickness is only significant on DGAC pavements. Thicker DGAC layers have lower macrotexture, probably due to better compaction of thicker layers. Higher temperature duration, in terms of number of days with air temperature greater than 30 º C, is a significant factor on RAC O pavements but not on other types of pavement. The effect of pavement age on macrotexture is much more prominent ( in terms of both statistical significance and practical significance) on nonrubberized pavements ( DGAC and OGAC) than on rubberized pavements ( RAC G, and RAC O). 3.3 Summary of Findings The following findings were obtained regarding macrotexture: 1. Among all mixes investigated, F mixes have the highest MPD. RAC G mixes have higher MPD values than the dense graded mixes, while open graded mixes have higher MPD values than RAC G mixes. Among the two open graded mixes, RAC O mixes have lower MPD values than OGAC mixes. 2. MPD generally increases with pavement age. The age effect on macrotexture is much more prominent ( in terms of both statistical significance and practical significance) on nonrubberized pavements ( DGAC and OGAC) than on rubberized pavements ( RAC G, and RAC O). The growth rate ( with age) of MPD is significantly higher on OGAC pavements than on DGAC pavements. The growth rates of MPD of RAC G and RAC O pavements are not statistically different from those of DGAC pavements. 3. Within each mix type, air void content has no significant effect on the value of MPD. 4. Fineness modulus is significant in affecting the macrotexture of open graded pavements, including both OGAC and RAC O, marginally significant in affecting the macrotexture of DGAC pavements, and insignificant for RAC G pavements. Generally the coarser the mix gradation is ( i. e., higher fineness modulus), the larger the MPD. 5. Layer thickness is only significant on DGAC pavements. Thicker DGAC layers have lower macrotexture, probably due to better compaction of thicker layers. 6. The macrotexture of RAC O pavements decreases with the number of high temperature days. UCPRC RR 2009 01 25 4. SURFACE DISTRESS RESULTS AND ANALYSIS Traffic closures were not included in the scope of the the third year survey. Therefore, pavement conditions were evaluated using a method different from the one used the previous two years. In the first two years’ surveys, the truck lane was temporarily closed and pavement conditions were measured, visually assessed, and recorded on site during the traffic closure. During the third year survey, highresolution digital photos were taken from the shoulder along the whole length of each section, and pavement conditions were assessed afterwards, based on pavement surface images. A variety of flexible pavement distresses, consistent with the descriptions in the Caltrans Office Manual ( part of the Guide to the Investigation and Remediation of Distress in Flexible Pavements [ 4]), were recorded. It must be noted that some distresses such as rutting could not be evaluated accurately solely with surface images. Because of the differences in distress assessment in the first two years and the third year, some distresses were recorded as less severe in the third year than in the previous years. A basic assumption was made in post processing the distress data that the third year distress was no less than the second year. In this report, six major distress types, including bleeding, rutting, transverse/ reflective cracking, raveling, and wheelpath cracking, were analyzed for four pavement types: DGAC, OGAC, RAC G, and RAC O. The numbers of sections included in the survey are 16, 18, 11, and 20 for DGAC, OGAC, RAC G, and RAC O pavements, respectively. The evaluation of distresses answers these questions: • Do the initiation and progression of distresses differ for different mixes? • How do traffic and climate affect distress initiation and progression? The hypotheses regarding the effects of the explanatory variables on distress development are discussed in Reference ( 1), and will be revisited in more detail at the conclusion of the fourth year of measurement, analysis and modeling. The distresses present on the pavement surface at the time of construction of the overlays is not known. The current condition of the pavement layers beneath the overlays is also not known. 