STATE OF CALIFORNIA DEPARTMENT OF TRANSPORTATION
TECHNICAL REPORT DOCUMENTATION PAGE
TR0003 ( REV. 10/ 98)
1. REPORT NUMBER
CA09- 0291
2. GOVERNMENT ASSOCIATION NUMBER
3. RECIPIENT’S CATALOG NUMBER
5. REPORT DATE
December 2008
4. TITLE AND SUBTITLE
Pavement Performance Evaluation, Phase II – Data Collection
6. PERFORMING ORGANIZATION CODE
7. AUTHOR( S)
Sameh Zaghloul
8. PERFORMING ORGANIZATION REPORT NO.
10. WORK UNIT NUMBER
9. PERFORMING ORGANIZATION NAME AND ADDRESS
Stantec Consulting Inc.
150 Lawrence Bell Drive, Suite 110
Buffalo, NY 14221
and
H. W. Lochner, Inc.
310 Fullerton Ave, Suite 200
Newburgh, NY 12550
11. CONTRACT OR GRANT NUMBER
65A0463
13. TYPE OF REPORT AND PERIOD COVERED
Final Report
Sept 2004 – June 2008
12. SPONSORING AGENCY AND ADDRESS
California Department of Transportation
Division of Research and Innovation, MS- 83
1227 O Street
Sacramento, CA 95819
14. SPONSORING AGENCY CODE
15. SUPPLEMENTAL NOTES
16. ABSTRACT
Phase I and II of this study tested approximately 1500 rehabilitated pavements ( asphalt and PCC)
throughout the State. These pavements ranged from 5 to 15 years old and were intended to develop a
snapshot of how various rehabilitations were performing. Data for each site consisted of office data
( asbuilts), field testing ( FWD, distress and coring) and laboratory testing ( Rice, gradation, etc.). Data
was provided in an Access database. Indices were created for structural, roughness and distress as an
evaluation tool to compare sections. Initial analyses using these indices were made on RAP and RAC
sections throughout the State which showed that the data provides a basis for comparing strategies.
17. KEY WORDS
Asphalt pavement, PCC pavement, rehabilitation,
field testing, laboratory testing
18. DISTRIBUTION STATEMENT
No restrictions. This document is available to the
public through the National Technical Information
Service, Springfield, VA 22161
19. SECURITY CLASSIFICATION ( of this report)
Unclassified
20. NUMBER OF PAGES
187
21. PRICE
Reproduction of completed page authorized
Division of Research
& Innovation
Report CA09- 0291
December 2008
Pavement Performance Evaluation,
Phase II – Data Collection
Final Report
Pavement Performance Evaluation
Phase II – Data Collection
Final Report
Report No. CA09- 0291
December 2008
Prepared By:
Stantec Consulting Inc.
150 Lawrence Bell Drive, Suite 110
Buffalo, NY 14221
and
H. W. Lochner, Inc.
310 Fullerton Ave, Suite 200
Newburgh, NY 12550
Prepared For:
California Department of Transportation
Division of Research and Innovation, MS- 83
1227 O Street
Sacramento, CA 95814
DISCLAIMER STATEMENT
This document is disseminated in the interest of information exchange. 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 publication does not constitute a standard, specification
or regulation. This report does not constitute an endorsement by the Department of any product
described herein.
For individuals with sensory disabilities, this document is available in Braille, large print,
audiocassette, or compact disk. To obtain a copy of this document in one of these alternate formats,
please contact: the Division of Research and Innovation, MS- 83, California Department of
Transportation, P. O. Box 942873, Sacramento, CA 94273- 0001.
Final Report for Pavement Performance
Evaluation, Phase II – Data Collection
Submitted by:
Stantec Consulting Inc.
150 Lawrence Bell Drive, Suite 110
Buffalo, NY 14221
and
H. W. Lochner, Inc.
310 Fullerton Ave, Suite 200
Newburgh, NY 12550
Submitted to:
Caltrans
Division of Research and Technology, MS# 5
5900 Folsom Blvd.
Sacramento, CA 95819
December 23, 2008
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Executive Summary
In 2000, Caltrans initiated Pavement Performance Evaluation - Phase I research project. The overall
goals were to evaluate the performance of different pavement types and treatments across
California and investigate the impact of different factors ( design parameters, materials, construction
variables, and environmental effects) on actual pavement performance. In total, around 1,000 test
sections were evaluated in this phase, located in all but one of California’s districts and all but one of
the state’s environmental zones.
The Phase I study concluded that two main issues limited the completeness of the analysis: the
absence of traffic count data and the unbalanced distribution of test sections among districts and
environmental zones. The Pavement Performance Evaluation - Phase II project was initiated in 2004
to address these issues and expand the Phase I investigations and analyses. The main goals of
Phase II were to:
1. Select and test approximately 500 additional test sections to enhance the project dataset.
This was referred to as the Phase II Main Study.
2. Ensure compatibility between the Phase I and Phase II data through harmonization of data
collection and QC techniques between phases. A further task was the performance of an
FWD correlation study account for any difference in collected deflection data that was
attributable to use of different FWD equipment.
3. Perform a limited seasonal study to develop seasonal and temperature adjustment models.
These models would be used to adjust FWD data for seasonal and temperature variations
and bring pavement response parameters measured at different times of the day and year to
the same standard conditions.
4. Perform a traffic study to estimate the accumulative axle weights that passed over Phase I
and II sections since the construction of the last rehabilitation treatment. This would allow a
more accurate assessment of how well a particular treatment has performed relative to the
traffic loading it has been subjected to.
The information in this report represents results of the Phase II analyses performed up to the
allowed limit of contract funds.
In the Seasonal Study, temperature adjustment models were developed for each sensor ( D1- D9) for
flexible and rigid pavements. These models were applied to the collected deflection data for Phase I
and II Main Study sections to bring all deflections to the same standard temperature.
In the Traffic Study, axle weight data was collected for the Main Study test sections. Using the
collected data and Caltrans permanent weigh station data, the total accumulated traffic carried since
the last rehabilitation was estimated for 888 sections.
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In the FWD Correlation Study, models were developed that would account for any differences in the
measured deflections that were attributable to use of the different FWD units. However, as the team
successfully achieved the primary goal of not using different units, the models did not need to be
implemented.
In the Phase II Main Study, 537 sections were tested using ostensibly the same data collection and
QC/ QA procedures as in Phase I. The Phase II database was populated with office, field and
laboratory data for these sections. Analyses were then conducted on two individual treatments – 60
Recycled Asphalt Pavement ( RAP) sections and 69 Rubberized Asphalt Concrete ( RAC) sections.
Each treatment was evaluated in a number of environmental zones to assess the treatment’s
performance and to determine the effect of environmental conditions on that performance.
The performance evaluation covered all aspects of pavement performance – structural through the
Structural Adequacy Index ( SAI), functional through the Roughness Index ( RI), and distresses
through the Distress Index ( DI). Each of these indices had a 0.0- 1.0 scale, where 1.0 was a perfect
pavement section and 0.5 was the assumed trigger level for rehabilitation.
As the test sections in this study had been in service for differing numbers of years, age adjustment
was performed on the SAI, RI, and DI values to bring all values to those of the pavement section at
age 5 years. This would allow for fair comparison of performance of sections with different ages. The
effect of different accumulated traffic levels was not accounted for at this time.
For each pavement section, the expected service lives based on SAI, RI, and DI were calculated as
the age at which the index would reach the assumed trigger level of 0.5. This resulted in the
measures of Structural Service Life ( SSL) based on SAI, Distress Service Life ( DSL) based on DI,
and Roughness Service Life ( RSL) based on RI.
For the 60 RAP sections considered in these analyses, the average expected SSL, DSL, and RSL
for each environmental zone are shown in Table E- 1.
Table E- 1: Average Expected Service Lives of RAP Sections by Environmental Zone
SSL ( years) DSL ( years) RSL ( years)
North Coast 19 18 20
Desert 19 9 20
Mountain 20 14 19
If the shortest of the 3 service lives will control when rehabilitation is required, then the RAP sections
in the North Coast, Desert, Mountain zones would all be triggered for distresses first, after 18, 9, and
14 years, respectively. However, if appropriate and timely maintenance is performed, the DSL of
these sections could be significantly increased. In this case, the RAP sections in the North Coast
and Desert zones would instead be triggered for structural performance, both after 19 years. RAP
sections in the Mountain zone would be triggered for ride quality, again after 19 years.
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For the 69 RAC sections considered in these analyses, the average expected SSL, DSL, and RSL
for each environmental zone are shown in Table E- 2.
Table E- 2: Average Expected Service Lives of RAC Sections by Environmental Zone
SSL ( years) DSL ( years) RSL ( years)
Central Valley 18 16 18
North Coast 16 16 20
Bay Area 19 19 19
Desert 19 15 19
South Coast 20 10 20
If the shortest of the 3 service lives will control when rehabilitation is required, then RAC sections in
the Central Valley, Desert, and South Coast zones would be triggered for distresses first, after 16,
15, and 10 years, respectively. However, if appropriate and timely maintenance is performed, the
DSL of these sections could be significantly increased. In this case, RAC sections in these zones
would instead be triggered for ride quality or structural performance after 18, 19 or 20 years,
respectively. In the North Coast zone, the RAC sections will be triggered for structural adequacy or
distresses first after 16 years. In the Bay Area zone, the RAC sections may be triggered for
structural adequacy, distresses or ride quality first, after 19 years.
The noticeably lower distress performance of the South Coast zone RAC sections was noted in the
report and further investigation is recommended in this area.
Analysis of the sections’ structural performance was based on FWD data that had been corrected
using the temperature adjustment models developed in the Seasonal Study. A comparison of RAP
structural performance analysis before and after applying the temperature adjustment models
highlighted the importance of using temperature- corrected deflections when assessing a pavement
section’s structural performance.
A substantial amount of data has been collected and analyzed in this study so far. However, the
report recommends the performance of the additional analysis required to fully complete the Phase II
project. In comparison with the significant effort already expended, the effort required to complete
these additional analyses should be minimal and is expected to produce a very positive return.
Further recommendations include the monitoring of additional test sections within the Seasonal
Study to enhance the developed temperature adjustment models and for Caltrans to continue
monitoring some of the Main Study sections to gain additional long- term data.
