Research, Development, and Technology

Turner-Fairbank Highway Research Center

6300 Georgetown Pike

McLean, VA 22101-2296

Pavement Performance Measures 

and Forecasting and the Effects of 

Maintenance and Rehabilitation Strategy

on Treatment Effectiveness (Revised)

PUBLICATION NO. FHWA-HRT-17-095 SEPTEMBER 2017
FOREWORD

“How effective will a particular treatment be on this roadway?” Data from the Long-Term 

Pavement Performance (LTPP) program have supported efforts to answer this question. This 

report documents work to advance the development of pavement performance measures to 

classify pavement sections, estimate future pavement conditions, evaluate the effectiveness of 

pavement maintenance and rehabilitation treatments, and analyze the role of pavement treatment 

strategies in extending pavement life.

Data from the various LTPP experiments were analyzed to define pavement performance in a way 

that supports the selection of cost-effective pavement treatment strategies. The research approach 

and results are presented in this report, including 1) the newly developed dual (functional and 

structural) pavement rating systems and their impact on pavement management, 2) the impacts of 

pavement preservation actions on pavement performance and longevity, 3) the effects of various 

pavement design parameters and climatic factors on pavement performance, 4) the aspects of a 

newly developed global model to accurately adjust the measured pavement deflections, and 5) the 

elements of a new statistical model to predict future pavement condition and distress based on a 

single data point.

This report can be used by pavement researchers, teachers in academic and lifelong (continuing) 

education settings, practicing engineers and contractors involved in pavement preservation, and 

road owners to develop cost-effective pavement preservation strategies.

Cheryl Allen Richter

Director, Office of Infrastructure 

Research, Development, and Technology

Notice

This document is disseminated under the sponsorship of the U.S. Department of Transportation 

in the interest of information exchange. The U.S. Government assumes no liability for the use of 

the information contained in this document.

The U.S. Government does not endorse products or manufacturers. Trademarks or 

manufacturers’ names appear in this report only because they are considered essential to the 

objective of the document.

Quality Assurance Statement

The Federal Highway Administration (FHWA) provides high-quality information to serve 

Government, industry, and the public in a manner that promotes public understanding. Standards 

and policies are used to ensure and maximize the quality, objectivity, utility, and integrity of its 

information. FHWA periodically reviews quality issues and adjusts its programs and processes to 

ensure continuous quality improvement.
TECHNICAL REPORT DOCUMENTATION PAGE

1. Report No.

FHWA- HRT-17-095

2. Government Accession No. 3. Recipient’s Catalog No.

4. Title and Subtitle

Pavement Performance Measures and Forecasting and the 

Effects of Maintenance and Rehabilitation Strategy on 

Treatment Effectiveness (Revised)

5. Report Date

September 2017

6. Performing Organization Code

7. Author(s)

Gilbert Y. Baladi, Ph.D., P.E.; Tyler Dawson, Ph.D., P.E.; 

Gopikrishna Musunuru; Michael Prohaska; and Kyle Thomas

8. Performing Organization Report No. 

9. Performing Organization Name and Address

Michigan State University

Department of Civil Engineering

428 S. Shaw Lane, Room 3546

East Lansing, MI 48824

10. Work Unit No.

11. Contract or Grant No.

DTFH-6113-C-00023

12. Sponsoring Agency Name and Address

Federal Highway Administration

6300 Georgetown Pike

McLean, VA 22101-2296

13. Type of Report and Period Covered

Final Report 

14. Sponsoring Agency Code

15. Supplementary Notes

The Federal Highway Administration Contracting Officer’s Representative was Yan “Jane” Jiang.

16. Abstract

This is a revised version of the following report: Pavement Performance Measures and Forecasting and the Effects 

of Maintenance and Rehabilitation Strategy on Treatment Effectiveness (FHWA-HRT-16-047), available at 

https://www.fhwa.dot.gov/publications/research/infrastructure/pavements/ltpp/16047/index.cfm.

New and innovative pavement performance measures and rating systems were developed and successfully 

implemented using pavement condition and distress data obtained from the Long-Term Pavement Performance 

(LTPP) database and the databases of three State transportation departments—Colorado Department of 

Transportation, Louisiana Department of Transportation and Development, and Washington State Department of 

Transportation. The new systems were used to calculate the benefits of various pavement treatments, determine the 

impacts of the pavement conditions in climatic regions on pavement performance, and assess the effects of design 

variables on pavement longevity. All analyses were conducted using MATLAB®-based computer programs that 

were specifically developed during this study. A preliminary method to estimate pavement performance based on a 

single data point was developed. Finally, the LTPP measured pavement deflection data were analyzed to determine 

whether the data could be used as pavement distress indicators. During the analyses, existing temperatureadjustment procedures were evaluated, and a global algorithm was developed that was applied to all deflection 

sensors in all climatic regions.

Due to editorial errors, this report is a revised version of a report with the same name issued in January 2017 under 

publication number FHWA-HRT-16-047. Revisions were made to tables 109–111 and figures 83–90. An errata 

document is available at https://www.fhwa.dot.gov/publications/research/infrastructure/pavements/ltpp/16047/.

17. Key Words

Pavement performance, Performance 

measures, Pavement rating, Treatment 

benefits, Pavement deflection

18. Distribution Statement

No restrictions. This document is available through the National 

Technical Information Service, Springfield, VA 22161.

http://www.ntis.gov

19. Security Classif. (of this report)

Unclassified

20. Security Classif. (of this page)

Unclassified

21. No. of Pages

329

22. Price

N/A

Form DOT F 1700.7 (872) Reproduction of completed page authorized.
ii
iii

TABLE OF CONTENTS

EXECUTIVE SUMMARY .......................................................................................................... 1

CHAPTER 1. INTRODUCTION................................................................................................ 7

CHAPTER 2. LITERATURE REVIEW.................................................................................. 11

PAVEMENT DISTRESS SEVERITY LEVELS................................................................. 11

ENGINEERING THRESHOLDS (CRITERIA) ................................................................. 15

PAVEMENT DISTRESS AND CONDITION INDICES ................................................... 18

DESCRIPTIVE PAVEMENT CONDITIONS .................................................................... 19

PAVEMENT PERFORMANCE MODELING AND PREDICTION ............................... 21

PAVEMENT PRESERVATION BENEFITS...................................................................... 24

PAVEMENT TREATMENT TYPES................................................................................... 31

PAVEMENT TREATMENT SELECTION ........................................................................ 31

Pavement Preservation Costs................................................................................................ 35

PAVEMENT PRESERVATION EFFECTIVENESS......................................................... 38

Pavement Preservation Cost Effectiveness at the Project Level........................................... 39

Pavement Preservation Effectiveness at the Network Level ................................................ 39

TREATMENT TRANSITION MATRIX (T2M) ................................................................. 40

PRESERVATION TIME SELECTION............................................................................... 42

THE LTPP PROGRAM......................................................................................................... 42

OBJECTIVES AND SCOPE OF THE LTPP PROGRAM................................................ 42

LTPP TEST SECTIONS........................................................................................................ 43

SPS........................................................................................................................................ 43

GPS ....................................................................................................................................... 44

Summary of Previous Findings............................................................................................. 45

SUMMARY, CONCLUSIONS, AND RECOMMENDATIONS ....................................... 50

CHAPTER 3. PAVEMENT CONDITION CLASSIFICATION........................................... 53

PAVEMENT CONDITION RATING SYSTEM................................................................. 55

RECOMMENDED THRESHOLD VALUES...................................................................... 66

FLEXIBILITY OF THE PAVEMENT RATING SYSTEMS............................................ 72

SUMMARY, CONCLUSIONS, AND RECOMMENDATIONS ....................................... 76

CHAPTER 4. DATA MINING AND SYNTHESIS................................................................. 77

DATA SOURCES ................................................................................................................... 77

AUTOMATED AND MANUAL PAVEMENT DISTRESS DATA................................... 77

DATA EXTRACTION........................................................................................................... 80