26 UCPRC RR 2009 01 4.1 Bleeding In the survey, bleeding is reported in terms of severity— low, medium, and high— and extent, expressed as the percentage of the total area with bleeding. In the analysis for this study, 3 percent of the test section area with bleeding was selected as the threshold for the start of bleeding. 4.1.1 Descriptive Analysis Figure 4.1 shows the percentage of bleeding area measured in three consecutive years for individual pavement sections of four mix types: DGAC, OGAC, RAC G, and RAC O. In this figure, bleeding includes all three severity levels ( low, medium, and high). The figure shows that bleeding may appear two to four years after construction on all pavement types, and it tends to appear earlier on rubberized pavements than on nonrubberized ones. Among the four mix types, RAC G pavements seem to be most susceptible to bleeding in terms of both the time of occurrence and the extent of distress. Age ( year) Bleed ingAll (%) 0 5 10 15 20 0 20 40 60 DGAC Age ( year) Bleed ingAll (% ) 0 5 10 15 20 0 20 40 60 OGAC 01 N104 01 N105 Age ( year) Blee d ingAll (%) 0 5 10 15 20 0 20 40 60 RAC G QP 19 QP 39 QP 46 Age ( year) Blee d ingAll (%) 0 5 10 15 20 0 20 40 60 RAC O QP 24 Figure 4.1: Bleeding development trend over three years for each pavement section. Figure 4.2 shows the percentage of sections with bleeding over three consecutive years for the four pavement types: DGAC, OGAC, RAC G, and RAC O. It can be seen that bleeding develops with UCPRC RR 2009 01 27 pavement age, and RAC G pavements show the most bleeding in all three years among the four pavement types. 0 5 10 15 20 25 30 35 40 45 50 55 60 1 2 3 1 2 3 1 2 3 1 2 3 DGAC OGAC RAC G RAC O Sections with Bleeding (%) Figure 4.2: Percentage of pavement sections of the four mix types with at least 3 percent of their area showing bleeding for each of the three measured years. 4.1.2 Regression Analysis Regression analysis was performed to evaluate the effects of traffic, climate, and mix type on bleeding. The percentage of pavement surface area with bleeding is selected as the response variable. Table 4.1 shows the results of the single variable regression analysis. Based on a 95 percent confidence level, Age, Cc( coefficient of curvature), annual average rainfall, cumulative wet days, and annual freeze thaw cycles are significant factors. Mix type, air void content and other mix properties, and traffic volume are all insignificant. The R2 value, however, is very small for every model, indicating a poor fitting of the singlevariable regression model. Based on the results in Table 4.1, multiple regression analysis was conducted to account for the effect of various factors simultaneously. The regression equation, Equation 4.1, is (%) 8.31833 1.34027 ( ) 3.05324 ( ) 12.74202 ( ) 2.3931 ( ) 1.1134 0.00261 ( ) 0.04448 Bleeding Age year ind MixTypeOGAC ind MixTypeRAC G ind MixTypeRAC O FinenessModulus AverageAnnualRainfall mm AverageAnnualWetDay = − + × + × + × − + × − − × + × + × 0.06624 30 0.20956 331.3915 ( 10 6) s NumberOfDays C AnnualFTCycles CumulativeAADTTinCoringLane e + × > − × + × ( 4.1) 28 UCPRC RR 2009 01 where ind(⋅) is an indicator function, 1 if the variable in the parentheses is true and 0 if false. Table 4.1: Regression Analysis of Single Variable Models for Bleeding Model Number Variable Name Coefficient P value Constant Term R2 1 Age ( year) 1.1707131 < 0.001  0.277 0.080 2 Air void Content (%) 0.0097543 0.956 4.969 < 0.001 3 Mix Type 2.1498328 0.399 2.601 0.074 4 Rubber Inclusion 2.7343317 0.148 3.710 0.012 5 Fineness Modulus 0.8046441 0.714 1.244 