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Table of Contents
EXECUTIVE SUMMARY E. 1
1.0 PROJECT BACKGROUND.......................................................................................................... 1.1
1.1 OVERVIEW OF PHASE I PROJECT............................................................................................ 1.1
1.2 RECOMMENDATIONS OF PHASE I PROJECT ......................................................................... 1.4
1.3 OBJECTIVES OF PHASE II PROJECT........................................................................................ 1.4
2.0 MAIN STUDY DATA..................................................................................................................... 2.1
2.1 TEST SECTIONS....................................................................................................................... .. 2.1
2.2 DATA COLLECTION..................................................................................................................... 2.2
2.2.1 Office Data......................................................................................................................... 2.2
2.2.2 Field Data .......................................................................................................................... 2.2
2.2.2.1 RT3000 Survey ................................................................................................... 2.2
2.2.2.2 Visual Distress Survey ( VDS) Data..................................................................... 2.3
2.2.2.3 Falling Weight Deflectometer ( FWD) Data ......................................................... 2.4
2.2.2.4 Core/ Bore Data ................................................................................................... 2.5
2.2.2.5 Field Classified Subgrade Data .......................................................................... 2.5
2.2.2.6 Site Characterization Data .................................................................................. 2.6
2.2.3 Laboratory Data................................................................................................................. 2.6
2.2.4 Database ........................................................................................................................... 2.7
2.3 ENHANCEMENTS TO MAIN STUDY DATA................................................................................ 2.7
3.0 SEASONAL STUDY..................................................................................................................... 3.1
3.1 FIELD TESTING ........................................................................................................................... 3.1
3.1.1 Test Sections ..................................................................................................................... 3.1
3.1.2 FWD Testing Protocols...................................................................................................... 3.2
3.1.3 Testing Frequency............................................................................................................. 3.4
3.2 DEVELOPMENT OF TEMPERATURE ADJUSTMENT MODELS – FLEXIBLE PAVEMENTS... 3.4
3.2.1 Model Development........................................................................................................... 3.4
3.2.2 Application of Models ........................................................................................................ 3.6
3.2.2.1 Sample Application ............................................................................................. 3.7
3.3 DEVELOPMENT OF TEMPERATURE ADJUSTMENT MODELS – RIGID PAVEMENTS ....... 3.10
3.3.1 Model Development......................................................................................................... 3.10
3.3.2 Application of Models ...................................................................................................... 3.19
3.3.2.1 Sample Application ........................................................................................... 3.19
3.4 APPLICATION OF TEMPERATURE ADJUSTMENT MODELS TO MAIN STUDY DATA ........ 3.22
4.0 TRAFFIC STUDY.......................................................................................................................... 4.1
4.1 TRAFFIC DATA ANALYSIS.......................................................................................................... 4.2
4.1.1 Determination of Traffic at Permanent Weigh Station Locations ...................................... 4.2
4.1.2 Estimation of Annual ESALs from WIM Survey Measurements........................................ 4.8
4.2 APPLICATION OF TRAFFIC STUDY DATA TO MAIN STUDY TEST SECTIONS................... 4.14
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5.0 FWD CORRELATION STUDY ..................................................................................................... 5.1
5.1 TEST SECTIONS....................................................................................................................... .. 5.2
5.2 FIELD TESTING ........................................................................................................................... 5.4
5.3 ANALYSIS....................................................................................................................... ............. 5.4
5.3.1 Flexible Pavement ............................................................................................................. 5.5
5.3.2 Rigid Pavements.............................................................................................................. 5.20
5.4 APPLICATION OF FWD CORRELATION MODELS TO PROJECT FWD DATA...................... 5.26
6.0 ANALYSIS PROCEDURE............................................................................................................ 6.1
6.1 PERFORMANCE INDICES........................................................................................................... 6.1
6.1.1 Structural Adequacy Index ( SAI) ....................................................................................... 6.1
6.1.2 Distress Index ( DI)............................................................................................................. 6.3
6.1.3 Roughness Index ( RI)........................................................................................................ 6.3
6.2 AGE ADJUSTMENT OF PERFORMANCE INDICES .................................................................. 6.3
6.2.1 Age Adjustments for SAI and RI........................................................................................ 6.3
6.2.2 Age Adjustments for DI...................................................................................................... 6.4
6.3 PERFORMANCE EVALUATION .................................................................................................. 6.5
6.3.1 Performance Classes ........................................................................................................ 6.5
6.3.2 Expected Service Lives ..................................................................................................... 6.6
7.0 RECYCLED ASPHALT PAVEMENT ........................................................................................... 7.1
7.1 IN- SITU STRUCTURAL PERFORMANCE – SAI......................................................................... 7.3
7.1.1 Prior to Application of Temperature Adjustment Models................................................... 7.3
7.1.2 After Application of Temperature Adjustment Models....................................................... 7.6
7.2 DISTRESS PERFORMANCE – DI ............................................................................................... 7.9
7.3 RIDE QUALITY PERFORMANCE – RI ...................................................................................... 7.12
7.4 CONCLUSIONS.................................................................................................................... ..... 7.15
8.0 RUBBERIZED ASPHALT CONCRETE ....................................................................................... 8.1
8.1 IN- SITU STRUCTURAL PERFORMANCE - SAI.......................................................................... 8.4
8.2 DISTRESS PERFORMANCE – DI ............................................................................................... 8.7
8.3 RIDE QUALITY PERFORMANCE – RI ...................................................................................... 8.14
8.4 CONCLUSIONS.................................................................................................................... ..... 8.17
9.0 SUMMARY, CONCLUSIONS & RECOMMENDATIONS ............................................................ 9.1
9.1 SUMMARY........................................................................................................................ ........... 9.1
9.2 CONCLUSIONS.................................................................................................................... ....... 9.1
9.3 RECOMMENDATIONS................................................................................................................ 9.3
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APPENDIX A: PHASE II TEST SECTIONS............................................................................................ A. 1
APPENDIX B: DATABASE TABLES ..................................................................................................... B. 1
APPENDIX C: DATES OF SEASONAL FWD TESTING........................................................................ C. 1
APPENDIX D: TRAFFIC STUDY RESULTS .......................................................................................... D. 1
List of Tables
Table E- 1: Average Expected Service Lives of RAP Sections by Environmental Zone............................ E. ii
Table E- 2: Average Expected Service Lives of RAC Sections by Environmental Zone........................... E. iii
Table 2- 1: Types of Collected Surface Distresses..................................................................................... 2.3
Table 3- 1: Seasonal Study Test Sections.................................................................................................. 3.3
Table 3- 2: Environmental Zone Codes ...................................................................................................... 3.4
Table 3- 3: ANOVA Testing – Summary of Results.................................................................................... 3.5
Table 3- 4: Coefficients for Temperature Adjustment Models – Flexible Pavement................................... 3.6
Table 3- 5: Ranges of Validity for Temperature Adjustment Models – Flexible Pavement ........................ 3.6
Table 3- 6: Environmental Zone Codes .................................................................................................... 3.11
Table 3- 7: ANOVA Testing – Summary of Results for Mid- Slab ............................................................. 3.12
Table 3- 8: ANOVA Testing – Summary of Results for Joint Approach ................................................... 3.13
Table 3- 9: ANOVA Testing – Summary of Results for Joint Leave ......................................................... 3.14
Table 3- 10: Coefficients for Temperature Adjustment Models – Rigid Pavement at Mid- Slab ............... 3.16
Table 3- 11: Coefficients for Temperature Adjustment Models – Rigid Pavement at Joint Approach...... 3.17
Table 3- 12: Coefficients for Temperature Adjustment Models – Rigid Pavement at Joint Leave ........... 3.18
Table 3- 13: Ranges of Validity for Temperature Adjustment Models – Rigid Pavement ........................ 3.19
Table 4- 1: Permanent Weigh Station Locations Assigned to Traffic Segments........................................ 4.2
Table 4- 2: Time of Portable WIM Survey at Example Segments .............................................................. 4.6
Table 4- 3: ESALs Calculated from Vacaville and Gilroy Weigh Stations .................................................. 4.7
Table 4- 4: Total Monthly ESALs for Vacaville ( EB) and Gilroy Weigh Stations ........................................ 4.7
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Table 4- 5: Ratios Calculated from Vacaville ( EB) and Gilroy Weigh Station Data.................................. 4.12
Table 4- 6: Annual ESALs Calculated for Example Traffic Segments...................................................... 4.12
Table 5- 1: FWD Correlation Test Sections ................................................................................................ 5.2
Table 5- 2: Values for Correlation Models – Flexible.................................................................................. 5.5
Table 5- 3: Values for Correlation Models – Flexible ( Revised D8).......................................................... 5.10
Table 5- 4: Values for Revised Correlation Models – Flexible.................................................................. 5.16
Table 5- 5: Values for Correlation Models – Rigid .................................................................................... 5.20
Table 9- 1: Average Expected Service Lives of RAP Sections by Environmental Zone ............................ 9.2
Table 9- 2: Average Expected Service Lives of RAC Sections by Environmental Zone ............................ 9.2
Table A- 1: Phase II Test Sections ............................................................................................................. A. 2
Table B- 1: Database Tables ...................................................................................................................... B. 1
Table C- 1: Dates of Seasonal FWD Testing.............................................................................................. C. 1
Table D- 1: Results of Traffic Study ............................................................................................................ D. 1
List of Figures
Figure 2- 1: Example Image File................................................................................................................. 2.3
Figure 2- 2: Example Core Image File ........................................................................................................ 2.5
Figure 3- 1: Implementation of Models ....................................................................................................... 3.7
Figure 3- 2: Example Flexible Model Implementation 96.3° F..................................................................... 3.8
Figure 3- 3: Example Flexible Model Implementation 60.0° F..................................................................... 3.9
Figure 3- 4: Example Flexible Model Implementation 71.2° F..................................................................... 3.9
Figure 3- 5: Example Rigid Model Implementation 76.0° F ....................................................................... 3.20
Figure 3- 6: Example Rigid Model Implementation 69.1° F ....................................................................... 3.21
Figure 3- 7: Example Rigid Model Implementation 58.0° F ....................................................................... 3.22
Figure 4- 1: Total 24- Hour ESALs Calculated at Each Weigh Station on Day of WIM Survey .................. 4.9
Figure 4- 2: Total Monthly ESALs Calculated at Each Weigh Station for Month of WIM Survey ............. 4.10
Figure 4- 3: Total 2005 Annual ESALs Calculated for Each Weigh Station ............................................. 4.11
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Figure 4- 4: Annual 2005 ESALs Calculated for Traffic Segments........................................................... 4.13
Figure 5- 1: Correlation Study Test Sections – Flexible ............................................................................. 5.3
Figure 5- 2: Correlation Study Test Sections - Rigid .................................................................................. 5.3
Figure 5- 3: FWD Sensor Correlation Model, Flexible – D1 ....................................................................... 5.6
Figure 5- 4: FWD Sensor Correlation Model, Flexible – D2 ....................................................................... 5.6
Figure 5- 5: FWD Sensor Correlation Model, Flexible – D3 ....................................................................... 5.7
Figure 5- 6: FWD Sensor Correlation Model, Flexible – D4 ....................................................................... 5.7
Figure 5- 7: FWD Sensor Correlation Model, Flexible – D5 ....................................................................... 5.8
Figure 5- 8: FWD Sensor Correlation Model, Flexible – D6 ....................................................................... 5.8
Figure 5- 9: FWD Sensor Correlation Model, Flexible – D7 ....................................................................... 5.9
Figure 5- 10: FWD Sensor Correlation Model, Flexible – D8 ..................................................................... 5.9
Figure 5- 11: FWD Sensor Correlation Model, Flexible – Filtered D8 Data.............................................. 5.10
Figure 5- 12: Time Gap Distribution.......................................................................................................... 5.11
Figure 5- 13: Temperature Difference Distribution.................................................................................... 5.11
Figure 5- 14: Basic Temperature Adjustment Model – D1........................................................................ 5.12
Figure 5- 15: Basic Temperature Adjustment Model – D2........................................................................ 5.13
Figure 5- 16: Basic Temperature Adjustment Model – D3........................................................................ 5.13
Figure 5- 17: Basic Temperature Adjustment Model – D4........................................................................ 5.14
Figure 5- 18: Basic Temperature Adjustment Model – D5........................................................................ 5.14
Figure 5- 19: Basic Temperature Adjustment Model – D6........................................................................ 5.15
Figure 5- 20: Basic Temperature Adjustment Model – D7........................................................................ 5.15
Figure 5- 21: Revised FWD Sensor Correlation Model, Flexible – D1 ..................................................... 5.16
Figure 5- 22: Revised FWD Sensor Correlation Model, Flexible – D2 ..................................................... 5.17
Figure 5- 23: Revised FWD Sensor Correlation Model, Flexible – D3 ..................................................... 5.17
Figure 5- 24: Revised FWD Sensor Correlation Model, Flexible – D4 ..................................................... 5.18
Figure 5- 25: Revised FWD Sensor Correlation Model, Flexible – D5 ..................................................... 5.18
Figure 5- 26: Revised FWD Sensor Correlation Model, Flexible – D6 ..................................................... 5.19
Figure 5- 27: Revised FWD Sensor Correlation Model, Flexible – D7 ..................................................... 5.19
Figure 5- 28: FWD Sensor Correlation Model, Flexible – D8 ................................................................... 5.20
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Figure 5- 29: FWD Sensor Correlation Model, Rigid – D1........................................................................ 5.21
Figure 5- 30: FWD Sensor Correlation Model, Rigid – D2........................................................................ 5.22
Figure 5- 31: FWD Sensor Correlation Model, Rigid – D3........................................................................ 5.22
Figure 5- 32: FWD Sensor Correlation Model, Rigid – D4........................................................................ 5.23
Figure 5- 33: FWD Sensor Correlation Model, Rigid – D5........................................................................ 5.23
Figure 5- 34: FWD Sensor Correlation Model, Rigid – D6........................................................................ 5.24
Figure 5- 35: FWD Sensor Correlation Model, Rigid – D7........................................................................ 5.24
Figure 5- 36: FWD Sensor Correlation Model, Rigid – D8........................................................................ 5.25
Figure 5- 37: FWD Sensor Correlation Model, Rigid – D9........................................................................ 5.25
Figure 6- 1: SAI Standard Age Deterioration Model ................................................................................... 6.2
Figure 6- 2: Age Adjustment Procedure for SAI and RI.............................................................................. 6.4
Figure 6- 3: Age Adjustment Procedure for DI............................................................................................ 6.5
Figure 7- 1: In- Situ Layer Thickness of RAP Sections – North Coast ........................................................ 7.1
Figure 7- 2: In- Situ Layer Thickness of RAP Sections – Desert ................................................................. 7.2
Figure 7- 3: In- Situ Layer Thickness of RAP Sections – Mountain............................................................. 7.2
Figure 7- 4: Average SAI5 by Environmental Zone – Before Seasonal Adjustment .................................. 7.4
Figure 7- 5: Average SAI5 by Performance Class....................................................................................... 7.4
Figure 7- 6: Distribution of SAI5................................................................................................................... 7.5
Figure 7- 7: Structural Service Life – Before Seasonal Adjustment ........................................................... 7.5
Figure 7- 8: Average SAI5 by Environmental Zone – Before & After Temperature Adjustment ................. 7.6
Figure 7- 9: Average SAI5 by Performance Class – Before & After Temperature Adjustment................... 7.7
Figure 7- 10: Distribution of SAI5 – Before & After Temperature Adjustment ............................................. 7.7
Figure 7- 11: Structural Service Life – Before & After Temperature Adjustment........................................ 7.8
Figure 7- 12: Temperature during Pavement Testing................................................................................. 7.9
Figure 7- 13: Average DI5 by Environmental Zone ................................................................................... 7.10
Figure 7- 14: Average DI5 by Performance Class..................................................................................... 7.10
Figure 7- 15: Distribution of DI5................................................................................................................. 7.11
Figure 7- 16: Distress Service Life............................................................................................................ 7.11
Figure 7- 17: Impact of Maintenance on DSL ........................................................................................... 7.12
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Figure 7- 18: Average RI5 by Environmental Zone ................................................................................... 7.13
Figure 7- 19: Average RI5 by Performance Class..................................................................................... 7.13
Figure 7- 20: Distribution of RI5................................................................................................................. 7.14
Figure 7- 21: Roughness Service Life....................................................................................................... 7.14
Figure 8- 1: In- Situ Layer Thickness of RAC Sections – Central Valley..................................................... 8.1
Figure 8- 2: In- Situ Layer Thickness of RAC Sections – North Coast ........................................................ 8.2
Figure 8- 3: In- Situ Layer Thickness of RAC Sections – Bay Area............................................................. 8.2
Figure 8- 4: In- Situ Layer Thickness of RAC Sections – Desert................................................................. 8.3
Figure 8- 5: In- Situ Layer Thickness of RAC Sections – South Coast........................................................ 8.3
Figure 8- 6: Average SAI5 by Environmental Zone ..................................................................................... 8.4
Figure 8- 7: Average SAI5 by Performance Class....................................................................................... 8.5
Figure 8- 8: Distribution of SAI5................................................................................................................... 8.6
Figure 8- 9: Structural Service Life ............................................................................................................. 8.7
Figure 8- 10: Average DI5 by Environmental Zone ..................................................................................... 8.8
Figure 8- 11: Average DI5 by Performance Class....................................................................................... 8.8
Figure 8- 12: Distribution of DI5................................................................................................................... 8.9
Figure 8- 13: Extent of Distresses – SC Zone RAC sections ................................................................... 8.10
Figure 8- 14: Example Distresses on SC Zone RAC Sections ................................................................. 8.11
Figure 8- 15: Example Distresses on SC Zone RAC Sections ................................................................. 8.12
Figure 8- 16: Example Distresses on SC Zone RAC Sections ................................................................. 8.13
Figure 8- 17: Distress Service Life............................................................................................................ 8.14
Figure 8- 18: Average RI5 by Environmental Zone ................................................................................... 8.15
Figure 8- 19: Average RI5 by Performance Class..................................................................................... 8.15
Figure 8- 20: Distribution of RI5................................................................................................................. 8.16
Figure 8- 21: Roughness Service Life....................................................................................................... 8.17
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1.0 Project Background
Caltrans initiated the first phase of the Pavement Performance Evaluation research project in 2000.