Inventory Data ...................................................................................................................... 80

Time-Series Pavement Condition and Distress Data ............................................................ 80

Pavement Rehabilitation, Preservation, and Maintenance Data ........................................... 83

Traffic Data ........................................................................................................................... 83

FWD Data ............................................................................................................................. 83

Climatic Data ........................................................................................................................ 83

STATUS OF THE CONDITION AND DISTRESS DATA................................................ 84

STATUS OF THE MAINTENANCE AND REHABILITATION DATA ........................ 86

ANALYSES PROCEDURES................................................................................................. 86
iv

Addition of One Data Point Immediately After Certain Treatments.................................... 87

Using the Control Section Data for Before Treatment Conditions....................................... 91

Severity Level ....................................................................................................................... 96

SUMMARY AND CONCLUSIONS ..................................................................................... 96

CHAPTER 5. LTPP DATA ANALYSES OF FLEXIBLE PAVEMENTS ........................... 97

MODELING THE TIME-SERIES PAVEMENT CONDITION AND DISTRESS 

DATA ................................................................................................................................. 97

IMPACTS OF CLIMATIC REGIONS, DRAINAGE, AND AC THICKNESS ON 

PAVEMENT PERFORMANCE USING THE LTPP SPS-1 TEST SECTIONS..... 102

IRI ...................................................................................................................................... 111

Rut Depth ............................................................................................................................ 112

Alligator Cracking .............................................................................................................. 113

Longitudinal Cracking ........................................................................................................ 113

Transverse Cracking ........................................................................................................... 114

Summary, Conclusions, and Recommendations for LTPP SPS-1...................................... 115

IMPACTS OF MAINTENANCE TREATMENTS ON PAVEMENT 

PERFORMANCE USING THE LTPP SPS-3 TEST SECTIONS............................. 116

IRI ...................................................................................................................................... 120

Rut Depth ............................................................................................................................ 125

Alligator Cracking .............................................................................................................. 127

Longitudinal Cracking ........................................................................................................ 129

Transverse Cracking ........................................................................................................... 131

Summary, Conclusions, and Recommendations for LTPP SPS-3...................................... 133

IMPACT OF REHABILITATION TREATMENTS ON PAVEMENT 

PERFORMANCE USING LTPP SPS-5 TEST SECTIONS ...................................... 134

IRI ...................................................................................................................................... 146

Rut Depth ............................................................................................................................ 147

Alligator Cracking .............................................................................................................. 147

Longitudinal Cracking ........................................................................................................ 147

Transverse Cracking ........................................................................................................... 147

Summary, Conclusions and Recommendations for LTPP SPS-5....................................... 147

IMPACTS OF PAVEMENT TREATMENTS ON PAVEMENT PERFORMANCE 

USING THE LTPP GPS-6 TEST SECTIONS............................................................. 148

IRI ...................................................................................................................................... 155

Rut Depth ............................................................................................................................ 155

Alligator Cracking .............................................................................................................. 156

Longitudinal Cracking ........................................................................................................ 156

Transverse Cracking ........................................................................................................... 156

Impact of the Before Treatment Condition and Distress on the Performance of the 

Pavement After Treatment for LTPP GPS-6 ................................................................ 157

Summary, Conclusions, and Recommendations for LTPP GPS-6 ..................................... 162

ORCSE METHOD ............................................................................................................... 163

ORCSE Method and Development Procedures Using LTPP Data..................................... 163

Results of ORCSE Predictions and Analyses of LTPP SPS-1 Test Sections..................... 170

Results of Validation of ORCSE Predictions and Analyses of LTPP SPS-1 Test 

Sections......................................................................................................................... 173
v

Summary, Conclusions, and Recommendations for the ORCSE Model............................ 179

CHAPTER 6. LTPP DATA ANALYSES OF RIGID PAVEMENTS ................................. 181

IMPACTS OF MAINTENANCE TREATMENTS ON PAVEMENT CONDITION 

AND DISTRESS USING THE LTPP SPS-2 TEST SECTIONS ............................... 181

Analysis Steps..................................................................................................................... 181

Analyses Results................................................................................................................. 184

IRI ...................................................................................................................................... 199

Longitudinal Cracking ........................................................................................................ 199

Transverse Cracking ........................................................................................................... 199

Summary, Conclusions, and Recommendations for LTPP SPS-2...................................... 200

IMPACTS OF MAINTENANCE TREATMENTS ON PAVEMENT CONDITION 

AND DISTRESS USING THE LTPP SPS-4 TEST SECTIONS ............................... 200

IRI ...................................................................................................................................... 207

Longitudinal Cracking ........................................................................................................ 207

Transverse Cracking ........................................................................................................... 207

Summary, Conclusions, and Recommendations for LTTP SPS-4...................................... 208

IMPACTS OF REHABILITATION TREATMENTS ON PAVEMENT 

CONDITION AND DISTRESS USING THE LTPP SPS-6 TEST SECTIONS....... 208

IRI ...................................................................................................................................... 216

Rut Depth ............................................................................................................................ 216

Alligator Cracking .............................................................................................................. 216

Longitudinal Cracking ........................................................................................................ 217

Summary, Conclusions, and Recommendations for LTPP SPS-6...................................... 218

IMPACTS OF BONDED CONCRETE OVERLAYS ON PAVEMENT 

PERFORMANCE USING THE LTPP SPS-7 TEST SECTIONS............................. 219

IRI ...................................................................................................................................... 224

Longitudinal Cracking ........................................................................................................ 224

Transverse Cracking ........................................................................................................... 225

Summary, Conclusions, and Recommendations for LTPP SPS-7...................................... 225

IMPACTS OF PAVEMENT TREATMENTS ON PAVEMENT PERFORMANCE 

USING THE LTPP GPS-7 TEST SECTIONS............................................................. 226

IRI ...................................................................................................................................... 232

Rut Depth ............................................................................................................................ 232

Alligator Cracking .............................................................................................................. 232

Longitudinal Cracking ........................................................................................................ 233

Transverse Cracking ........................................................................................................... 233

Summary, Conclusions, and Recommendations for LTPP GPS-7 ..................................... 234

CHAPTER 7. LTPP DEFLECTION DATA ANALYSES.................................................... 235

FWD DEFLECTION DATA AND RFP AND RSP........................................................... 235

TIME-SERIES FWD DATA MODELING—FLEXIBLE PAVEMENT........................ 236

Temperature-Deflection Correlation................................................................................... 239

Flexible Pavement Deflection Versus Time ....................................................................... 248

Backcalculation of Pavement Layer Resilient Moduli Values ........................................... 253

Rigid Pavement Deflection Versus Time ........................................................................... 259

Rigid Pavement LTE Versus Time ..................................................................................... 266

SUMMARY, CONCLUSIONS, AND RECOMMENDATIONS ..................................... 270
vi

CHAPTER 8. STATE DATA ANALYSES ............................................................................ 273

BACKGROUND ................................................................................................................... 273

ANALYSIS STEPS............................................................................................................... 274

IRI276

RUT DEPTH ......................................................................................................................... 279

ALLIGATOR CRACKING................................................................................................. 282

LONGITUDINAL CRACKING ......................................................................................... 285

TRANSVERSE CRACKING .............................................................................................. 288

SUMMARY, CONCLUSIONS, AND RECOMMENDATIONS ..................................... 291

CHAPTER 9. SUMMARY, CONCLUSIONS, AND RECOMMENDATIONS................. 293

SUMMARY ........................................................................................................................... 293

CONCLUSIONS ................................................................................................................... 295

Pavement Performance Measures....................................................................................... 296

Flexible Pavements............................................................................................................. 296

ORCSE Method .................................................................................................................. 298

Rigid and Composite Pavements ........................................................................................ 298

Deflection............................................................................................................................ 299

State Data ............................................................................................................................ 299

RECOMMENDATIONS...................................................................................................... 300

Performance Measures........................................................................................................ 300

Flexible Pavements............................................................................................................. 301