0.001 6 NMAS ( mm)  0.0514452 0.888 5.686 < 0.001 7 Cu  0.0155074 0.810 5.556 < 0.001 8 Cc 1.9569111 < 0.001  1.458 0.090 9 Surface Thickness ( mm)  0.0644396 0.233 7.529 0.008 10 Average Annual Rainfall ( mm)  0.0042234 0.042 7.613 0.023 11 Age * Average Annual Rainfall ( mm) 0.0005775 0.192 3.601 0.010 12 Average Annual Wet Days  0.0077203 0.672 5.618 0.001 13 Age * Average Annual Wet Days 0.0108951 0.002 1.604 0.051 14 Average Annual Max. Daily Air Temp ( º C) 0.5352416 0.150  7.258 0.012 15 Annual Number of Days > 30 º C 0.0277209 0.139 2.884 0.012 16 Annual Degree Days > 30 º C 0.0007627 0.147 2.993 0.012 17 Annual FT Cycles  0.1649969 0.023 7.203 0.029 18 Annual AADTT per Coring Lane 0.0000142 0.185 4.037 0.010 The estimated coefficients of the independent variables and corresponding P values are shown below: Value Std. Error t value P value ( Intercept)  8.31833 15.57583  0.5341 0.5940 Age 1.34027 0.31703 4.2276 < 0.0000 PvmntTypeOGAC 3.05324 3.99935 0.7634 0.4463 PvmntTypeRAC G 12.74202 3.67548 3.4668 0.0007 PvmntTypeRAC O 2.39310 3.87593 0.6174 0.5378 FinenessModulus  1.11340 3.42440  0.3251 0.7455 AvgAnnualRainfall 0.00261 0.00253 1.0319 0.3037 AvgAnnualWetDays 0.04448 0.01987 2.2388 0.0265 NoDaysTempGT30 0.06624 0.02138 3.0981 0.0023 AnnualFTCycles  0.20956 0.07501  2.7936 0.0058 Age* AADTTCoringLane 331.39150 124.13478 2.6696 0.0084 Residual standard error: 11.21 on 160 degrees of freedom; Multiple R Squared: 0.28. The results show that at the 95 percent confidence level, age, pavement type, average annual wet days, number of days with temperature greater than 30 º C, annual freeze thaw cycles, and cumulative truck traffic are significant in affecting bleeding. Bleeding area increases with age, number of wet days, number of high temperature days, and cumulative truck traffic, but decreases with the number of freeze thaw UCPRC RR 2009 01 29 cycles. Higher freeze thaw cycles indicate that the pavement is in a colder region, where bleeding is less likely to occur. Among the four pavement types, OGAC and RAC O pavements are not significantly different from DGAC pavement, but RAC G pavement is significantly ( statistically) more prone to bleeding. 4.2 Rutting In the first two year survey, the maximum rut depth at every 25 m of the test section was recorded in millimeters following the 2000 Pavement Condition Survey ( PCS), and rut depth was measured across the wheelpaths with a straight edge ruler. In the third year survey, there was an unsuccessful attempt to assess the rut depth from photographs of the surface taken from the shoulder. For this reason, it is assumed that the rut depth in the third survey year was no less than those in the previous survey years. In the analysis, a maximum of a 3 mm rut present on at least 25 m of the total section ( 125 or 150 m) was assumed as the threshold for the occurrence of rutting. 4.2.1 Descriptive Analysis Figure 4.3 shows the rut depths measured in three consecutive years ( essentially the first two years of measurement) for individual pavement sections of four mix types: DGAC, OGAC, RAC G, and RAC O. The figure shows that rutting may appear four to six years after construction on all pavement types, but it only appeared on a few pavement sections. Because OGAC, RAC G, and RAC O are typically constructed as thin overlays rutting on these pavements is significantly affected by the mix properties of the underlying layers. Therefore, comparison of the rutting resistance of the four mixes cannot be made without knowledge of the underlying layers. Figure 4.4 shows the percentage of sections with rutting in three consecutive survey years for the four pavement types: DGAC, OGAC, RAC G, and RAC O. It can be seen that rutting develops with pavement age, and that DGAC pavements show more rutting than other pavement types in all three years. 30 UCPRC RR 2009 01 Figure 4.3: Rutting development trend in three years for each pavement section. 