The overall goals of this project were to evaluate the performance of different pavement types and
treatments across California, and hence the success of Caltrans’ pavement design and rehabilitation
procedures. The project scope covered investigation of the impact of different factors on actual
pavement performance as compared to the designed performance. The factors considered included
design parameters, materials, construction variables, and environmental effects. At the completion of
the project, it was concluded that a number of factors that could enhance the reported results had
not been included in the project scope. Recommendations were made for additional tasks that would
enhance and improve the findings of the project.
The Pavement Performance Evaluation - Phase II project was initiated in 2004 to address these
recommendations and expand the investigations and analysis conducted in Phase I. A number of
additional tasks based on the Phase I recommendations were included in the Phase II project scope
with the intention of producing a more accurate evaluation of pavement performance in California
and, therefore, a more realistic picture of the success of Caltrans’ pavement design and
rehabilitation procedures.
This section gives a summary of the Phase I project, the identified needs for additional study, and
the objectives that the Phase II project set out to address.
1.1 OVERVIEW OF PHASE I PROJECT1
As mentioned above, the Pavement Performance Evaluation - Phase I project began in 2000 and
had the overall goal of evaluating the performance of in- service pavements across the State of
California. This in turn would give an indication of the success of Caltrans’ pavement design and
rehabilitation procedures. To meet all Caltrans’ requirements for the project, it was divided into five
main studies:
􀂃 Study 1: Construction Quality Evaluation Study
􀂃 Study 2: Concrete Pavement Rehabilitation Study
􀂃 Study 3: Asphalt Pavement Rehabilitation Study
􀂃 Study 4: Rubberized Asphalt Concrete ( RAC) Study
􀂃 Study 5: Capitol Preventive Maintenance ( CAPM) Study
In total, around 1,000 test sections were evaluated to determine the effect that environmental
conditions, design parameters, materials, and construction variables had on structural and functional
pavement performance. The test sections were located in all but one of California’s districts ( District
1 Stantec Consulting. ‘ Caltrans Pavement Performance Evaluation Services - Contract 65A0069 - Final Report’.
November 2002
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Project Background
December 23, 2008
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4 was not represented) and all but one of the state’s environmental zones ( the Bay Area zone was
not represented). The sections covered a number of different rehabilitation treatments, materials,
and pavement types in each district and zone.
Office, field, and laboratory data were collected for each pavement section and stored in the project
database. Collected data included pavement structural information, core/ bore logs, laboratory test
results, deflection data, roughness data, and surface distress data for each section.
Extensive analysis was required in order to meet the project objectives. To address the large
number of test sections and the large number of variables to be considered, three types of analysis
were performed:
􀂃 Stage I analysis – Section- level analysis
􀂃 Stage II analysis – Project- level analysis
􀂃 Stage III analysis – Across- projects analysis
In the Stage I analysis, each section was analyzed separately. The International Roughness Index
( IRI) and the Pavement Condition Index ( PCI) were used to evaluate the functional performance,
while Falling Weight Deflectometer ( FWD) deflection data was used to evaluate the pavement
structural performance. For Stage II analysis, sections located within the same project were grouped
and compared to evaluate the construction and material variability within a project. Sections within
each project were compared to evaluate cross- project consistency, structural performance,
functional performance, and material properties. In the Stage III analysis, sections and groups of
sections were compared across environmental zones and across treatment types to evaluate and
compare the performance of different treatments.
Study 1 looked at construction consistency using two approaches. In the first approach, Stage I
structural analysis results were used to evaluate the structural construction quality for each section.
A Structural Construction Quality Index ( SCQI) was developed for this purpose that was an indicator
of the degree of variability in the structural capacity along the section. SCQI was used to compare
the variation in construction consistency that occurred across different environmental zones and
different districts. Based on the sections considered in this study, it was found that some districts
had higher construction consistency, i. e. less variability, than others. In the second approach, Stage
II analysis results were used to evaluate the overall construction consistency across projects in
terms of material and thickness variability and structural capacity variability. A Construction Quality
Index ( CQI) was developed that considered a number of construction- related factors, including the
variability in materials, the actual constructed layer thicknesses, and the structural capacity between
sections within the project. Results of the CQI analysis indicated that there were, in some cases,
significant differences in construction consistency among environmental zones.
The amount of traffic loading that a pavement is subjected to during its life cycle is an extremely
important factor in determining how well a pavement has performed. If traffic loading is not
considered, a pavement that has been subjected to substantially more than the design traffic may
erroneously appear to have performed poorly, and a pavement subjected to substantially less than
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Project Background
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the design traffic may erroneously appear to have performed well. As accurate traffic data is vital to
produce reliable results and conclusions on pavement performance, the 1998 Caltrans Traffic
Database was searched for traffic counts for the selected test sections. However, the number of test
sections with measured traffic counts was limited and as a result, actual accumulated traffic data
was not considered in Phase I. Performance analysis was instead carried out in terms of the age of
the pavement. As such, it was not possible to compare the actual pavement performance against the
designed performance. For Studies 2 to 4, only the tasks to compare the performance of different
treatments were completed; tasks that involved evaluating the performance of the treatment itself
were not completed.
Analysis of data was first performed using a deterministic approach. However, the use of pavement
age instead of traffic data resulted in a large scatter in the performance results. For non- Long Term
Pavement Performance ( LTPP) study sections, the deterministic approach was deemed to yield
insignificant results. Deterministic analysis was successful for LTPP sections, but only up to Stage II.
Stage III analysis was not possible for these sections due to the different levels of construction
quality control and the limited number of sections within each treatment type. As a result, a
probabilistic analysis approach was used for Studies 2- 4 and also to address the effect that
environmental zone has on pavement performance. Pavement performance was evaluated in terms
of a Structural Adequacy Index ( SAI), IRI, and PCI. Analysis of Variance ( ANOVA) was used
compare multiple treatments.
Environmental effects on pavement performance were evaluated by assessing the performance of
several flexible pavement sections with DGAC overlay, which were distributed across the different
environmental zones. Analysis performed on these selected sections suggested that environmental
zone can have a significant effect on pavement performance – most particularly on functional rather
than structural performance. However, this analysis was based on pavement age rather than traffic
loading. A more comprehensive study that incorporated more treatments and test sections, and that
included traffic data, may produce different results.
The effect of interlayers on pavement performance was evaluated by analyzing and comparing
sections with Pavement Reinforcing Fabrics ( PRF) and Stress Absorbing Membrane Interlayer
( SAMI) against control sections without any interlayers. Results from these analyses suggested that
use of interlayers generally had only minimal impact on pavement performance. However, this
analysis was again performed in terms of pavement age rather than accumulated traffic, and the
data for the PRF and SAMI sections was not comprehensive. As such, it was felt that definitive
conclusions could not be drawn from this investigation.
The performance of the RAC overlays was evaluated against DGAC overlays in terms of SAI, IRI
and PCI. The analysis results indicated that the RAC overlay had a lower structural capacity, which
was expected as RAC overlays are typically thinner than the DGAC overlays. Statistical analysis
indicated that there was no significant difference between the two overlay types in terms of PCI and
IRI. The results of this analysis were, however, considered to be inconclusive.
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Project Background
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Overall, at the completion of the Phase I project, it was concluded that due to the absence of reliable
traffic data and the unbalanced distribution of test sections across environmental zones and districts,
further study and analysis was required in order to produce more meaningful results.
1.2 RECOMMENDATIONS OF PHASE I PROJECT
The Phase I final report2 concluded that two main issues limited the completeness of the analysis in
this project: the absence of traffic count data and the unbalanced distribution of test sections among
districts and environmental zones.
As such, it was recommended that a traffic study should be performed through which the traffic
loading at the test sections could be estimated. This would allow a more accurate assessment of
how well a particular treatment has performed given the traffic loading that it has been subjected to
during its service life.
It was also recommended that an additional 350- 400 ( minimum) test sections should be added to the
project in order to have a dataset that provides sufficient data for all variables. It was advised that a
representative number of test sections be selected from the Bay Area ( BA) environmental zone and
from District 4, as these areas were not included in the Phase I project.
1.3 OBJECTIVES OF PHASE II PROJECT
Phase II was initiated with the purpose of expanding the investigations and analysis conducted in
Phase I. The overall goal remained the same: to perform a comprehensive evaluation of in- situ
pavement performance across the state of California, and therefore assess the success of Caltrans’
design procedures. A number of tasks that would help achieve this goal were included in the Phase
II scope. Overall, the main goals of Phase II can be summarized as follows:
1. Select and test additional test sections to complement the Phase I sections
At the conclusion of Phase I, it was felt that the selected test sections did not give the
coverage needed to properly evaluate the performance of certain treatments. In addition, the
Bay Area environmental zone and District 4 had not been represented at all in the project. As
a result, Phase II sought to enhance the dataset by adding approximately 500 test sections
to the project. This included sections in the Bay Area and District 4, and additional sections
for treatments that had been under- represented in the first phase. This initiative was referred
to as the Phase II Main Study.
2. Ensure compatibility between the Phase I and Phase II data
Between the two phases, data would be collected from around 1,500 test sections over a
period of more than five years. Data from both phases was intended to form one complete
dataset and be used to achieve the same overall goal. Therefore, it was important that all the
collected data would be compatible. This was achieved through similar data collection
2 Stantec Consulting. ‘ Caltrans Pavement Performance Evaluation Services - Contract 65A0069 - Final Report’.
November 2002
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Project Background
December 23, 2008
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procedures being implemented in both phases and extensive QA checks being performed on
all data.
As it was possible that different FWD equipment would be used in Phase II to collect
deflection data, an FWD correlation study was added to the project scope. Through this
study, models would be developed that would account for any difference in collected
deflection data that was attributable to use of different FWD equipment.
