January 2009
Research Report: UCPRC- RR- 2009- 01
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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, dense-graded,
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 dense-graded
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 gap-graded
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: First-and
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 F-mixes,
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 highly-correlated
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 RAC-O,
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 dense-and
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, high-resolution
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 single-variable
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