ORCSE Method .................................................................................................................. 301

Deflection............................................................................................................................ 301

State Data ............................................................................................................................ 302

Future Studies ..................................................................................................................... 302

REFERENCES.......................................................................................................................... 303
vii

LIST OF FIGURES

Figure 1. Graph. Time-series transverse cracking data for each severity level and the sum 

of all severity levels for LTTP SPS-3 test section A330 in California................................... 12

Figure 2. Graph. Cumulative time-series transverse cracking data showing individual 

transverse crack severity level and the sum of all severity levels for LTTP SPS-3 test 

section A330 in California ...................................................................................................... 12

Figure 3. Graph. Time-series transverse cracking data for each severity level and the sum 

of all severity levels, Highway 24, direction 2, BMP 329.9, in Colorado.............................. 14

Figure 4. Graph. Cumulative time-series transverse cracking data showing individual 

transverse crack severity level and the sum of all severity levels, Highway 24, direction 

2, BMP 329.9, in Colorado ..................................................................................................... 14

Figure 5. Chart. Rating and descriptive scales and distress points............................................... 16

Figure 6. Graph. Descriptive rating scale for pavement condition............................................... 17

Figure 7. Equation. DI .................................................................................................................. 18

Figure 8. Graph. Pavement condition classification system......................................................... 20

Figure 9. Graph. Shortcomings of the recommended classification system when dealing 

with real but good data (not the worst-case scenario)............................................................. 20

Figure 10. Graph. A typical pavement performance curve........................................................... 22

Figure 11. Equation. PI................................................................................................................. 23

Figure 12. Equation. RD ............................................................................................................... 24

Figure 13. Equation. IRI................................................................................................................ 24

Figure 14. Equation. Crack........................................................................................................... 24

Figure 15. Graph. Relationship between RSL and cost of managing pavements ......................... 26

Figure 16. Equation. RSL.............................................................................................................. 27

Figure 17. Equation. RSLnetwork ..................................................................................................... 27

Figure 18. Graph. Schematic of the definition of SLE ................................................................. 28

Figure 19. Graph. Schematic of the definition of TL ................................................................... 29

Figure 20. Graph. Schematic of the definition of negative TL..................................................... 29

Figure 21. Graph. Schematic of the definition of TB ................................................................... 30

Figure 22. Graph. PJ and DRR ..................................................................................................... 31

Figure 23. Illustration. Example of decision tree for continuously reinforced concrete 

pavement (CRCP)................................................................................................................... 32

Figure 24. Equation. NPW ............................................................................................................ 37

Figure 25. Equation. EUAC .......................................................................................................... 38

Figure 26. Graph. Typical pavement condition or distress over the pavement lifecycle.............. 54

Figure 27. Graph. IRI versus elapsed time for LTTP SPS-1 test sections 0102 and 0103 

in Iowa .................................................................................................................................... 55

Figure 28. Equation. Rut depth ..................................................................................................... 57

Figure 29. Equation. t.................................................................................................................... 57

Figure 30. Equation. RSP.............................................................................................................. 58

Figure 31. Graph. Example of idealized rut depth data and function versus elapsed time........... 59

Figure 32. Graph. RSP versus the pavement surface age for an idealized power function .......... 60

Figure 33. Graph. IRI versus elapsed time for LTPP SPS-1 test sections 0102 and 0103 

in Iowa .................................................................................................................................... 60

Figure 34. Graph. RFP versus pavement surface age for LTPP test section 0102 in Iowa .......... 61
viii

Figure 35. Graph. RFP CSs .......................................................................................................... 62

Figure 36. Graph. RSP CSs .......................................................................................................... 62

Figure 37. Graph. Conceptualized cost of pavement preservation versus RSP............................ 63

Figure 38. Graph. Correlations among RSP, cost of preservation, and descriptive pavement 

classification ........................................................................................................................... 63

Figure 39. Graph. Example of pavement condition over time with two thin HMA overlays 

and one thin mill-and-fill action ............................................................................................. 65

Figure 40. Graph. Example RSP over time with two thin HMA overlays and one mill-andfill action ................................................................................................................................. 65

Figure 41. Illustration. Envisioned change in deflection through the progression of alligator 

cracking................................................................................................................................... 71

Figure 42. Graph. Idealized S-shaped curve for alligator cracking showing three windows 

(threshold values) for various treatment actions..................................................................... 75

Figure 43. Graph. Transverse cracking versus elapsed time for LTTP SPS-3 test section 

A330 in California .................................................................................................................. 81

Figure 44. Graph. Transverse cracking versus elapsed time for LTTP SPS-5 test section 

0502 in California ................................................................................................................... 82

Figure 45. Graph. Total longitudinal cracking versus time for LTTP SPS-3 test section 

A310 in Maryland................................................................................................................... 92

Figure 46. Graph. Total longitudinal cracking versus time for LTPP SPS-3 control section 

A340 in Maryland................................................................................................................... 92

Figure 47. Graph. Total longitudinal cracking versus elapsed time for LTPP GPS-1634 

linked test section to SPS-3 experiment in Maryland............................................................. 93

Figure 48. Graph. Total longitudinal cracking versus elapsed time for LTTP SPS-3 test 

section A350 and A340 control section in New York ............................................................ 94

Figure 49. Graph. IRI versus elapsed time for LTTP SPS-1 test section 0119 in Texas.............. 95

Figure 50. Graph. Exponential, power, and logistic (S-shaped) curves ....................................... 98

Figure 51. Illustration. Flowchart of the MATLAB® program.................................................. 101

Figure 52. Graph. After-treatment RFP versus before-treatment IRI of LTPP SPS-3 test 

sections subjected to thin overlay ......................................................................................... 123

Figure 53. Graph. After-treatment RFP versus before-treatment IRI of LTPP SPS-3 test 

sections subjected to slurry seal............................................................................................ 123

Figure 54. Graph. After-treatment RFP versus before-treatment IRI of LTPP SPS-3 test 

sections subjected to crack seal............................................................................................. 124

Figure 55. Graph. After-treatment RFP versus before-treatment IRI of LTPP SPS-3 test 

sections subjected to aggregate seal coat.............................................................................. 124

Figure 56. Graph. ORCSE model probability graph from LTPP SPS-1 before treatment 

evaluation.............................................................................................................................. 164

Figure 57. Illustration. Flowchart of ORCSE model steps with examples from LTPP SPS-1 

before treatment evaluation................................................................................................... 165

Figure 58. Graph. Backpropagation of IRI data for SHRP ID 26_0116 from an RFP of 

0 years................................................................................................................................... 166

Figure 59. Graph. Backpropagation of IRI data for SHRP ID 19_0102 from an RFP of 

0 years................................................................................................................................... 167

Figure 60. Graph. Backpropagation of IRI data for SHRP ID 19_0103 from an RFP of 

0 years................................................................................................................................... 167
ix

Figure 61. Graph. Backpropagation of IRI data for SHRP IDs 26_0116, 19_0102, and 

19_0103 displaying variation in IRI growth from an RFP of 0 years .................................. 168

Figure 62. Graph. ORCSE model graph for LTPP SPS-1 virgin pavement analysis for 

iteration 1 .............................................................................................................................. 170

Figure 63. Graph. ORCSE model graph for LTPP SPS-1 virgin pavement analysis for 

iteration 2 .............................................................................................................................. 171

Figure 64. Graph. ORCSE model graph for LTPP SPS-1 virgin pavement analysis for 

iteration 3 .............................................................................................................................. 171

Figure 65. Graph. ORCSE model graph for LTPP SPS-1 virgin pavement analysis for 

iteration 4 .............................................................................................................................. 172

Figure 66. Graph. ORCSE model graph for LTPP SPS-1 virgin pavement analysis for 

iteration 5 .............................................................................................................................. 172

Figure 67. Graph. AI TAF .......................................................................................................... 240

Figure 68. Equation. APDi .......................................................................................................... 241