3. Develop seasonal and temperature adjustment models to adjust FWD data for
seasonal and temperature variations
Pavement performance is highly influenced by environmental factors, most particularly by
temperature and moisture. Temperature and moisture conditions vary with time ( daily,
seasonal, and longer cycles), meaning that deflection testing can be performed at the same
pavement section, but yield very different results depending on the climatic conditions at the
time of the test. Performance of different pavement sections that have been tested at
different times of the day and year therefore cannot be meaningfully compared – differences
in measured deflections may be due to climatic conditions rather than to a difference in
structural performance. To allow fair comparison, adjustment models are required to account
for the environmental variations and to bring pavement response parameters measured at
different times of the day and year to the same standard conditions.
In this project’s two phases, tests were conducted not only in different years, but at different
times of the year and at different times of the day. In order to allow meaningful comparisons
between the FWD results collected under such different climatic conditions, and to therefore
fully meet the overall project goals, some adjustment of the collected data was necessary. As
such, a limited seasonal study was added to the scope of the Phase II project with the
intention of developing adjustment models based on California conditions.
4. Collect traffic data from Phase I and Phase II sections to enhance the pavement
performance analysis
Due to the lack of available traffic counts in the Caltrans Traffic Database, analysis in Phase
I was based on pavement age only. The limitation of this approach was that two pavement
sections of the same age may receive significantly different traffic loadings, and as truck
traffic is one of the key sources of damage to pavements, using only pavement age does not
allow a fair comparison of performance in such a case. This limited the validity of the
deterministic analysis approach and led to a probabilistic analysis approach being used in
Phase I.
As such, a traffic study was included in the Phase II project to collect limited time axle weight
data and utilize the existing Caltrans permanent weigh stations to estimate the accumulative
axle weights that passed over a project since the construction of the last rehabilitation
treatment.
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2.0 Main Study Data
2.1 TEST SECTIONS
During Phase I, a list of candidate sections was proposed for the Phase II project. This list
represented the starting point in the selection of test sections for the Phase II Main Study. The list
was extensively reviewed with respect to what had been achieved in Phase I and what was needed
in Phase II in order to select the most relevant sections.
Caltrans required that at least 200 sections from the QC/ QA and PMS lists be included in the Phase
II testing. As such, these lists were examined and, based on as- built documents, Phase I roughness,
distress, and FWD data, the sections were divided into three categories:
1. Sections that matched the Phase II test section requirements.
2. Sections that did not match the Phase II test section requirements exactly, but could
potentially be considered for testing.
3. Sections that could not be considered at all, due to the nature of the project, such as
bridge widening or interchange improvement, or due to safety concerns, such as very
high traffic volumes.
From the candidate sections identified during Phase I and the QC/ QA and PMS lists, a draft test
section list was compiled and these sections were surveyed using the RT3000. In this survey,
longitudinal profiles, left and right wheel path IRI measurements, and limited distress data were
recorded, and digital images were taken.
The results of the RT3000 survey, as well as checks on the validity of available IRI data, were used
to refine the list of test sections. The test sections that passed these checks underwent detailed field
testing. Once coring had been completed, its results were compared with the expected as- built
pavement structure. In cases where there were discrepancies, the list of sections was revised to
ensure that all the required rehabilitation treatments and pavement types were represented in the
final list of test sections.
After making all necessary refinements to the test section list in order to successfully meet the
project requirements, 537 sections were included in the Phase II Main Study. Appendix A gives
detailed information on the final 537 sections. As can be seen, test sections were located across 30
counties and in all six of the State’s environmental zones. Sections were selected in all but two of
Caltrans’ twelve Districts ( no sections were selected in Districts 7 and 12). While most of the
sections were Asphalt Concrete ( AC), more than 70 were Portland Cement Concrete ( PC) and a
further 13 were composite ( CO). The QC/ QA and PMS list supplied 220 of the sections and 17
sections from the Federal Highway Administration’s ( FHWA) LTPP program were also included.
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Main Study Data
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2.2 DATA COLLECTION
Since the objective of the Phase II Main Study is to supplement the Phase I project, the Phase II
data collection program was very similar to Phase I, and can be divided into three main categories –
office, field, and laboratory. Once collected, data was subjected to QC/ QA checks, processed, and
uploaded into the project database. The following subsections give an overview of the different types
of data collection performed in this project.
2.2.1 Office Data
As in Phase I, office data was collected from a variety of sources in Caltrans, such as the local
district offices and Caltrans’ headquarters in Sacramento. The collected data included:
􀂃 As built and construction data, such as:
- actual treatment
- layer type and thickness
- traffic loads
􀂃 Pavement design parameters, such as:
- design treatment
- design traffic
- layer type and thickness
All collected office and as- built information was loaded into the project database.
2.2.2 Field Data
The field data collection program used in Phase I was followed in Phase II, with only slight
modifications that were requested by Caltrans. These modifications included taking an additional
core outside the wheel path and performing field classifications of the subgrade soil. Each data
collection element is explained in the sections below.
2.2.2.1 RT3000 Survey
As mentioned above, an RT3000 survey was conducted on the list of potential test sections before
the final 537 were selected. In this survey, longitudinal profiles, left and right wheel path IRI, digital
images, and limited distresses were measured and recorded. IRI and rut depth data for the final
Phase II test sections was loaded into the project database; front and rear images, such as the
example shown in Figure 2- 1, were hyperlinked to the database.
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Main Study Data
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Figure 2- 1: Example Image File
2.2.2.2 Visual Distress Survey ( VDS) Data
In the distress surveys, the test sections were divided into 50- ft increments and the type, severity,
and extent of any of the pavement distresses presented in Table 2- 1 within each 50- ft section were
recorded. The distress data was then loaded into the project database.
Table 2- 1: Types of Collected Surface Distresses
Pavement Type Distress Type Severity levels Extent Units
Block Cracking
Low ‐ Crack width < 0.25”
Medium ‐ Crack width 0.25”‐ 0.75”
High ‐ Crack width > 0.75”
% area
Alligator Cracking
( wheel path)
Low ‐ Crack width < 0.25”
Medium ‐ Crack width 0.25”‐ 0.75”
High ‐ Crack width > 0.75”
% area
Alligator Cracking
( non‐ wheel path)
Low ‐ Crack width < 0.25”
Medium ‐ Crack width 0.25”‐ 0.75”
High ‐ Crack width > 0.75”
% area
Transverse cracking
Low ‐ Crack width < 0.25”
Medium ‐ Crack width 0.25”‐ 0.75”
High ‐ Crack width > 0.75”
Count
Longitudinal cracking
( wheel path)
Low ‐ Crack width < 0.25”
Medium ‐ Crack width 0.25”‐ 0.75”
High ‐ Crack width > 0.75”
Linear feet
Longitudinal cracking
( non‐ wheel path)
Low ‐ Crack width < 0.25”
Medium ‐ Crack width 0.25”‐ 0.75”
High ‐ Crack width > 0.75”
Linear feet
Flexible
Pavements
Rutting*
Low – rut depth < 0.50”
Medium ‐ rut depth 0.50”‐ 1.0”
High ‐ rut depth > 1.0”
Linear feet
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Main Study Data
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Pavement Type Distress Type Severity levels Extent Units
Raveling
Low – minor loss in fines
Medium – shallow disintegration
High – rough surface
% area
Bleeding
Low – visible coloring
Medium – visible free asphalt
High – Wet looking
% area
Longitudinal cracking
Low ‐ Crack width < 0.125”
Medium ‐ Crack width 0.125”‐ 0.50”
High ‐ Crack width > 0.50”
Linear feet
Transverse cracking
Low ‐ Crack width < 0.125”
Medium ‐ Crack width 0.125”‐ 0.50”
High ‐ Crack width > 0.50”
Count
Corner Cracking
Low ‐ Crack width < 0.125”
Medium ‐ Crack width 0.125”‐ 0.50”
High ‐ Crack width > 0.50”
% affected corners
Durability Cracking
Low ‐ Crack width < 0.125”
Medium ‐ Crack width 0.125”‐ 0.50”
High ‐ Crack width > 0.50”
% affected sides
Map Cracking % area
Pumping Count
Popouts
Low – voids < 0.25”
Medium – voids well defined
High – closely spaced voids
% area
Corner Spalling % area
Joint Spalling Count
Rigid
Pavements
Smashed slabs Count
* In the database, rutting is shown in the roughness table rather than the distress table.
2.2.2.3 Falling Weight Deflectometer ( FWD) Data
The FWD testing was carried out in 50- ft increments across the length of the section. Testing was
carried out in the right wheel path for flexible pavements. For rigid pavements, testing was carried
out in the right wheel path and at the center of the slab. Sensor offset distances from the center of
the load plate were as follows:
D1 D2 D3 D4 D5 D6 D7 D8 D9
0 in. 12 in. 18 in. 24 in. 36 in. 48 in. 60 in. 72 in. - 12 in.
The loading sequence consisted of one seating drop at 12,000 lbs followed by one drop at each of
three defined load levels. The load levels depended on the pavement type being tested, and were as
follows:
Flexible Pavement Level 1 Level 2 Level 3
7,000 lbs 9,000 lbs 12,000 lbs
Rigid Pavement Level 1 Level 2 Level 3
9,000 lbs 12,000 lbs 14,000 lbs
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Main Study Data
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All collected FWD data was loaded into the project database.
2.2.2.4 Core/ Bore Data
Cores/ bores were extracted at Station (-) 50ft from all sections to obtain in- situ pavement structural
information, such as material type and thickness of the pavement and base/ subbase condition. FWD
testing was also performed at the core location in order that the cores could be used to provide in-situ
layer thickness information necessary for backcalculation analysis. As a slight modification for
Phase II testing, two cores were extracted from each flexible pavement test section ( one within the
wheel path and one between wheel paths). Each core was assigned a unique core ID number and
details on the material type and thickness were recorded on a Core Log. Digital images were taken
to document each core and the cores themselves were sent for laboratory testing.
Data from the cores was uploaded into the project database. Core images, such as the example
shown in Figure 2- 2, are provided alongside the database and labeled with the corresponding
section number.
Figure 2- 2: Example Core Image File
2.2.2.5 Field Classified Subgrade Data
As a second modification to Phase II data collection, Caltrans requested the addition of subgrade
classification at each site. This was conducted according to the American Society for Testing &
Materials ( ASTM) Standard 2488 for field soil classification. However, the data did not pass the
rigorous QC/ QA tests employed in this project and as a result no subgrade classifications are
available in the project database.
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Main Study Data
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2.2.2.6 Site Characterization Data
In the site characterization task, data attributes, such as geometry ( curve, slope, tangent),
pavement, substructure, shoulder type and condition, and cut/ fill were collected using a detailed Site
Characterization Form. These attributes were uploaded into the site characterization table within the
project database.
2.2.3 Laboratory Data
As in Phase I, the following laboratory tests were performed on the samples taken from the AC top
layer cored from each test section:
􀂃 AC Extraction, as per American Association of State Highway and Transportation Officials
( AASHTO) T- 164 Standard
􀂃 Gradation, as per AASHTO T27- 97, ASTM C 136- 95a Standards
􀂃 Bulk specific gravity, as per AASHTO T166- 93 Standard
􀂃 Maximum theoretical specific gravity, as per AASHTO T 209- 94 Standard
􀂃 Air voids using the bulk specific gravity and the maximum theoretical specific gravity
Two cores were extracted from the flexible pavement test sections ( one within the wheel path and
one between wheel paths). The between- wheel- path core was used to determine the impact of traffic
on air voids, i. e. secondary compaction, as it is expected that the voids ratio will be different from
within the wheel path to between wheel paths. However, the aggregate gradation and binder content
of the AC mix are not expected to be significantly different between the two cores. Therefore, the
wheel- path core was subject to all laboratory tests, whereas only specific gravity was performed on
the between- wheel- path core. It should be noted that rigid pavement sections were not subject to
laboratory testing.
In addition to the tests mentioned above, those sections that were part of Study 1 – the Construction
Quality Evaluation Study – were subject to the following laboratory tests carried out on the aggregate
base:
􀂃 Moisture content
􀂃 Aggregate gradation
These tests were conducted on Study 1 sections regardless of pavement type, i. e. flexible,
composite, and rigid sections.
All collected laboratory data was uploaded into the project database.
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Main Study Data
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2.2.4 Database
The collected data ( field and office), the results of the laboratory tests and the results of the analyses
performed on the collected data were loaded to the project Access database. Appendix B shows a
list of the Access database tables and fields.
2.3 ENHANCEMENTS TO MAIN STUDY DATA
From the data collection initiatives, the database for Phase II Main Study test sections was
populated with office, field and laboratory data. However, a number of additional factors needed to
be considered in order to produce more meaningful results:
􀂃 The effects of climatic conditions on FWD test results
􀂃 The accumulated traffic loading that the test sections have been exposed to during their service
life
􀂃 Differences in FWD results related to use of different FWD equipment within the project
These factors were addressed in Phase II through the Seasonal Study, the Traffic Study, and the
FWD Correlation Study. Details on each of these studies, including how their results were
implemented to enhance the Main Study data, are included in the next three sections of the report.