0 5 10 15 20 25 30 35 40 45 50 1 2 3 1 2 3 1 2 3 1 2 3 DGAC OGAC RAC G RAC O Sections with Rutting (%) Figure 4.4: Percentage of pavement sections with rutting of at least 3 mm on at least 25 m of a 150 m long section in the first two years of measurement for four mix types. UCPRC RR 2009 01 31 4.2.2 Regression Analysis Because the number of sections with rutting is small and the third year data are rough estimates, no regression analysis was performed on the rutting data. 4.3 Transverse/ Reflective Cracking Because all the sections investigated in this study are overlays of AC or PCC and it is difficult to distinguish the thermal and reflective cracking mechanisms based only on surface condition observations, the analysis in this study combines thermal cracking and reflective cracking as one distress type. 4.3.1 Descriptive Analysis In the condition survey, the number and length of transverse/ reflective cracks were recorded for each of three severity levels ( low, medium, and high) for each 25 m subsection. The average length of transverse/ reflective cracking ( at all severity levels) per unit length of pavement is shown in Figure 4.5 for three survey years for four pavement types. Age ( year) Transverse and Reflective Cracking ( m/ m) 0 5 10 15 20 0.0 0.2 0.4 0.6 0.8 1.0 DGAC QP 09 Age ( year) Transverse and Reflective Cracking ( m/ m) 0 5 10 15 20 0.0 0.2 0.4 0.6 0.8 1.0 OGAC QP 22 Age ( year) Transverse and Reflective Cracking ( m/ m) 0 5 10 15 20 0.0 0.2 0.4 0.6 0.8 1.0 RAC G QP 05 QP 14 QP 46 Age ( year) Transverse and Reflective Cracking ( m/ m) 0 5 10 15 20 0.0 0.2 0.4 0.6 0.8 1.0 RAC O Figure 4.5: Transverse/ reflective cracking development trends in three years for each pavement section. 32 UCPRC RR 2009 01 It can be seen that transverse/ reflective cracking generally propagates with pavement age. The transverse/ reflective cracks seem to initiate earlier and propagate faster on the rubberized asphalt pavements ( RAC G and RAC O) than on the nonrubberized pavements ( DGAC and OGAC). As pointed out in the two year noise study report ( 2), the increased cracking in the rubber mixes may be biased by the condition of the underlying pavements because RAC G and RAC O mixes tend to be placed more on pavements with a greater extent of existing cracking. A 5 m total transverse crack length out of 125 or 150 m was assumed as the threshold of transverse/ reflective cracking. With this threshold, Figure 4.6 shows the percentage of sections with transverse and reflective cracking in three consecutive survey years for the four pavement types: DGAC, OGAC, RAC G, and RAC O. It can be seen that the percentage of sections with transverse/ reflective cracking increased significantly from the first survey year to the second survey year for pavements overlaid with open graded mixes ( OGAC and RAC O), but stayed relatively stable for pavements overlaid with DGAC and RAC G mixes. From the second survey year to the third survey year, the percentage of cracked sections does not change for any pavement type. 0 5 10 15 20 25 30 35 40 45 50 1 2 3 1 2 3 1 2 3 1 2 3 DGAC OGAC RAC G RAC O Sections with Transverse/ Reflective Cracking (%) Figure 4.6: Percentage of pavement sections with 5 m of transverse/ reflective cracking in 150 m section in three years for four mix types. UCPRC RR 2009 01 33 4.3.2 Statistical Analysis Regression analysis was performed to evaluate the effects of traffic, climate, and mix properties on transverse/ reflective cracking. The total length of the cracks ( at all severity levels) was selected as the response variable. A single variable regression analysis was first conducted to check the correlation between the dependent variable and each independent variable, and then a multiple regression model was estimated to consider the effects of various variables simultaneously. Results of the single variable regression analysis are given in Table 4.2. To account for the effects of underlying