Figure 69. Graph. Peak measured pavement deflection at sensors 1, 2, 4, and 7 versus 

pavement surface temperature for SHRP test section 010101, F3........................................ 242

Figure 70. Graph. Average measured peak deflection at sensor 1 versus the measured 

pavement surface temperature along the outer wheelpath and mid lane of SHRP test 

sections 010101 and 010102................................................................................................. 243

Figure 71. Equation. TAFdi ......................................................................................................... 243

Figure 72. Equation. TAF............................................................................................................ 244

Figure 73. Equation. A ................................................................................................................ 244

Figure 74. Equation. B ................................................................................................................ 244

Figure 75. Equation. C................................................................................................................ 244

Figure 76. Graph. Measured deflection basins at three pavement surface temperatures 52, 

70, and 86 oF (11, 21, and 30 oC) along the mid-lane of the SHRP test section 351112 

on April 25, 1995 .................................................................................................................. 246

Figure 77. Graph. Error from TAF and AI adjusted deflection basin at two pavement 

surface temperatures (52 and 86 oF (11 and 30 oC)), along the mid-lane of the SHRP 

test section 351112 on April 25, 1995 .................................................................................. 247

Figure 78. Graph. Percent error of the temperature-adjusted deflection data using the 

equation in figure 72 and the AI procedure .......................................................................... 247

Figure 79. Graph. Average measured peak pavement deflection at sensors 1, 2, 4, and 

7 versus time for SHRP test section 010101, F1 .................................................................. 249

Figure 80. Graph. Temperature-adjusted peak deflection at sensors 1, 2, 4, and 7 versus 

time for SHRP test section 010101, F1................................................................................. 250

Figure 81. Graph. Temperature-adjusted peak deflection at sensor 1 versus IRI for 

SHRP test section 010101, F1 .............................................................................................. 251

Figure 82. Graph. Temperature-adjusted peak deflection at sensor 1 versus rut depth for 

SHRP test section 010101, F1 .............................................................................................. 251

Figure 83. Graph. Temperature-adjusted peak deflection at sensor 1 versus alligator 

cracking for SHRP test section 010101, F1.......................................................................... 252

Figure 84. Graph. Temperature-adjusted peak deflection at sensor 1 versus longitudinal 

cracking for SHRP test section 010101, F1.......................................................................... 252

Figure 85. Graph. Temperature-adjusted peak deflection at sensor 1 versus transverse 

cracking for SHRP test section 010101, F1.......................................................................... 253
x

Figure 86. Graph. AC layer MR values versus pavement surface temperature for SHRP test 

section 010101 ...................................................................................................................... 256

Figure 87. Graph. AC layer MR values versus pavement surface temperature for SHRP test 

section 081053 ...................................................................................................................... 256

Figure 88. Graph. AC layer MR values versus pavement surface temperature for SHRP test 

section 271028 ...................................................................................................................... 257

Figure 89. Graph. AC layer MR values versus pavement surface temperature for SHRP test 

section 351112 ...................................................................................................................... 257

Figure 90. Graph. AC layer MR values from measured and temperature-adjusted 

deflection data....................................................................................................................... 258

Figure 91. Graph. Base and roadbed soil layer MR values from measured and temperatureadjusted deflection data......................................................................................................... 259

Figure 92. Graph. Measured peak deflection at sensor 1 versus time for SHRP test section 

100201, J1............................................................................................................................. 263

Figure 93. Graph. Measured peak deflection at sensor 1 versus IRI for SHRP test section 

100201, J1............................................................................................................................. 264

Figure 94. Graph. Measured peak deflection at sensor 1 versus faulting for SHRP test 

section 100201, J1................................................................................................................. 265

Figure 95. Graph. Measured peak deflection at sensor 1 versus longitudinal cracking for 

SHRP test section 100201, J1 ............................................................................................... 265

Figure 96. Graph. Measured peak deflection at sensor 1 versus transverse cracking for 

SHRP test section 100201, J1 ............................................................................................... 266

Figure 97. Graph. LTE versus time for SHRP test section 100201, J4 ...................................... 267

Figure 98. Graph. LTE versus IRI for SHRP test section 100201, J4 ........................................ 268

Figure 99. Graph. LTE versus faulting for SHRP test section 100201, J4................................. 269

Figure 100. Graph. LTE versus longitudinal cracking for SHRP test section 100201, J4 ......... 269

Figure 101. Graph. LTE versus transverse cracking for SHRP test section 100201, J4 ............ 269

Figure 102. Graph. Comparison of the weighted average RFP based on IRI of five 

treatment types performed on LTPP test sections and on various pavement projects of 

CDOT, LADOTD, and WSDOT .......................................................................................... 278

Figure 103. Graph. Comparison of the weighted average CFP based on IRI of five 

treatment types performed on LTPP test sections and on various pavement projects of 

CDOT, LADOTD, and WSDOT .......................................................................................... 278

Figure 104. Graph. Comparison of the weighted average FCROP based on IRI of five 

treatment types performed on LTPP test sections and on various pavement projects of 

CDOT, LADOTD, and WSDOT .......................................................................................... 279

Figure 105. Graph. Comparison of the weighted average RFP/RSP based on rut depth of 

five treatment types performed on LTPP test sections and on pavement projects of 

CDOT, LADOTD, and WSDOT .......................................................................................... 281

Figure 106. Graph. Comparison of the weighted average CFP/CSP based on rut depth of 

five treatment types performed on LTPP test sections and on pavement projects of 

CDOT, LADOTD, and WSDOT .......................................................................................... 281

Figure 107. Graph. Comparison of the weighted average FCROP/SCROP based on rut 

depth of five treatment types performed on LTPP test sections and on pavement 

projects of CDOT, LADOTD, and WSDOT ........................................................................ 282
xi

Figure 108. Graph. Comparison of the weighted average RSP based on alligator cracking 

of five treatment types performed on LTPP test sections and on pavement projects of 

CDOT, LADOTD, and WSDOT .......................................................................................... 284

Figure 109. Graph. Comparison of the weighted average CSP based on alligator cracking 

of five treatment types performed on LTPP test sections and on pavement projects of 

CDOT, LADOTD, and WSDOT .......................................................................................... 284

Figure 110. Graph. Comparison of the weighted average SCROP based on alligator 

cracking of five treatment types performed on LTPP test sections and on pavement 

projects of CDOT, LADOTD, and WSDOT ........................................................................ 285

Figure 111. Graph. Comparison of the weighted average RSP based on longitudinal 

cracking of five treatment types performed on LTPP test sections and on pavement 

projects of CDOT, LADOTD, and WSDOT ........................................................................ 287

Figure 112. Graph. Comparison of the weighted average CSP based on longitudinal 

cracking of five treatment types performed on LTPP test sections and on pavement 

projects of CDOT, LADOTD, and WSDOT ........................................................................ 287

Figure 113. Graph. Comparison of the weighted average SCROP based on longitudinal 

cracking of five treatment types performed on LTPP test sections and on pavement 

projects of CDOT, LADOTD, and WSDOT ........................................................................ 288

Figure 114. Graph. Comparison of the weighted average RSP based on transverse cracking 

of five treatment types performed on LTPP test sections and on pavement projects of 

CDOT, LADOTD, and WSDOT .......................................................................................... 290

Figure 115. Graph. Comparison of the weighted average CSP based on transverse cracking 

of five treatment types performed on LTPP test sections and on pavement projects of 

CDOT, LADOTD, and WSDOT .......................................................................................... 290

Figure 116. Graph. Comparison of the weighted average SCROP based on transverse 

cracking of five treatment types performed on LTPP test sections and on pavement 

projects of CDOT, LADOTD, and WSDOT ........................................................................ 291
xii

LIST OF TABLES

Table 1. Engineering criteria and deduct points for alligator cracking......................................... 18

Table 2. Estimated and reported pavement treatment service life ................................................ 23

Table 3. Threshold values that could be used in the AASHTO MEPDG..................................... 25