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3.0 Seasonal Study
Pavement performance is highly influenced by environmental factors, most particularly by
temperature and moisture. Since temperature and moisture conditions vary with time ( daily,
seasonal, and longer cycles) their effects should be accounted for when comparing the performance
of different pavement sections that were tested under different environmental conditions. In such
cases, the use of adjustment models is required to account for the environmental variations and to
bring pavement response parameters measured at different times of the day and year to the same
standard conditions.
In Phase I of this project, FWD data was collected from approximately 1,000 test sections. For the
Phase II Main Study, it was collected from over 500 more. These 1,500+ sections were located
across the state of California in different environmental zones, and FWD tests were conducted not
only in different years, but at different times of the year and at different times of the day. In order to
allow meaningful comparisons between the FWD results collected under such different climatic
conditions, some adjustment of the collected data was necessary. As such, a limited seasonal study
was added to the scope of the Phase II project.
The main objective of the Phase II Seasonal Study was to develop adjustment models based on
California conditions that could be used to account for the variation in environmental factors during
the FWD tests performed on Phase I and II sections. This would allow meaningful comparison of the
FWD test results of the 1,500+ test sections – a necessary step to achieve the overall project goals.
3.1 FIELD TESTING
Two different kinds of FWD field testing were conducted within the Seasonal Study: monthly testing
to monitor the seasonal changes ( month to month); and 24- hour testing cycles ( sections tested
every 2 hours for a 24- hour period) to monitor short- term variability ( mainly temperature variability).
In addition to FWD testing, cores/ bores were extracted from each test section during the first testing
cycle to provide layer thickness information necessary for backcalculation analysis.
3.1.1 Test Sections
During the Phase I project, the State was divided into the following six environmental zones3:
􀂃 Bay Area ( BA)
􀂃 Central Valley ( CV)
􀂃 Desert ( DS)
􀂃 Mountain ( MT)
3 Harvey, J., Chong, A., Roesler, J. Climate Regions for Mechanistic- Empirical Pavement Design in California and Expected Effects
on Performance. Draft report prepared for California Department of Transportation. Publication UCPRC- RR- 2000- 07. Pavement
Research Center, CAL/ APT Program, Institute of Transportation Studies, University of California, Berkeley, 2000.
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􀂃 North Coast ( NC)
􀂃 South Coast ( SC)
Seasonal Study test sections were selected from within these six zones. Since traffic control
represents a major cost item in California, test sections were selected from areas that required less
traffic control, such as rest areas and weigh stations. The final list of sections tested in the Seasonal
Study is shown in Table 3- 1. As can be seen, a total of 11 flexible ( asphalt concrete ( AC)) and 7 rigid
( Portland Cement Concrete ( PC)) were included in the study. The three sections highlighted in the
table were also used for the 24- hour testing cycles.
The flexible pavement sections had AC layer thicknesses ranging from 3.5 to 7 in., with base layers
ranging from 2 to 15 in. The rigid pavement sections had PC layers ranging in thickness from 9 to 13
in., with base layers ranging from 3.5 to 10 in. A range of base / subbase and subgrade materials
were represented in the chosen test sections.
3.1.2 FWD Testing Protocols
For flexible pavements, the FWD testing was conducted along the right wheel path and between the
wheel paths. A minimum of 11 test points were tested per path for each test section. For rigid
pavements, at least three slabs were tested per section. Three paths were tested at each slab:
􀂃 Pavement Edge ( closest to shoulder)
􀂃 Right Wheel Path ( 3 ft from lane/ shoulder joint)
􀂃 Between Wheel Path ( 6 ft from lane/ shoulder joint)
Each slab was tested at mid- slab ( 5 ft from nearest joint or transverse crack) and at the approach
and leave sides of the following joint/ crack.
Testing consisted of a seating drop and one drop at each of three load levels. Sensor offset distance
from the center of the load plate was as follows:
D1 D2 D3 D4 D5 D6 D7 D8 D9
0 in. 8 in. 12 in. 18 in. 24 in. 36 in. 48 in. 60 in. - 12 in.
Pavement temperature measurements 0.5” from the surface, at mid- depth, and 0.5” from the bottom
were taken at the beginning and end of testing at each section. Air temperature was continuously
monitored throughout all tests.
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Table 3- 1: Seasonal Study Test Sections
AC Pavement Layers PC Pavement Layers
Name Site ID Type Route Dir. MP
Env.
Zone
AC
( in.)
Base
( in.)
Subbase
( in.) Base/ Subbase
PC
( in.)
Base
( in.) Base Subgrade
Alliso Creek ALISO_ S Rest Area 5 S 59 SC 5 2 12 Gravel / Sand Soft Clay
Antelope ANT_ E Weigh Station 80 E 16 CV 4.5 4 5
CTB / Sandy
Gravel
Silty Sand w
Gravel
Antelope ANT_ E Weigh Station 80 E 16 CV 9 5 Sandy Gravel
Silty Sand w
Gravel
Antelope ANT_ W Weigh Station 80 W 16 CV 9.5 4
Cement
Treated Base
Silty Sand w
Gravel up to 24"
Buckhorn BUCK_ W Weigh Station 299 W 7.4 NC 6.5 15 0
Sandy Gravel &
Cobble
Silty Sand
Buckhorn BUCK_ W Weigh Station 299 W 7.4 NC 13 10
Sandy Gravel &
Cobble
Silty Sand
Camino CAM_ W Weigh Station 50 W 27.1 MT 3.5 7.5 0 Sandy Gravel Clay
Camino CAM_ W Weigh Station 50 W 27.1 MT 13 10
Sandy Gravel &
Cobble
Silty Sand
Cordelia CORD_ W Weigh Station 80 W 14.5 BA 11 3.5
Cement
Treated Base
Silty Sand w
Gravel
Desert Hill DES_ W Weigh Station 10 W 15.8 DS 6.5 14 Sandy Gravel Sandy Gravel
Dunnigan DUN_ N Rest Area 5 N 26.3 CV 4 9 0
Sandy Gravel /
Gravel
Silty Sand w
Gravel
Gold Run GOLD_ W Rest Area 80 W 41 MT 5.5 5 Gravelly Sand Silty Clay
Irvine IRV_ N Rest Area 101 N 61.82 NC 4 7 0 Clean Gravel Sandy Gravel
Nimitz NIM_ S Weigh Station 880 S 3.7 BA 10 6.25 Silty Gravel Clay
Peralta PER_ E Weigh Station 91 E 13.8 SC 9.5 5
Cement
Treated Base
Sandy Gravel
Trinidad TRIN_ N Rest Area 101 N 70 NC 7 4.75 0 Sandy Gravel Silty Sand
Whitewater WHITE_ E Rest Area 10 E 26 DS 4 2 17
Gravel ( open
graded)
Silty Clay
Whitewater WHITE_ W Rest Area 10 W 26 DS 4.5 2 20
Gravel / Sand
with Gravel
Silty Clay
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3.1.3 Testing Frequency
Regular Seasonal Study test sections were tested approximately once a month for one year, using
the above protocols. The 24- hour test sections were each tested every 2 hours for a 24- hour period
to focus on the effect of short- term, mainly temperature, variations. Appendix C shows the dates of
testing carried out at regular ( non- 24- hour) test sections.
3.2 DEVELOPMENT OF TEMPERATURE ADJUSTMENT MODELS – FLEXIBLE
PAVEMENTS
3.2.1 Model Development
There are a number of factors that could possibly need to be accounted for in the development of
temperature adjustment models for deflection data from flexible pavements. These include
pavement surface temperature, sensor location, AC layer thickness, and environmental zone. It is
not simply the factors themselves that may need to be considered, but also interaction between any
one or more of these factors. Therefore the first step in the development of the models for flexible
pavements was to perform Analysis of Variance ( ANOVA) to determine which factors, or ‘ main
effects’, and two- way interactions between main effects, had significant effect on the deflection data
and therefore needed to be addressed in the model.
The deflections measured from all 11 flexible test sites were considered. ANOVA was performed for
each sensor individually ( D1- D9) to examine the significance of main effects and two- way
interactions on the measured deflections at that sensor. The main effects examined were pavement
surface temperature, AC thickness, and environmental zone. The environmental zones were
represented by the codes shown in Table 3- 2.
Table 3- 2: Environmental Zone Codes
Env. Zone Code
Bay Area 31*
Central Valley 32
Desert 33
Mountain 34
North Coast 35
South Coast 36
* Because no flexible test sections from the BA zone were included in the
Seasonal Study, a code of 35 ( North Coast) is suggested to be used for Bay
Area flexible pavement sections because of the similarity in climatic conditions.
Table 3- 3 shows a summary of the significant and non- significant main effects and two- way
interactions for each sensor. Aside from the noted exception, all results are based on a 95%
confidence level and 3333 degrees of freedom.
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Table 3- 3: ANOVA Testing – Summary of Results
Env. Zone
AC
Thickness
Surface
Temperature
Env. Zone & AC
Thickness
Env. Zone &
Temp
AC Thickness
& Temp
D1 S S S S S NS
D2 NS S S S S NS
D3 NS S S S S S
D4 NS S* S NS S NS
D5 S S S S S S
D6 S S NS S S S
D7 S S NS S NS S
D8 S S NS S NS S
D9 NS S S S S NS
* 94% confidence level used instead based on practical engineering judgment.
As can be seen, all parameters were found to have a significant impact on all sensors ( D1 – D9),
either as main effects and/ or as part of a two- way interaction. For example, environmental zone has
a significant impact as a main effect on D1. This is not the case for D2 – environmental zone has no
significant impact as a main effect. However, environmental zone does have significant impact on
D2 in two- way interactions with asphalt thickness and also with pavement surface temperature.
Taking each sensor individually ( D1 to D9), the non- significant main effects and two- way interactions
were removed and multi- regression analysis was performed to develop temperature adjustment
models for the deflections measured by each sensor as function of significant main effects and two-way
interactions. The general form of the model is:
Yi = mij * Xj [ 3- 1]
Where,
Yi = D1 to D9
Xj = Surface Temperature ( T), AC Thickness ( AC), Environmental Zone ( EZ),
Surface Temperature * AC Thickness ( T* AC), Surface Temperature * Environmental
Zone ( T* EZ), and AC Thickness * Environmental Zone ( AC* EZ), respectively
mij = regression coefficient
Table 3- 4 shows the model coefficients for D1 to D9 for flexible pavements. The table also shows
the coefficient of determination ( R2) and degrees of freedom ( DF) for each model.
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Table 3- 4: Coefficients for Temperature Adjustment Models – Flexible Pavement
Env.
Zone
AC
Thickness
Surface
Temperature
Env. Zone &
AC Thickness
Env. Zone
& Temp
AC Thickness
& Temp DF R2
D1 0.704 ‐ 5.879 ‐ 1.020 0.085 0.032 3335 85%
D2 8.853 ‐ 1.178 ‐ 0.245 0.037 3336 78%
D3 1.670 0.081 ‐ 0.023 0.002 ‐ 0.021 3335 73%
D4 0.575 ‐ 0.374 0.012 3337 71%
D5 0.039 0.180 ‐ 0.167 0.010 0.006 ‐ 0.006 3334 74%
D6 ‐ 0.008 ‐ 1.216 0.053 0.001 ‐ 0.007 3335 75%
D7 0.042 ‐ 1.260 0.040 ‐ 0.001 3336 76%
D8 0.035 ‐ 0.973 0.030 ‐ 0.001 3336 78%
D9 8.036 ‐ 1.129 ‐ 0.222 0.036 3336 80%
These models were based on the data available in the Seasonal Study database, and are valid only
for the range of parameters that are present in that dataset. The range of validity for each model, by
individual parameter, is shown in Table 3- 5. It should be noted that for the purposes of this particular
study, deflections were measured at pavement surface temperatures higher than 120° F, but that it is
not usually recommended that FWD tests be performed at temperatures higher than 120° F.
Table 3- 5: Ranges of Validity for Temperature Adjustment Models – Flexible Pavement
Env. Zone ( No.) AC Thickness ( in.) Surface Temperature (° F)
Min Max Min Max Min Max
32 36 2.5 7.5 42.4 147.1
3.2.2 Application of Models
The developed models estimate the deflection at different sensor locations as a function of
pavement surface temperature, AC layer thickness, and environmental zone, as well as different
two- way combinations of these parameters. However, it should be noted that these models are
mainly concerned with the impact of temperature on the measured deflections and that no material
properties are contained in the models, i. e. they are not structural models. The models were not
developed with the intention of predicting measured deflections and should not under any
circumstances be used for this purpose. The models are intended to be used to bring measured
deflections recorded at different temperatures to the same standard temperature.
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The steps that should be followed to apply these models are illustrated in Figure 3- 1, and can be
summarized as follows:
􀂃 Estimate the deflections at different sensors using the appropriate model and the actual
pavement surface temperature during testing ( Dip).