layers, the following variables were included in the analysis: the presence of a PCC underlayer ( determined from coring), thickness of the layer underneath the surface, and the presence of cracking in the layer underneath the surface. The P values less than 0.05 are shown in bold, indicating statistical significance at the 95 percent confidence interval. Table 4.2: Regression Analysis of Single Variable Models for Transverse/ Reflective Cracking Model Number Variable Name Coefficient P value Constant Term R2 1 Age ( year) 0.0118358 0.009 0.043 0.037 2 Air void Content (%)  0.0031251 0.228 0.133 0.008 3 Mix Type  0.0586000 0.128 0.101 0.038 4 Rubber Inclusion 0.0531253 0.057 0.071 0.020 5 Fineness Modulus  0.0766643 0.017 0.479 0.033 6 PCC Below ( 1  yes) 0.1147345 0.025 0.052 0.043 7 Underneath Layer Thickness ( mm)  0.0002376 0.392 0.103 0.006 8 Cracking in Underneath Layer ( 1  yes)  0.0165455 0.575 0.073 0.003 9 Surface Thickness ( mm)  0.0002858 0.721 0.107 0.001 10 Average Annual Rainfall ( mm)  0.0000898 0.003 0.151 0.048 11 Age * Average Annual Rainfall ( mm) 0.0000030 0.649 0.089 0.001 12 Average Annual Wet Days  0.0008404 0.002 0.161 0.055 13 Age* Average Annual Wet Days 0.0000450 0.399 0.082 0.004 14 Average Annual Max. Daily Air Temp ( º C) 0.0136164 0.013  0.216 0.034 15 Annual Number of Days > 30 º C 0.0007965 0.004 0.035 0.047 16 Annual Degree Days > 30 º C 0.0000221 0.004 0.038 0.045 17 Annual FT Cycles  0.0025364 0.018 0.130 0.031 18 Annual AADTT per Coring Lane 0.0000146 0.126 0.079 0.013 Results of the single variable regression analysis indicate that transverse/ reflective cracking may be significantly affected by pavement age, aggregate gradation ( in terms of Fineness Modulus), the existence of underlying PCC slabs, rainfall, high temperature days, and freeze thaw cycles. Based on the results in Table 4.2, multiple regression analysis was conducted to account for the effect of various factors simultaneously. The regression equation, Equation 4.2, is 34 UCPRC RR 2009 01 / Re ( / ) 0.271686 0.004845 (%) 0.018047 ( ) 0.188134 ( ) 0.054069 ( ) 0.136324 ( ) 0.025383 ( ) 0.018369 ( Transverse flectiveCracking m m AirVoid Age year ind MixTypeOGAC ind MixTypeRAC G ind MixTypeRAC O ind PCCBelow ind C = + × + × − × − × − − × − − × + × ) 0.003510 ( ) 0.000447 ( ) 0.000014 ( ) 0.000224 0.001113 30 0.000585 8.170241 rackBelow SurfaceThickness mm UnderlyingThickness mm AverageAnnualRainfall mm AverageAnnualWetDays NumberOfDays C AnnualFTCycles Cu − × − × + × − × − × > − × + × mulativeAADTTinCoringLane( 10e6) ( 4.2) where ind(⋅) is an indicator function, 1 if the variable in the parentheses is true and 0 if false. The estimated coefficients of the independent variables and corresponding P values are shown below: Value Std. Error t value P value ( Intercept) 0.271686 0.104323 2.6043 0.0107 AirVoid 0.004845 0.003805 1.2734 0.2059 Age 0.018047 0.004194 4.3028 0.0000 PvmntTypeOGAC  0.188134 0.054370  3.4602 0.0008 PvmntTypeRAC G  0.054069 0.037564  1.4394 0.1533 PvmntTypeRAC O  0.136324 0.047260  2.8846 0.0048 PCCBelow  0.025383 0.046622  0.5445 0.5874 CrackBelow 0.018369 0.031515 0.5829 0.5613 Thickness  0.003510 0.001063  3.3007 0.0014 UnderlyingThickness  0.000447 0.000325  1.3771 0.1717 AvgAnnualRainfall 0.000014 0.000030 0.4716 0.6383 AvgAnnualWetDays  0.000224 0.000230  0.9762 0.3314 NoDaysTempGT30  0.001113 0.000351  3.1712 0.0020 AnnualFTCycles  0.000585 0.000999  0.5855 0.5596 Age* AADTTCoringLane 8.170241 3.549995 2.3015 0.0235 Residual standard error: 0.1153 on 97 degrees of freedom; Multiple R Squared: 0.49. The results show that at the 95 percent confidence level, age, pavement type, overlay thickness, number of days with temperature greater than 30 º C, and cumulative truck traffic are significant in affecting transverse/ reflective cracking. The crack length increases with age and cumulative truck traffic, but d 



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