Table 4. Trigger values for functional classifications................................................................... 33

Table 5. Example of decision matrix ............................................................................................ 34

Table 6. Review of user cost components .................................................................................... 35

Table 7. Historical discount rates.................................................................................................. 38

Table 8. T2M for single chip seal in Colorado.............................................................................. 41

Table 9. The SPS categories and experiments.............................................................................. 44

Table 10. The GPS experiments ................................................................................................... 45

Table 11. Pavement condition rating based on three CSs............................................................. 57

Table 12. Pavement condition rating based on five CSs .............................................................. 57

Table 13. Progressive calculation of RSP of the idealized rut depth shown in figure 31............. 59

Table 14. RFP of LTTP test section 0102 in Iowa based on the IRI data shown and 

depicted in figure 34 ............................................................................................................... 61

Table 15. Threshold values describing RFP ................................................................................. 66

Table 16. Threshold values describing RSP ................................................................................. 67

Table 17. Active LTPP SPS test sections as of January 2014 ...................................................... 77

Table 18. Active LTPP GPS test sections as of January 2014...................................................... 77

Table 19. Number of manual and semi-automated surveys for test sections 0101–0112 in 

LTPP SPS-1 experiment ......................................................................................................... 79

Table 20. Number of manual and semi-automated surveys for test sections 0113–0124 in 

LTPP SPS-1 experiment ......................................................................................................... 79

Table 21. Number of cracking data points available before treatment and after treatment for 

LTTP SPS-1 test sections in Montana .................................................................................... 85

Table 22. Number of LTTP SPS test sections with available treatment data in the database ...... 86

Table 23. Number of LTTP GPS test sections with available treatment data in the database...... 86

Table 24. Condition or distress type eligible for data addition for different treatments............... 88

Table 25. Summary of cracking data for LTPP SPS-1 test sections located in the DNF 

region ...................................................................................................................................... 89

Table 26. Summary of treatments applied to LTTP SPS-1 through -7 and GPS-6, -7, and -9 

sections analyzed in this study................................................................................................ 91

Table 27. Description of the mathematical functions used in the analyses of the pavement 

distress and condition data ...................................................................................................... 98

Table 28. Crack saturation values used in the analyses of the pavement cracking data............... 99

Table 29. Summary of the results of analyses of the impacts of design factors on RFP of 

LTPP SPS-1 test sections based on IRI (years) .................................................................... 104

Table 30. Summary of the results of analyses of the impacts of design factors on RFP/RSP 

of LTPP SPS-1 test sections based on rut depth (years)....................................................... 105

Table 31. Summary of the results of analyses of the impacts of design factors on RSP of 

LTPP SPS-1 test sections based on alligator cracking (years) ............................................. 106

Table 32. Summary of the results of analyses of the impacts of design factors on RSP of 

LTPP SPS-1 test sections based on longitudinal cracking (years) ....................................... 107
xiii

Table 33. Summary of the results of analyses of the impacts of design factors on RSP of 

LTPP SPS-1 test sections based on transverse cracking (years)........................................... 108

Table 34. Summary of the results of analyses of the effects of climatic regions on the 

performance of the LTPP SPS-1 test sections...................................................................... 109

Table 35. Linked GPS sections that serve as control sections(60)............................................... 117

Table 36. Number of test sections that have before treatment and after treatment pavement 

condition, distress, and traffic data ....................................................................................... 119

Table 37. Impacts of various maintenance treatments and control section on pavement 

performance in terms of RFP based on IRI .......................................................................... 121

Table 38. Impacts of various maintenance treatments and control section on pavement 

performance in terms of RFP/RSP based on rut depth ......................................................... 126

Table 39. Impacts of various maintenance treatments and control section on pavement 

performance in terms of RSP based on alligator cracking.................................................... 128

Table 40. Impacts of various maintenance treatments and control section on pavement 

performance in terms of RSP based on longitudinal cracking.............................................. 130

Table 41. Impacts of various maintenance treatments and control section on pavement 

performance in terms of RSP based on transverse cracking................................................. 132

Table 42. Summary of the impact of treatment type on pavement performance compared 

with the control sections (years) ........................................................................................... 133

Table 43. Impacts of various treatments and control section on pavement performance in 

terms of RFP based on IRI for virgin AC mixes (years) ...................................................... 136

Table 44. Impacts of various treatments and control section on pavement performance in 

terms of RFP based on IRI for recycled AC mixes (years) .................................................. 137

Table 45. Impacts of various treatments and control section on pavement performance in 

terms of RFP/RSP based on rut depth for virgin AC mixes (years)..................................... 138

Table 46. Impacts of various treatments and control section on pavement performance in 

terms of RFP/RSP based on rut depth for recycled AC mixes (years)................................. 139

Table 47. Impacts of various treatments and control section on pavement performance in 

terms of RSP based on alligator cracking for virgin AC mixes (years)................................ 140

Table 48. Impacts of various treatments and control section on pavement performance in 

terms of RSP based on alligator cracking for recycled AC mixes (years)............................ 141

Table 49. Impacts of various treatments and control section on pavement performance in 

terms of RSP based on longitudinal cracking for virgin AC mixes (years).......................... 142

Table 50. Impacts of various treatments and control section on pavement performance in 

terms of RSP based on longitudinal cracking for recycled AC mixes (years)...................... 143

Table 51. Impacts of various treatments and control section on pavement performance in 

terms of RSP based on transverse cracking for virgin AC mixes (years)............................. 144

Table 52. Impacts of various treatments and control section on pavement performance in 

terms of RSP based on transverse cracking for recycled AC mixes (years)......................... 145

Table 53. Summary of benefits of various rehabilitation treatments (years).............................. 146

Table 54. Impacts of various treatments on pavement performance in terms of CFP based 

on IRI (years)........................................................................................................................ 150

Table 55. Impacts of various treatments on pavement performance in terms of CFP/CSP 

based on rut depth (years)..................................................................................................... 151

Table 56. Impacts of various treatments on pavement performance in terms of CSP based 

on alligator cracking (years) ................................................................................................. 152
xiv

Table 57. Impacts of various treatments on pavement performance in terms of CSP based 

on longitudinal cracking (years) ........................................................................................... 153

Table 58. Impacts of various treatments on pavement performance in terms of CSP based 

on transverse cracking (years) .............................................................................................. 154

Table 59. Functional T2M for thin overlay using virgin AC mix (IRI, number of LTPP test 

sections) ................................................................................................................................ 158

Table 60. Functional T2M for thick overlay using virgin AC mix (IRI, number of LTPP test 

sections) ................................................................................................................................ 159

Table 61. Structural T2M for thin overlay using virgin AC mix (transverse cracking, 

number of LTPP test sections).............................................................................................. 160

Table 62. Structural T2M for thick overlay using virgin AC mix (transverse cracking, 

number of LTPP test sections).............................................................................................. 161

Table 63. ORCSE model table example from LTPP SPS-1 before treatment evaluation .......... 169

Table 64. ORCSE model table for LTPP SPS-1 virgin pavement analysis for iteration 1......... 173

Table 65. ORCSE model table for LTPP SPS-1 virgin pavement analysis for iteration 2......... 173

Table 66. ORCSE model table for LTPP SPS-1 virgin pavement analysis for iteration 3......... 174

Table 67. ORCSE model table for LTPP SPS-1 virgin pavement analysis for iteration 4......... 174

Table 68. ORCSE model table for LTPP SPS-1 virgin pavement analysis for iteration 5......... 175

Table 69. ORCSE validation of estimated versus modeled CS groups for iteration 1 ............... 176

Table 70. ORCSE validation of estimated versus modeled CS groups for iteration 2 ............... 176

Table 71. ORCSE validation of estimated versus modeled CS groups for iteration 3 ............... 176

Table 72. ORCSE validation of estimated versus modeled CS groups for iteration 4 ............... 177

Table 73. ORCSE validation of estimated versus modeled CS groups for iteration 5 ............... 177