􀂃 Estimate the deflections at different sensors using the appropriate model and the standard
pavement surface temperature, i. e. 68° F ( Dis).
􀂃 Determine the required deflection adjustment ( ΔD) due to the difference between the actual
pavement surface temperature during testing and the standard pavement surface temperature,
as ΔD = Dis - Dip.
􀂃 Calculate the temperature adjusted deflection by applying ΔD to the actual measured deflection,
as Adjusted Deflection = Measured Deflection + ΔD
0
2
4
6
8
10
12
14
16
18
0 20 40 60 80 100 120
Deflection ( mils)
Temperature (° F)
Standard Temp Measured Temp.
Delta (‐ ve)
Adjusted Deflection = Measured Deflection + Delta
Measured Temp.
Delta (+ ve)
Figure 3- 1: Implementation of Models
As a reasonableness check, the models were then applied to each of the measured deflections
recorded within the Seasonal Study. As per the steps described above, the appropriate model ( D1 to
D9) was applied to each record in the database to estimate the deflection using the actual pavement
surface temperature at the time of testing as an input. The process was then repeated but using a
standard temperature ( 68° F) as an input, i. e. to estimate the deflection at the standard temperature.
ΔD was then calculated as the difference in value between the estimated deflection at the actual
measured temperature and the estimated deflection at the standard temperature. The actual
measured deflection was then adjusted using ΔD, resulting in a temperature adjusted deflection.
3.2.2.1 Sample Application
Figure 3- 2 shows an example implementation of the developed models. In this example, the
recorded surface temperature during the testing was 96.3° F, AC thickness was 2.5 in. and
environmental zone was South Coast ( 36). The actual measured deflection basin is represented by
the blue line. The appropriate model was implemented for each deflection ( D1- D8), resulting in the
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adjusted deflection basin ( green line). For example, the actual measured D1 @ 96.3° F was 23.3
mils. The D1 model was used first to estimate the deflection @ 96.3° F as follows:
D1 = mij * Xj [ 3- 2]
or,
D1 = 0.704 * Env. Zone – 5.879 * AC Thick – 1.020 * Temp + 0.085 * Env. Zone * AC Thick
+ 0.032 * Env. Zone * Temp
or,
D1 = 0.704 * 36 – 5.879 * 2.5 – 1.020 * 96.3 + 0.085 * 36 * 2.5 + 0.032 * 36 * 96.3
D1 = 31.25 mils
The process was then repeated to estimate the deflection @ 68° F:
D1 = 0.704 * Env. Zone – 5.879 * AC Thick – 1.020 * Temp + 0.085 * Env. Zone * AC Thick
+ 0.032 * Env. Zone * Temp
or,
D1 = 0.704 * 36 – 5.879 * 2.5 – 1.020 * 68 + 0.085 * 36 * 2.5 + 0.032 * 36 * 68
D1 = 27.45 mils
The difference between these two deflections, ΔD1, is equal to - 3.8 mils. As a result, the adjusted
D1 would be 23.3 – 3.8 = 19.5 mils. The same steps were then followed for each of the measured
deflections using the appropriate model.
25
20
15
10
5
0
1 2 3 4 5 6 7 8
Deflection ( mils)
Sensor Number
Measured Adjusted
Figure 3- 2: Example Flexible Model Implementation 96.3° F
Figure 3- 3 illustrates another example implementation of the developed models. In this example, the
recorded surface temperature during the testing was 60.0° F. Again, the actual measured deflection
basin is represented by the blue line. The appropriate model was implemented for each deflection
( D1- D8), resulting in the adjusted deflection basin ( green line). For example, the measured D1 @
60.0° F was 9.22 mils. The D1 model was used first to estimate the deflection @ 60.0° F ( 11.95 mils)
and then the deflection @ 68° F ( 12.77 mils). The difference between these two deflections, Δ D1, is
equal to + 0.82 mils. As a result, the adjusted D1 would be 9.22 + 0.82 = 10.04 mils. The same steps
were then followed for each of the measured deflections using the appropriate model.
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12
10
8
6
4
2
0
1 2 3 4 5 6 7 8
Deflection ( mils)
Sensor Number
Measured Adjusted
Figure 3- 3: Example Flexible Model Implementation 60.0° F
Figure 3- 4 shows a final example implementation of the developed models. In this example, the
recorded surface temperature during the testing was 71.2° F. The actual measured deflection basin
is once again represented by the blue line. The appropriate model was implemented for each
deflection ( D1- D8), resulting in the adjusted deflection basin ( green line). For example, the
measured D1 @ 71.2° F was 26.41 mils. The D1 model was used first to estimate the deflection @
71.2° F ( 20.85 mils) and then the deflection @ 68° F ( 20.42 mils). The difference between these two
deflections, ΔD1, is equal to - 0.43 mils. As a result, the adjusted D1 would be 26.41 - 0.43 = 25.98
mils. The same steps were then followed for each of the measured deflections using the appropriate
model.
30
25
20
15
10
5
0
1 2 3 4 5 6 7 8
Deflection ( mils)
Sensor Number
Measured Adjusted
Figure 3- 4: Example Flexible Model Implementation 71.2° F
As can seen from these three examples, when the temperature during the testing was much higher
than the standard temperature ( 96.3° F compared to 68° F), ΔD1 was - 3.8 mils (- 14.4% of D1), i. e. D1
was reduced by ~ 15% to account for the 28.3° F difference between the temperature during testing
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and the standard temperature. The corresponding ΔD1 in the second and third examples, where the
temperature differences from the standard temperature are - 8° F and + 3.2° F, are 8.9% and - 1.6% of
the measured D1s, respectively.
3.3 DEVELOPMENT OF TEMPERATURE ADJUSTMENT MODELS – RIGID
PAVEMENTS
3.3.1 Model Development
A very similar approach was used in the development of temperature adjustment models for rigid
pavements. However, because of the nature of the differences between flexible and rigid
pavements, a number of additional considerations were necessary. Firstly, rigid pavement models
were developed not only for each sensor ( D1- D9), but also for each testing location on the slab ( i. e.
mid- slab, approach side of joint/ crack, leave side of joint/ crack), resulting in three models for each
sensor. Secondly, due to the additional factors that may affect the response of rigid pavement to
temperature, additional main effects were considered in the development of the rigid pavement
models. The main effects considered were:
􀂃 Pavement surface temperature
􀂃 Air temperature gradient ( change in air temperature between current test and the test
conducted immediately prior to it)
- “ 1” if air temperature is increasing
- “ 0” if air temperature is constant
- “- 1” if air temperature is decreasing
􀂃 Environmental zone, as shown in Table 3- 6
􀂃 Test path
- Between wheel paths = “ 1”
- Edge = “ 2”
- Right wheel path = “ 3”
􀂃 PC Slab Thickness
􀂃 Base Course Thickness
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Table 3- 6: Environmental Zone Codes
Env. Zone Code
Bay Area 31
Central Valley 32
Desert 33*
Mountain 34
North Coast 35
South Coast 36
* As no rigid sections in the DS zone were included in the study, it is
recommended that Zone 33 not be used in the rigid pavement models.
In the same way as described for flexible pavements, ANOVA was performed on the main effects
and some two- way interactions to identify those having significant impact on the deflections
measured at the different sensors and different testing locations. Tables 3- 7 to 3- 9 present the
results of the ANOVA analysis and show which main effects and two- way interactions are
considered significant ( S) or not significant ( NS) for each sensor when testing is performed at the
mid- slab, joint approach, and joint leave test locations, respectively. Other than a small number of
noted exceptions, all results are based on a 95% confidence level. Degrees of freedom were 1448
for mid- slab, 1437 for joint approach, and 1366 for joint leave. The tables use the following
abbreviations:
􀂃 Pavement surface temperature = T
􀂃 Air temperature gradient = G
􀂃 Environmental zone = EZ
􀂃 Test path = P
􀂃 PC Slab Thickness = PC
􀂃 Base Course Thickness = B
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Table 3- 7: ANOVA Testing – Summary of Results for Mid- Slab
T G EZ P PC B T* G T* EZ T* P T* PC G* PC G* P EZ* PC EZ* P PC* P PC* B
D1 NS NS S NS S S NS S NS S NS NS S NS NS S
D2 NS S S NS S S NS S NS S NS NS S NS NS S
D3 NS S S NS S S NS S NS S NS NS S NS NS S
D4 NS S S NS S S NS S NS S NS NS S NS NS S
D5 NS S S NS S S NS S NS S S NS S NS NS S
D6 NS S S NS S S S S NS S S NS S NS NS S
D7 NS S S NS S S S S NS S S NS S S NS S
D8 NS S NS S NS S S S NS S NS NS NS S NS S
D9 NS NS S NS S S NS S NS S NS NS S NS NS S
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Table 3- 8: ANOVA Testing – Summary of Results for Joint Approach
T G EZ P PC B T* G T* EZ T* P T* PC G* PC G* P EZ* PC EZ* P PC* P PC* B
D1 S NS S S S S S* NS NS NS NS NS S S NS S
D2 S NS S NS S S NS S NS S NS NS S S* NS S
D3 S NS S NS S S NS S NS S NS NS S S NS S
D4 S NS S NS S S NS S NS S NS NS S S NS S
D5 S NS NS NS NS S NS S NS S NS NS NS NS NS S
D6 S NS NS NS NS NS NS S NS S NS NS NS NS NS NS
D7 S NS NS NS NS NS NS S NS S NS NS NS S NS NS
D8 S NS NS NS S NS NS S NS S NS NS S S NS NS
D9 S S S S S S NS S NS NS NS NS S S NS S
* 94% confidence level used instead based on practical engineering judgment.
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Table 3- 9: ANOVA Testing – Summary of Results for Joint Leave
T G EZ P PC B T* G T* EZ T* P T* PC G* PC G* P EZ* PC EZ* P PC* P PC* B
D1 S* NS NS NS NS S NS NS NS NS NS NS NS S* NS S
D2 S NS NS NS NS S NS S NS NS NS NS NS NS NS S
D3 S NS NS NS NS S NS S NS NS NS NS NS NS NS S
D4 S S NS NS NS NS NS S NS NS NS NS NS NS NS NS
D5 S S NS NS NS NS NS S NS NS NS NS NS NS NS NS
D6 S S NS NS S NS NS S NS NS NS NS NS NS NS NS
D7 S S NS NS S NS NS S NS NS NS NS S NS NS NS
D8 S S S NS S S NS S NS NS NS NS S S NS S
D9 S NS S NS S S NS S NS S NS NS S S NS S
* 94% confidence level used instead based on practical engineering judgment.
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Taking each sensor ( D1 to D9) and each testing location individually, the non- significant main effects
and two- way interactions were removed and multi- regression analysis was performed to develop
temperature adjustment models for the deflections measured by each sensor as a function of
significant main effects and two- way interactions. Similarly to flexible pavements, the general form of
the model is:
Yi = mij * Xj
Where,
Yi = D1 to D9
Xj = Surface Temperature ( T), Temperature Gradient ( G), Environmental Zone ( EZ),
Test Path ( P), PC Slab Thickness ( PC), Base Course Thickness ( B), Surface
Temperature * Temperature Gradient ( T* G), Surface Temperature * Environmental
Zone ( T* EZ), Surface Temperature * Test Path ( T* P), Surface Temperature * PC
Slab Thickness ( T* PC), Temperature Gradient * PC Slab Thickness ( G* PC),
Temperature Gradient * Test Path ( G* P), Environmental Zone * PC Slab Thickness
( EZ* PC), Environmental Zone * Test Path ( EZ* P), PC Slab Thickness * Test Path
( PC* P), PC Slab Thickness * Base Course Thickness ( PC* B), respectively
mij = regression coefficient
Tables 3- 10 to 3- 12 show the model coefficients for D1 to D9 for mid- slab, joint approach, and joint
leave testing locations, respectively. The tables also show the coefficient of determination ( R2) and
degrees of freedom ( DF) for each model.
These models were based on the data available in the Seasonal Study database, and are valid only
for the range of parameters that are present in that dataset. The range of validity for each set of
models, by individual parameter, is shown in Table 3- 13. It should be noted that although deflections
were measured at high pavement surface temperatures, it is not recommended to perform FWD
tests at temperatures higher than 80° F to avoid artificially high load transfer efficiencies.
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Table 3- 10: Coefficients for Temperature Adjustment Models – Rigid Pavement at Mid- Slab
T G EZ P PC B T* G T* EZ T* P T* PC G* PC G* P EZ* PC EZ* P PC* P PC* B DF R2
D1 ‐ 0.834 0.828 3.456 0.002 ‐ 0.005 0.065 ‐ 0.325 1458 91%
D2 ‐ 0.075 ‐ 0.789 0.825 3.235 0.002 ‐ 0.005 0.060 ‐ 0.303 1457 90%
D3 ‐ 0.075 ‐ 0.744 0.821 3.037 0.002 ‐ 0.005 0.055 ‐ 0.284 1457 90%
D4 ‐ 0.075 ‐ 0.684 0.816 2.778 0.002 ‐ 0.005 0.049 ‐ 0.259 1457 89%
D5 ‐ 0.508 ‐ 0.555 0.790 2.245 0.002 ‐ 0.005 0.043 0.037 ‐ 0.208 1456 89%
D6 ‐ 0.818 ‐ 0.405 0.739 1.656 0.003 0.001 ‐ 0.004 0.048 0.023 ‐ 0.152 1455 88%
D7 ‐ 0.714 ‐ 0.213 0.730 0.904 0.003 0.001 ‐ 0.004 0.044 0.003 0.001 ‐ 0.077 1454 86%
D8 ‐ 0.145 1.814 0.356 0.001
‐
0.0002
0.001 ‐ 0.050 ‐ 0.023 1457 83%
D9 ‐ 0.759 0.882 3.110 0.002 ‐ 0.005 0.055 ‐ 0.291 1458 90%
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Table 3- 11: Coefficients for Temperature Adjustment Models – Rigid Pavement at Joint Approach
T G EZ P PC B T* G T* EZ T* P T* PC G* PC G* P EZ* PC EZ* P PC* P PC* B DF R2
D1 ‐ 0.026 ‐ 0.544 3.255 0.640 3.195 ‐ 0.001 0.058 ‐ 0.090 ‐ 0.309 1445 89%
D2 ‐ 0.179 ‐ 0.076 1.653 1.208 0.005 0.005 ‐ 0.038 0.002 ‐ 0.114 1445 91%
D3 ‐ 0.161 ‐ 0.054 1.526 1.044 0.004 0.005 ‐ 0.036 0.002 ‐ 0.100 1445 92%
D4 ‐ 0.141 ‐ 0.033 1.401 0.884 0.004 0.004 ‐ 0.034 0.001 ‐ 0.086 1445 92%
D5 0.015 0.725 ‐ 0.001 0.006 ‐ 0.066 1449 90%
D6 0.080 ‐ 0.001 ‐ 0.001 1451 88%
D7 0.071 ‐ 0.001 ‐ 0.001 0.002 1450 89%
D8 ‐ 0.028 0.802 0.001 ‐ 0.002 ‐ 0.022 0.001 1448 92%
D9 ‐ 0.071 ‐ 0.111 ‐ 0.447 2.652 0.940 2.552 0.002 0.033 ‐ 0.074 ‐ 0.244 1444 89%
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Table 3- 12: Coefficients for Temperature Adjustment Models – Rigid Pavement at Joint Leave
T G EZ P PC B T* G T* EZ T* P T* PC G* PC G* P EZ* PC EZ* P PC* P PC* B DF R2
D1 0.048 0.338 0.005 0.006 1379 77%
D2 0.260 0.259 ‐ 0.007 0.014 1379 80%
D3 0.233 0.224 ‐ 0.006 0.012 1379 80%
D4 0.172 ‐ 0.264 ‐ 0.004 1380 76%
D5 0.137 ‐ 0.195 ‐ 0.003 1380 77%
D6 0.072 ‐ 0.089 0.266 ‐ 0.002 1379 85%
D7 ‐ 0.105 ‐ 0.065 1.561 0.003 ‐ 0.041 1378 85%
D8 ‐ 0.070 ‐ 0.047 0.063 1.222 ‐ 0.067 0.002 ‐ 0.039 0.001 0.013 1374 87%
D9 ‐ 0.163 0.035 1.376 1.229 0.003 0.009 ‐ 0.037 0.001 ‐ 0.123 1374 89%
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Table 3- 13: Ranges of Validity for Temperature Adjustment Models – Rigid Pavement
Surface Temp.
(° F)
Temperature
Gradient ( No.)
Environmental
Zone ( No.)*
Testing Path
( No.)
PC Slab
Thickness ( in.)
Base
Thickness ( in.)
Min Max Min Max Min Max Min Max Min Max Min Max
Mid‐ Slab
Models
43.6 139.1 ‐ 1 1 31 36 1 3 9 13 3.5 14
Joint
Approach
Models
44.1 139.2 ‐ 1 1 31 36 1 3 9 13 3.5 14
Joint
Leave
Models
44.2 133.7 ‐ 1 1 31 36 1 3 9 13 3.5 14
* Excluding Zone 33
3.3.2 Application of Models
As with the flexible pavement models, these models were not developed with the intention of
predicting measured deflections and should not under any circumstances be used for this purpose.
The models are intended to be used to bring measured deflections recorded at different
temperatures to the same standard temperature. The steps that should be followed to use the rigid
pavement models can be summarized as follows:
􀂃 Estimate the deflections at different sensors using the appropriate model ( mid- slab, joint leave
or joint approach) and the actual pavement surface temperature during testing ( Dip).
􀂃 Estimate the deflections at different sensors using the appropriate model ( mid- slab, joint leave
or joint approach) and the standard pavement surface temperature, i. e. 68° F ( Dis).
􀂃 Determine the required deflection adjustment ( ΔD) due to the difference between the actual
pavement surface temperature during testing and the standard pavement surface temperature,
as ΔD = Dis - Dip.
􀂃 Calculate the temperature adjusted deflection by applying ΔD to the actual measured deflection,
as Adjusted Deflection = Measured Deflection + ΔD.
To check the reasonableness of the models, they were applied to the deflections measured within
the Seasonal Study. To do this, the models were applied to each record in the database to estimate
the deflection using the actual temperature at the time of testing as an input. The process was then
repeated but using a standard temperature ( 68° F) as an input, i. e. to estimate the deflection at the
standard temperature. ΔD was then calculated as the difference in value between the estimated
deflection at the actual measured temperature and the estimated deflection at the standard
temperature. The actual measured deflection was then adjusted using ΔD, resulting in a temperature
adjusted deflection.
3.3.2.1 Sample Application
Figure 3- 5 shows an example implementation of the developed models. In this example, a mid- slab
deflection basin was used. The recorded surface temperature during the testing was 76.0° F. The
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Seasonal Study
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actual measured deflections ( D1 to D4) are represented by the blue line. Only D1 to D4 deflections
are shown in this figure, since these deflections are the only ones used in backcalculation analysis
performed on mid- slab testing. The appropriate model was implemented for each deflection ( D1- D4),
resulting in the adjusted deflections represented by the green line. For example, the actual
measured mid- slab D1 @ 76.0° F was 4.11 mils. The D1 model was used first to estimate the
deflection @ 76.0° F as follows:
D1 = mij * Xj
or,
D1 = - 0.834 * EZ + 0.828 * PC + 3.456 * B + 0.002 * T * EZ – 0.005 * T * PC + 0.065 * EZ *
PC – 0.325 * PC * B
or,
D1 = - 0.834 * 36 + 0.828 * 9.5 + 3.456 * 5 + 0.002 * 76.0 * 36 – 0.005 * 76.0 * 9.5 + 0.065 *
36 * 9.5 – 0.325 * 9.5 * 5
D1 = 3.64 mils
The process was then repeated to estimate the deflection @ 68° F:
D1 = - 0.834 * EZ + 0.828 * PC + 3.456 * B + 0.002 * T * EZ – 0.005 * T * PC + 0.065 * EZ *
PC – 0.325 * PC * B
or,
D1 = - 0.834 * 36 + 0.828 * 9.5 + 3.456 * 5 + 0.002 * 68.0 * 36 – 0.005 * 68.0 * 9.5 + 0.065 *
36 * 9.5 – 0.325 * 9.5 * 5
D1 = 3.46 mils
The difference between these two deflections, ΔD1, is equal to - 0.18 mils (- 4% of measured D1). As
a result, the adjusted D1 would be 4.11 – 0.18 = 3.93 mils. The same steps were then followed for
each of the measured deflections using the appropriate model.
5
4
3
2
1
0
1 2 3 4
Deflection ( mils)
Sensor Number
Measured Adjusted
Figure 3- 5: Example Rigid Model Implementation 76.0° F
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Figure 3- 6 illustrates another example implementation of the developed models using a mid- slab
deflection basin. In this example, the recorded surface temperature during testing was 69.1° F. The
actual measured deflections ( D1 to D4) are represented by the blue line. The appropriate model was
implemented for each deflection ( D1- D4), resulting in the adjusted deflections ( green line). For
example, the actual measured mid- slab D1 @ 69.1° F was 3.63 mils. The D1 model was used first to
estimate the deflection @ 69.1° F ( 3.49 mils) and then the deflection @ 68° F ( 3.46 mils). The
difference between these two deflections, ΔD1, is equal to - 0.03 mils (- 1% of measured D1). As a
result, the adjusted D1 would be 3.63 – 0.03 = 3.60 mils.
5
4
3
2
1
0
1 2 3 4
Deflection ( mils)
Sensor Number
Measured Adjusted
Figure 3- 6: Example Rigid Model Implementation 69.1° F
Figure 3- 7 shows a final example implementation of the developed models, again using a mid- slab
deflection basin. In this example, the recorded surface temperature during testing was 58.0° F. The
actual measured deflections ( D1 to D4) are represented by the blue line. The appropriate model was
implemented for each deflection ( D1- D4), resulting in the adjusted deflections ( green line). For
example, the actual measured mid- slab D1 @ 58.0° F was 3.22 mils. The D1 model was used first to
estimate the deflection @ 58.0° F ( 3.62 mils) and then the deflection @ 68° F ( 3.64 mils). The
difference between these two deflections, ΔD1, is equal to + 0.02 mils (~ 1% of measured D1). As a
result, the adjusted D1 would be 3.22 + 0.02 = 3.24 mils.
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Seasonal Study
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5
4
3
2
1
0
1 2 3 4
Deflection ( mils)
Sensor Number
Measured Adjusted
Figure 3- 7: Example Rigid Model Implementation 58.0° F
As can be seen from these examples, in general the temperature adjustments of deflections for rigid
pavements are very small compared with those of flexible pavements.
3.4 APPLICATION OF TEMPERATURE ADJUSTMENT MODELS TO MAIN STUDY
DATA
Using the process outlined above, the developed temperature adjustment models were applied to
the FWD data collected from Phase I and Phase II sections. The adjusted D1 – D9, Ep, and Mr
values for flexible pavements, and the adjusted D1 – D9, Epcc and k- static values for rigid pavements
have been uploaded into the project database. The original deflections and parameters were not
overwritten – these can also still be found in the database.
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4.0 Traffic Study
Since traffic data represents a vital component for reaching reliable results and conclusions
regarding pavement performance, Caltrans’ Traffic Database was searched for traffic loadings for
the selected test sections in the Phase I project. However, it was found that the number of test
sections with measured traffic loadings was limited. Consequently, actual accumulated traffic
loadings were not considered in the Phase I and analysis of the impact of different factors ( materials,
environmental effects, etc.) on pavement performance was instead carried out in terms of pavement
age. The limitation of this approach is that two pavement sections of the same age may receive
significantly different traffic loadings ( i. e. truck loads), and as truck traffic is one of the key sources of
damage to pavements, using only pavement age does not allow a fair comparison of performance in
such a case.
The 2002 final report for the Phase I project4 concluded that the analysis results could not be
considered conclusive for two main reasons, one of which was the absence of reliable traffic data.
As a result, a traffic study was included in the Phase II project to collect limited time axle weight data
and utilize data from the existing Caltrans permanent weigh stations to estimate the accumulative
axle weights that have passed over a project since the construction of the last rehabilitation
treatment.
The following four steps were the main tasks involved in the Traffic Study:
1. Define the limits of the homogeneous traffic segments that contain one or more Phase I or II
test sections.
2. Perform an 8- or 24- hour traffic survey using portable Weigh- in- Motion ( WIM) devices on
each traffic segment.
3. Convert the collected 8- or 24- hour traffic data to an annual volume using the historical traffic
data available from Caltrans’ permanent weigh stations.
4. Apply reasonable growth factors to annual traffic to estimate the past traffic applied to each
test section since the construction of the existing treatment or to predict the expected future
traffic.
Homogeneous traffic segments, which contained multiple Phase I and II sections, were determined
and each segment was assigned an ID.
Traffic data collection using the portable WIMs was initially conducted in two periods – 2005 and
2007. Prior to commencing analysis, QC/ QA checks were performed on the collected data. Very little
of the 2007 data passed the QC/ QA protocols; as a result, WIM surveys for these traffic segments
were re- performed in 2008.
4 Stantec Consulting. ‘ Caltrans Pavement Performance Evaluation Services - Contract 65A0069 - Final Report’.
November 2002.
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The 2005 data collection was performed in conjunction with the FWD testing and included, as
planned, some 8- hour and some 24- hour collections. The 2008 data collection, however, was
performed as a standalone task and included only 24- hour surveys.