Table 74. ORCSE summarized validation of estimated versus modeled CS groups for all 

iterations................................................................................................................................ 177

Table 75. ORCSE averaged validation of estimated versus modeled CS groups, all 

iterations................................................................................................................................ 178

Table 76. ORCSE median validation of estimated versus modeled CS groups, all iterations ... 178

Table 77. Analysis subgroups and the number of test sections available for analyses in the 

LTPP SPS-2 experiment in each subgroup........................................................................... 183

Table 78. Summary of the results of the analyses of the impacts of design factors on RFP 

based on IRI of LTPP SPS-2 test sections with slab width of 12 ft (3.66 m) and 

concrete flexural strength of 551 lbf/inch2(3.8 MPa) .......................................................... 185

Table 79. Summary of the results of analyses of the impacts of design factors on RFP 

based on IRI of LTPP SPS-2 test sections with slab width 14 ft (4.27 m) and concrete 

flexural strength of 551 lbf/inch2(3.8 MPa)......................................................................... 186

Table 80. Summary of the results of analyses of the impacts of design factors on RFP 

based on IRI of LTPP SPS-2 test sections with slab width 12 ft (3.66 m) and concrete 

flexural strength of 899 lbf/inch2(6.2 MPa)......................................................................... 187

Table 81. Summary of the results of analyses of the impacts of design factors on RFP 

based on IRI of LTPP SPS-2 test sections with slab width 14 ft (4.27 m) and concrete 

flexural strength of 899 lbf/inch2(6.2 MPa)......................................................................... 188

Table 82. Summary of the results of analyses of the impacts of design factors on RSP 

based on longitudinal cracking of LTPP SPS-2 test sections with slab width 12 ft 

(3.66 m) and concrete flexural strength of 551 lbf/inch2(3.8 MPa)..................................... 189
xv

Table 83. Summary of the results of analyses of the impacts of design factors on RSP 

based on longitudinal cracking of LTPP SPS-2 test sections with slab width 14 ft 

(4.27 m) and concrete flexural strength of 551 lbf/inch2(3.8 MPa)..................................... 190

Table 84. Summary of the results of analyses of the impacts of design factors on RSP 

based on longitudinal cracking of LTPP SPS-2 test sections with slab width 12 ft 

(3.66 m) and concrete flexural strength of 899 lbf/inch2(6.2 MPa)..................................... 191

Table 85. Summary of the results of analyses of the impacts of design factors on RSP 

based on longitudinal cracking of LTPP SPS-2 test sections with slab width 14 ft 

(4.27 m) and concrete flexural strength of 899 lbf/inch2(6.2 MPa)..................................... 192

Table 86. Summary of the results of analyses of the impacts of design factors on RSP 

based on transverse cracking of LTPP SPS-2 test sections with slab width 12 ft 

(3.66 m) and concrete flexural strength of 551 lbf/inch2(3.8 MPa)..................................... 193

Table 87. Summary of the results of analyses of the impacts of design factors on RSP 

based on transverse cracking of LTPP SPS-2 test sections with slab width 14 ft 

(4.27 m) and concrete flexural strength of 551 lbf/inch2(3.8 MPa)..................................... 194

Table 88. Summary of the results of analyses of the impacts of design factors on RSP 

based on transverse cracking of LTPP SPS-2 test sections with slab width 12 ft 

(3.66 m) and concrete flexural strength of 899 lbf/inch2(6.2 MPa)..................................... 195

Table 89. Summary of the results of analyses of the impacts of design factors on RSP 

based on transverse cracking of LTPP SPS-2 test sections with slab width 14 ft 

(4.27 m) and concrete flexural strength of 899 lbf/inch2(6.2 MPa)..................................... 196

Table 90. Summary of the results of the analyses of the effects of climatic region on the 

performance of the LTPP SPS-2 test sections...................................................................... 198

Table 91. Number of test sections that have after treatment pavement condition and distress 

and traffic data ...................................................................................................................... 202

Table 92. Impacts of various maintenance treatments and control section on pavement 

performance in terms of RFP based on IRI .......................................................................... 204

Table 93. Impacts of various maintenance treatments and control section on pavement 

performance in terms of RSP based on longitudinal cracking.............................................. 205

Table 94. Impacts of various maintenance treatments and control section on pavement 

performance in terms of RSP based on transverse cracking................................................. 206

Table 95. RFP of control sections and the impact of treatment types on pavement 

performance in terms of RFP based on IRI .......................................................................... 210

Table 96. Impact of various treatments and control section on pavement performance in 

terms of RFP/RSP based on rut depth................................................................................... 211

Table 97. Impact of various treatments and control section on pavement performance in 

terms of RSP based on alligator cracking ............................................................................. 212

Table 98. Impact of various treatments and control section on pavement performance in 

terms of RSP based on longitudinal cracking....................................................................... 214

Table 99. Impact of various treatments and control section on pavement performance in 

terms of RSP based on transverse cracking .......................................................................... 215

Table 100. Impact of bonded concrete overlays on pavement performance in terms of RFP 

based on IRI.......................................................................................................................... 221

Table 101. Impact of bonded concrete overlays on pavement performance in terms of RSP 

based on longitudinal cracking ............................................................................................. 222
xvi

Table 102. Impact of bonded concrete overlays on pavement performance in terms of RSP 

based on transverse cracking ................................................................................................ 223

Table 103. Impacts of various treatment types on RFP of the test sections based on IRI.......... 228

Table 104. Impacts of various treatment types on RFP/RSP of the test sections based on rut 

depth...................................................................................................................................... 229

Table 105. Impacts of various treatment types on RSP of test sections based on alligator 

cracking................................................................................................................................. 230

Table 106. Impacts of various treatments on pavement performance in terms of RSP based 

on longitudinal cracking ....................................................................................................... 230

Table 107. Impacts of various treatments on pavement performance in terms of RSP based 

on transverse cracking........................................................................................................... 231

Table 108. LTPP SMP partial inventory data............................................................................. 237

Table 109. The regression parameters of figure 71 for each deflection sensor.......................... 244

Table 110. Regression values for Calculation of Parameters A, B, and C in figure 73 

through figure 75................................................................................................................... 245

Table 111. Averages and overall average of the regression parameters of TAF (figure 72) 

for each climatic region ........................................................................................................ 248

Table 112. Summary of deflection for sensor 1 versus time trend descriptions......................... 249

Table 113. Backcalculated resilient modulus values.................................................................. 255

Table 114. LTPP SPS-2 partial inventory data........................................................................... 260

Table 115. Summary of deflection for sensor 1 versus time trend descriptions for 

LTPP SPS-2 .......................................................................................................................... 264

Table 116. Summary of LTE versus time trend descriptions ..................................................... 267

Table 117. Number of 0.1-mi (0.16-km)-long pavement segments and LTPP test sections 

for each treatment type available for analyses...................................................................... 275

Table 118. Comparison of the weighted average treatment benefits based on IRI of 

five treatment types performed on LTPP test sections and on pavement projects of 

CDOT, LADOTD, and WSDOT .......................................................................................... 277

Table 119. Comparison of the weighted average treatment benefits based on rut depth of 

five treatment types performed on LTPP test sections and on pavement projects of 

CDOT, LADOTD, and WSDOT .......................................................................................... 280

Table 120. Comparison of the weighted average treatment benefits based on alligator 

cracking of five treatment types performed on LTPP test sections and on pavement 

projects of CDOT, LADOTD, and WSDOT ........................................................................ 283

Table 121. Comparison of the weighted average treatment benefits based on longitudinal 

cracking of five treatment types performed on LTPP test sections and on pavement 

projects of CDOT, LADOTD, and WSDOT ........................................................................ 286

Table 122. Comparison of the weighted average treatment benefits based on transverse 

cracking of five treatment types performed on LTPP test sections and on pavement 

projects of CDOT, LADOTD, and WSDOT ........................................................................ 289
xvii

LIST OF ABBREVIATIONS

AASHTO American Association of State Highway and Transportation Officials

AC asphalt concrete

AI Asphalt Institute

CDOT Colorado Department of Transportation

CFP change in functional period

CPI Consumer Price Index

CRCP continuously reinforced concrete pavement

CS condition state

CSP change in structural period

DF dry-freeze

DNF dry-no-freeze

DRR deterioration rate reduction

DSL design service life

ESAL equivalent single-axle load

EUAC equivalent uniform annual cost

FCROP functional condition reoccurrence period

FHWA Federal Highway Administration

FWD falling weight deflectometer

GPS General Pavement Studies

HMA hot-mix asphalt

IRI International Roughness Index

JPCC jointed portland cement concrete

JPCP jointed plain concrete pavement

JRCP jointed reinforced concrete pavement

LADOTD Louisiana Department of Transportation and Development

LCCA lifecycle cost analysis

LTE load transfer efficiency

LTPP Long-Term Pavement Performance

MEPDG Mechanistic-Empirical Pavement Design Guide

NPW net present worth

ORCSE One Record Condition State Estimate

PCC portland cement concrete

PCI Pavement Condition Index

PCR pavement condition rating

PDF probability density function

PJ performance jump

PMS pavement management system

PQI Pavement Quality Index

PSI Present Serviceability Index

PSR present serviceability rating

RFP remaining functional period

RMS root mean square

RSI remaining service interval

RSL remaining service life
xviii

RSP remaining structural period

RWD rolling wheel deflectometer

SA surface age

SCROP structural condition reoccurrence period

SHRP Strategic Highway Research Program

SLE service life extension

SMP Seasonal Monitoring Program

SPS Specific Pavement Studies

SR surface rating

T

2M treatment transition matrix

TAF temperature adjustment factor

TB total benefits

TL treatment life

VOC vehicle operating costs

WF wet-freeze

WMA warm-mix asphalt

WNF wet-no-freeze

WSDOT Washington State Department of Transportation
1

EXECUTIVE SUMMARY

The efforts of the research team in this study focused on the following two objectives of the 

Federal Highway Administration (FHWA) Long-Term Pavement Performance (LTPP) Data 

Analysis Program—Expanded Strategic Plan:

(1)

• Objective 7: Quantification of the performance impact of specific design features and its 

problem statement “Common Characteristics of Good and Poorly Performing AC and

PCC Pavements.”

• Objective 9: Comprehensive use of LTPP to improve the management of pavement assets.

These two objectives were addressed and accomplished through the development of the 

following products:

• Two sets of pavement performance measures (good, fair, and poor) and (very good, good,

fair, poor, and very poor) that were used to establish dual pavement condition rating 

systems based on pavement functional and structural performance over time.

• Efficient pavement performance quantification and prediction methodologies for 

pavement maintenance and rehabilitation treatment options.

• Tools for the evaluation of the roles of pavement preservation/maintenance and 

rehabilitation in the design of long-life pavement.

This study had the following specific objectives:

• Define pavement performance in a way that supports the selection of cost-effective 

pavement treatment strategy.

• Provide better estimates of pavement treatment effectiveness and the role of pavement 

treatments in the pavement’s service lifecycle.

• Develop pavement performance prediction methodologies that are applicable to the 

pavement condition and distress data collected before and after the application of 

treatments or series of treatments.

• Analyze whether falling weight deflectometer (FWD) data can be used to indicate 

impending surface defects.

• Make recommendations for subsequent studies regarding the impacts and/or selection of 

pavement maintenance, preservation, and rehabilitation treatment options and strategies 

and their impacts on the pavement service life.

The dual pavement condition rating systems (described in chapter 3) are based on proposed 

ranges of the remaining functional period (RFP) and the remaining structural period (RSP). The 

research team used RFP, RSP, and LTPP-measured time-series pavement condition and distresses 
2

to develop efficient pavement performance quantification and prediction methodologies (see 

chapters 5 and 6). The methodologies were then used to study the benefits of various pavement 

treatments. The team calculated the pavement treatment benefits using various approaches, the 

LTPP data, and databases obtained from three State transportation departments—the Colorado 

Department of Transportation (CDOT), Louisiana Department of Transportation and 

Development (LADOTD, and the Washington Department of Transportation (WSDOT). The 

resulting benefits were used to analyze the impacts of pavement design factors (such as asphalt 

and concrete thickness and base drainage) on pavement performance. Further, the research team 

calculated and scrutinized the weighted average benefits of treatments applied to all pavement 

types in each climatic region.

To define the pavement deterioration curve with a reasonable level of certainty, all functional and 

structural data analyses were based on three or more time-series data points (all available data 

from the LTPP or State databases). The team found that the LTPP database contains fewer than 

three International Roughness Index (IRI) and/or distress data points over time for some of the 

LTPP test sections (see chapter 4 for details). Indeed, in several cases, only one data point had 

been collected between the applications of consecutive treatments. To increase the number of test 

sections that could be analyzed, the research team developed the following two procedures (see 

chapters 5 and 6):

• For asphalt concrete (AC) overlay or mill-and-fill treatments, one data point of 

0.0039 inches (0.1 mm) at 0.01 years after construction was added to the rut depth and 

cracking data. This addition facilitated the analyses of most test sections having only 

two time-series data points.

• A novel probabilistic approach was developed using the LTPP data for the estimation of 

RFP or RSP of pavement sections where only one IRI and/or one distress data point was 

available in the database. The method was referred to as the One Record Condition State 

Estimate (ORCSE) method (see chapter 5).

During the study, the research team conducted two sets of analyses. In one set, the LTPP inventory 

and pavement condition and distress data from LTPP Standard Data Release 28.0 (2014) were 

used. Results of the analyses included RFP and RSP and the treatment benefits expressed in 

various terms. In the second set, the inventory and pavement condition and distress data that were 

measured along various pavement projects by three State transportation departments (CDOT, 

LADOTD, and WSDOT) were requested, received, reviewed for compatibility with the LTPP

data, and analyzed. For each treated pavement project, the treatment benefits were calculated using 

the same parameters as those used for the LTPP data analyses. The team then compared the results 

of the analyses of the LTPP and State data. Chapter 8 details the objectives of the comparison, 

which included the following:

• Determine whether the LTPP data are representative of the State data.

• Assess whether the dual pavement condition rating systems developed in this study are 

also applicable to the State data.

• Evaluate whether the developed treatment benefit measures are applicable to the State data.
3

In this study, the measured pavement deflection data along flexible and rigid pavement test 

sections were also studied to determine whether pavement deflection could be used as an 

indicator of future conditions or surface distresses. Because the deflection data were measured at 

different times and temperatures, the data for flexible pavement test sections were adjusted to a 

standard temperature of 70 oF (21 oC) using the Asphalt Institute (AI) and other procedures. It was 

determined that existing procedures were not accurate. Therefore, a new global temperature 

correction procedure applicable to all deflections measured by the FWD sensors in the four 

climatic regions was developed (see chapter 7). The impact of this global procedure on the 

backcalculated layer moduli in flexible pavement was also assessed.

Chapter 9 summarizes the numerous conclusions the research team reached based on the results 

of the analyses. The following milestone conclusions were reached:

• The dual pavement condition rating systems developed in this study and based on ranges 

in RFP and RSP were applicable to both the LTPP and State data.

• For any data collection cycle, the pavement condition and distress data varied from one test 

section to the next. Hence, the measured pavement condition and distress data along some 

control sections were not representative of the data along the corresponding test sections.

• The flexible pavement treatment benefits calculated from the LTPP data indicated that 

drainable bases had substantial positive impact on pavement performance in the wetfreeze (WF) region, and drainable bases did not add benefits in the other three climatic 

regions (wet-no-freeze (WNF), dry-freeze (DF), and dry-no-freeze (DNF)).

• Comparable test sections located in the four climatic regions did not perform the same 

with regard to IRI; rut depth; or alligator, longitudinal, or transverse cracking. Sections 

located in the WF region performed the worst compared with test sections located in the 

other three climatic regions.