4.1 TRAFFIC DATA ANALYSIS
In this analysis, the 8- or 24- hour ( approximate) traffic data collected using the portable WIMs was
converted to an annual volume using historical traffic data available from Caltrans’ permanent weigh
stations. In this section, the analysis procedure will be explained using two example traffic segments:
1. Traffic segment 02- 004- N- 01, located in Contra Costa County, on Route 4 between
mileposts 40.52 and 42.06
2. Traffic segment 02- 085- S- 02, located in Santa Clara County, on Route 85 between mileposts
13.52 and 13.63
4.1.1 Determination of Traffic at Permanent Weigh Station Locations
The first step in the analysis was to assign each of the traffic segments to their nearest permanent
weigh station location. For traffic segments 02- 004- N- 01 and 02- 085- S- 02, the nearest permanent
weigh stations were the Vacaville ( EB) and Gilroy stations, respectively. Table 4- 1 shows the
permanent weigh station location assigned to each traffic segment.
Table 4- 1: Permanent Weigh Station Locations Assigned to Traffic Segments
Traffic Segment ID Permanent Weigh Station
01‐ 005‐ L‐ 01 Mt Shasta
01‐ 005‐ L‐ 02 Mt Shasta
01‐ 005‐ L‐ 03 Mt Shasta
01‐ 005‐ L‐ 04 Mt Shasta
01‐ 005‐ L‐ 05 Mt Shasta
01‐ 005‐ L‐ 06 Mt Shasta
01‐ 005‐ L‐ 07 Redding
01‐ 005‐ L‐ 09 Lodi
01‐ 005‐ L‐ 09 Lodi
01‐ 005‐ L‐ 09 Lodi
01‐ 005‐ L‐ 11 Castaic ( SB)
01‐ 005‐ L‐ 12 Castaic ( SB)
01‐ 005‐ R‐ 01 Castaic ( SB)
01‐ 005‐ R‐ 04 Willows
01‐ 005‐ R‐ 05 Mt Shasta
01‐ 005‐ R‐ 06 Mt Shasta
01‐ 005‐ R‐ 07 Mt Shasta
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Traffic Segment ID Permanent Weigh Station
01‐ 005‐ R‐ 08 Mt Shasta
01‐ 008‐ R‐ 01 Cameron
01‐ 010‐ R‐ 01 Indio
01‐ 010‐ R‐ 02 Indio
01‐ 010‐ R‐ 03 Indio
01‐ 012‐ L‐ 01 Banta
01‐ 012‐ R‐ 01 Banta
01‐ 015‐ L‐ 01 Balboa ( NB)
01‐ 015‐ R‐ 01 Balboa ( NB)
01‐ 015‐ R‐ 02 Balboa ( NB)
01‐ 015‐ R‐ 03 Elsinore ( NB)
01‐ 015‐ R‐ 04 Elsinore ( NB)
01‐ 015‐ R‐ 05 Elsinore ( NB)
01‐ 029‐ L‐ 04 Lakeport
01‐ 050‐ L‐ 02 Antelope ( WB)
01‐ 050‐ L‐ 03 Antelope ( WB)
01‐ 050‐ R‐ 01 Antelope ( WB)
01‐ 050‐ R‐ 01 Antelope ( WB)
01‐ 050‐ R‐ 02 Antelope ( WB)
01‐ 050‐ R‐ 02 Antelope ( WB)
01‐ 058‐ L‐ 01 Arvin
01‐ 058‐ L‐ 02 Arvin
01‐ 058‐ R‐ 01 Arvin
01‐ 058‐ R‐ 02 Arvin
01‐ 059‐ L‐ 01 Los Banos
01‐ 059‐ R‐ 01 Los Banos
01‐ 060‐ L‐ 01 Murrieta
01‐ 060‐ R‐ 01 Murrieta
01‐ 073‐ L‐ 03 Saigon ( SB)
01‐ 073‐ L‐ 04 Saigon ( SB)
01‐ 073‐ L‐ 05 Saigon ( SB)
01‐ 073‐ R‐ 01 Saigon ( SB)
01‐ 078‐ R‐ 01 San Marcos
01‐ 078‐ R‐ 02 San Marcos
01‐ 080‐ L‐ 01 Antelope ( EB)
01‐ 080‐ R‐ 01 Antelope ( EB)
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Traffic Segment ID Permanent Weigh Station
01‐ 083‐ L‐ 01 Chino
01‐ 083‐ L‐ 02 Chino
01‐ 083‐ R‐ 01 Chino
01‐ 083‐ R‐ 02 Chino
01‐ 099‐ L‐ 02 Los Banos
01‐ 099‐ L‐ 03 Porterville
01‐ 099‐ L‐ 04 Bakersfield
01‐ 099‐ L‐ 05 Bakersfield
01‐ 101‐ L‐ 01 Templeton
01‐ 101‐ L‐ 03 Loleta
01‐ 101‐ R‐ 03 Loleta
01‐ 101‐ R‐ 05 Templeton
01‐ 166‐ R‐ 01 Positas
01‐ 227‐ R‐ 01 Templeton
01‐ 299‐ L‐ 01 Loleta
01‐ 299‐ R‐ 01 Loleta
01‐ 405‐ L‐ 01 Saigon ( NB)
01‐ 405‐ L‐ 02 Saigon ( NB)
01‐ 405‐ L‐ 03 Saigon ( NB)
01‐ 405‐ R‐ 01 Saigon ( NB)
01‐ 405‐ R‐ 02 Saigon ( NB)
01‐ 405‐ R‐ 03 Saigon ( NB)
01‐ 405‐ R‐ 04 Saigon ( NB)
02‐ 001‐ N‐ 01 Templeton
02‐ 001‐ N‐ 03 Gilroy
02‐ 001‐ N‐ 04 Loleta
02‐ 001‐ N‐ 05 Loleta
02‐ 001‐ S‐ 03 Woodside ( NB)
02‐ 001‐ S‐ 06 Templeton
02‐ 004‐ N‐ 01 Vacaville ( EB)
02‐ 005‐ N‐ 02 Redding
02‐ 005‐ S‐ 01 Redding
02‐ 020‐ E‐ 01 Lakeport
02‐ 020‐ E‐ 01 Lakeport
02‐ 020‐ E‐ 01 Lakeport
02‐ 029‐ N‐ 02 Lakeport
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Traffic Segment ID Permanent Weigh Station
02‐ 041‐ S‐ 03 Fresno
02‐ 058‐ E‐ 01 Lodi
02‐ 058‐ E‐ 03 Arvin
02‐ 058‐ W‐ 03 Arvin
02‐ 065‐ N‐ 03 Porterville
02‐ 065‐ S‐ 01 Porterville
02‐ 080‐ E‐ 02 Vacaville ( EB)
02‐ 080‐ E‐ 04 Vacaville ( EB)
02‐ 080‐ E‐ 05 Vacaville ( EB)
02‐ 080‐ W‐ 01 Vacaville ( EB)
02‐ 080‐ W‐ 01 Vacaville ( EB)
02‐ 084‐ E‐ 01 Hayward ( NB)
02‐ 084‐ E‐ 01 Hayward ( NB)
02‐ 085‐ N‐ 01 Gilroy
02‐ 085‐ S‐ 01 Gilroy
02‐ 085‐ S‐ 02 Gilroy
02‐ 101‐ N‐ 06 Positas
02‐ 101‐ N‐ 11 Templeton
02‐ 101‐ N‐ 12 Gilroy
02‐ 101‐ N‐ 12 Gilroy
02‐ 101‐ N‐ 12 Gilroy
02‐ 101‐ N‐ 14 Loleta
02‐ 101‐ N‐ 14 Loleta
02‐ 101‐ N‐ 14 Loleta
02‐ 101‐ N‐ 15 Loleta
02‐ 101‐ N‐ 16 Loleta
02‐ 101‐ N‐ 16 Loleta
02‐ 101‐ N‐ 16 Loleta
02‐ 101‐ N‐ 17 Loleta
02‐ 101‐ N‐ 17 Loleta
02‐ 101‐ N‐ 18 Loleta
02‐ 101‐ N‐ 18 Loleta
02‐ 101‐ N‐ 22 Loleta
02‐ 101‐ S‐ 06 Loleta
02‐ 101‐ S‐ 08 Loleta
02‐ 101‐ S‐ 09 Gilroy
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Traffic Segment ID Permanent Weigh Station
02‐ 101‐ S‐ 09 Gilroy
02‐ 101‐ S‐ 10 Gilroy
02‐ 152‐ W‐ 01 Gilroy
02‐ 152‐ W‐ 01 Gilroy
02‐ 154‐ E‐ 01 Positas
02‐ 299‐ N‐ 02 Loleta
02‐ 299‐ N‐ 03 Loleta
Once the permanent weigh stations were assigned, the data from each traffic segment was
examined to identify the date and time of the portable WIM survey. This information for the example
traffic segments is shown in Table 4- 2. The data available for the assigned permanent weigh station
was then examined. The permanent weigh stations typically had available data for the months of
January, April, July, and October 2005. This information was contained in a series of spreadsheets.
The spreadsheets for the month nearest to the month of the portable WIM survey were opened and
the record for the day of the survey was examined. Permanent weigh station data was not always
available for every day in the month, in which case the nearest date was selected.
Table 4- 2: Time of Portable WIM Survey at Example Segments
Traffic Segment ID Date From To Survey Hours
02‐ 004‐ N‐ 01 TUE 07/ 12/ 2005 12: 04: 36 AM 11: 53: 21 PM 23: 48
02‐ 085‐ S‐ 02 TUE 06/ 21/ 2005 6: 33: 33 AM 2: 39: 47 PM 8: 06
The permanent weigh stations record individual axle weights. However, the measure required for
this project is the Equivalent Single Axle Load ( ESAL). Throughout this analysis, the ASTM E 1318-
02 procedure5 was used to calculate ESALs from the individual axle weights. Using the weigh station
record from the day of the portable WIM survey ( or the nearest day), two calculations were made:
the total ESALs recorded at the weigh station for the entire day, and the total ESALs recorded at the
weigh station for the time that the WIM survey was being conducted at the traffic segment. The
calculated ESALs for the Vacaville ( EB) and Gilroy stations on the days that the portable WIM
surveys were being conducted at the example traffic segments are shown in Table 4- 3.
5 ASTM Designation E1318- 02. Standard Specification for Highway Weigh- in- Motion ( WIM) Systems with User
Requirements and Test Methods
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Table 4- 3: ESALs Calculated from Vacaville and Gilroy Weigh Stations
Total 24‐ hour ESALs
Total ESALs from 12: 04: 36 AM to
11: 53: 21 PM
Vacaville
( EB)
4,018 4,005
Total 24‐ hour ESALs
Total ESALs from 6: 33: 33 AM to
2: 39: 47 PM
Gilroy
6,807 3,139
The permanent weigh station records for the rest of the month were then examined. Total daily
ESALs were calculated for each day and totaled to give the total monthly ESALs at that station. If a
station did not have data for each day in the given month, the daily ESALs were totaled for each
available day and this figure was extrapolated to give a 30- day ( monthly) total. This process was
then repeated for all months in which data was available at that particular weigh station – typically
four months. The available monthly data was then extrapolated to give a 12- month ( annual) total.
The monthly and annual ESALs calculated for the Vacaville ( EB) and Gilroy stations are shown in
Table 4- 4. Occasionally during this process, a permanent WIM station measurement would appear
erroneously high. In such cases, this data was excluded from the analysis.
Table 4- 4: Total Monthly ESALs for Vacaville ( EB) and Gilroy Weigh Stations
Weigh Station
Total Monthly ESALs –
Month of WIM Survey Monthly ESALs for Other Available Months Total Annual ESALs
Vacaville ( EB) 94,934 120,820 66,297 104,376 1,159,278
Gilroy 164,712 150,085 160,730 119,869 1,786,185
The daily, monthly, and annual ESALs calculated for each weigh station ( based on the 2005 WIM
surveys only) are shown in Figures 4- 1 to 4- 3. Data for a weigh station may be repeated if it was
used for more than one traffic segment.
These figures give a good illustration of just how necessary the analysis being conducted is. In
Figure 4- 1, it can be seen how great the daily variability in truck traffic is amongst the weigh stations.
By looking at Figure 4- 2, it can be seen that the days of the WIM survey were not necessarily
representative of the month as a whole. For example, the Antelope ( WB) station has daily ESALs
that are fairly average for the stations as a whole, but has the highest monthly ESALs. This means
that the WIM survey was conducted on a day with unusually low truck traffic for that month. Figure 4-
3 reiterates this point. Using Antelope ( WB) as an example again, this station has gone from having
the highest monthly ESALs to having fairly average annual ESALs, meaning that the month of the
WIM survey had unusually high truck traffic. These noticeable variations are precisely the reason
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that accurate traffic data is needed for this project. It also shows how important it is not to rely on
traffic data collected on one day, without