• The impact on pavement performance in the WF region could be lessened by increasing 

the thickness of the AC and/or improving drainage.

• Thin overlay treatment had little to no impact on pavement performance in terms of

alligator, longitudinal, and transverse cracking. Reflective cracking appeared in a short 

time period after the application of thin overlay.

• The condition and distress of the pavement sections before treatment affected the 

treatment longevity. The worse the pavement condition and distress was before treatment, 

the shorter the expected service period of the treatment was.

• The LTPP Seasonal Monitoring Program (SMP) deflection data did not support the AI 

temperature correction procedure.

• A new and innovative algorithm was developed to adjust the measured deflection data to 

a standard temperature of 70 oF (21 oC). The new algorithm applied to all deflection 

sensors and in all climatic regions.
4

• No consistent trends in the pavement deflection were observed over time. Hence, 

inclusion of deflection data in the algorithm of the dual pavement condition rating 

systems was not appropriate.

• Deflection data measured using the FWD could be used neither as an indicator of future 

pavement condition or distress nor to develop threshold values for the analysis of the 

pavement RFP and RSP.

• The methodologies used in the analyses of the LTPP data were also successfully applied 

to the State data. These methodologies were computerized using MATLAB® computer 

programs and Microsoft® Excel spreadsheets for formatting and organizing the results.

• The major difference between the LTPP and the State data was that, for each pavement 

condition and distress type, the LTTP database contained one data point per test section 

for every data collection cycle. In contrast, for a given pavement project, the State

databases contained as many data points as the number of 0.1-mi (0.16-km)-long 

pavement segments along the project.

• The LTPP-measured IRI and distress data were similar and representative of the Statemeasured IRI and distress data in terms of magnitude and variability.

• The treatment benefits calculated using the LTPP data were parallel to the benefits of 

similar treatments calculated using the State data.

• The treatment benefits calculated using the LTPP data could be used as benchmarks for 

State transportation departments to check the performance of their pavement treatments 

and to assist them in conducting lifecycle cost analyses.

Based on the results of these analyses and the conclusions, the research team makes the 

following recommendations:

• Adopt the new dual pavement condition rating systems based on ranges in RFP and RSP 

as national standard measures to classify pavement condition and performance in a way 

that supports the selection of cost-effective pavement treatment strategy.

• Embrace the pavement performance prediction methodologies as national standard 

methodologies to unify and standardize the assessment of pavement performance and 

pavement treatment effectiveness.

• Conduct further research studies to incorporate the new dual pavement condition rating 

systems and the pavement performance prediction methodologies in the lifecycle cost 

analyses to optimize short- and long-term pavement treatment strategies.

• Conduct studies to determine the factors causing variability in the measured pavement 

condition and distress data and develop procedures to minimize their effects.
5

• Measure, at minimum, three sets of pavement condition and distress data over time 

before treatment.

• Initiate studies to establish automated quality control and assurance procedures for data 

collection and storage that minimize the impact of subjective factors. These studies may 

include the newly developed self-powered wireless macro-sensors that can be embedded 

in pavement and transportation infrastructures)

• Fund a study to determine the most efficient data collection frequency based on treatment types 

(such as thin and thick overlay, chip seal, and so forth) and their expected service periods.

7

CHAPTER 1. INTRODUCTION

This final report of the study entitled Pavement Performance Measures and Forecasting and the 

Effects of Maintenance and Rehabilitation Strategy on Treatment Effectiveness presents the 

findings and recommendations of the research team. The study was based on the following three 

groups of objectives:

• Group A—Condition State (CS): Address the classification of the CS (good, fair, or 

poor) of the various pavement segments and types based on their identified and measured 

functional condition (ride quality and safety) and structural integrity (rut depth and 

cracking) and their rates of deterioration

• Group B—Treatment Effectiveness: Address the developed methodology used to 

estimate the effectiveness of each pavement treatment type and combinations of 

treatments based on the time-series pavement condition and distress data. The series of 

pavement maintenance and rehabilitation treatments required to improve the longevity of 

the pavement structures and hence the pavement lifecycle costs were evaluated.

• Group C—Roles of Pavement Treatments: Document the roles of pavement 

maintenance and rehabilitation treatments in the design of long-life pavements through 

quantification of the pavement RFP and RSP.

Analyses of the pavement conditions, distresses, and other data were conducted to support each 

objective group. The data used in the analyses were obtained from Standard Data Release 28.0 of 

the LTPP database published in January 2014 and from three State transportation departments—

CDOT, LADOTD, and WSDOT. The data for all LTPP treatment types performed on test 

sections within the Specific Pavement Studies (SPS) (SPS-1 through -7) and General Pavement 

Studies (GPS) (GPS-6, -7, and -9) experiments were downloaded and organized for analyses. For 

some treatments, the before-treatment and the after-treatment pavement condition and distress 

data of each test section were modeled and analyzed to do the following:

• Determine the treatment benefits.

• Assess the impacts of the applied treatment strategies on the pavement performance. 

The study objectives were formulated to perform analyses and make recommendations in support 

of objectives 7 and 9 of the Long-Term Pavement Performance (LTTP) Data Analysis 

Program—Expanded Strategic Plan.

(1) The following specific LTPP goals and objectives were 

addressed and accomplished in this study:

• Performance measures of various distress indicators for different pavement types were 

established. The measures were used to develop pavement condition classification 

systems based on good, fair, and poor ratings. 

• Efficient pavement performance prediction methodologies for pavement maintenance and 

rehabilitation options were developed to characterize series of pavement maintenance and 

rehabilitation activities applied over the life of a pavement system. Recommendations for 
8

incorporating the methodologies into subsequent studies on maintenance and 

rehabilitation timing and strategy selection were also made.

• The roles of pavement preservation/maintenance and rehabilitation in the design of longlife pavement and quantification of the remaining service life (RSL) of cracked or 

damaged pavements were addressed. 

During the study, pavement condition and distress data and other inventory and treatment data 

from the LTPP experiments and from three State transportation departments (CDOT, LADOTD, 

and WSDOT) were obtained, organized, and analyzed to do the following:

• Define the pavement performance in a way that supports the selection of cost-effective 

pavement treatment strategy. 

• Provide better estimates of pavement treatment effectiveness and the role of pavement 

treatments in the pavement’s service lifecycle. 

• Develop pavement performance prediction methodologies applicable to the pavement 

condition and distress data collected before and after the application of treatments or 

series of treatments. 

• Analyze whether FWD data could be used to indicate impending surface defects.

• Make appropriate recommendations for subsequent studies regarding the impacts and/or 

selection of pavement maintenance, preservation, and rehabilitation options and strategies 

and their impacts on the pavement service life.

Consequently, the contents of this final report are organized in the following nine chapters:

• Chapter 1. Introduction: This chapter describes the objectives of the study and outlines 

the contents of the report.

• Chapter 2. Literature Review: This chapter includes a detailed literature review 

conducted in support of phases I and II of the study. The literature review addresses the 

state of the practice of various State transportation departments and the methodologies 

used in their analyses and interpretations of the pavement performance data. This chapter 

also provides a detailed review of previous analyses of the LTPP data and their 

consequent findings and recommendations as they relate to this study.

• Chapter 3. Pavement Condition Classification: This chapter presents the newly 

developed dual pavement condition rating systems.

• Chapter 4. Data Mining and Synthesis: This chapter summarizes the number of LTPP 

test sections and the number of applied treatments for which the LTPP database has 

sufficient time-series data to conduct the analyses in phase II. 
9

• Chapter 5. LTPP Data Analyses of Flexible Pavements: This chapter summarizes the 

results of the analyses of the LTPP flexible pavement data.

• Chapter 6. LTPP Data Analyses of Rigid Pavements: This chapter summarizes the 

results of the analyses of the LTPP rigid pavement data.

• Chapter 7. FWD Deflection Data Analyses: This chapter summarizes the results of the 

analyses of the LTPP deflection data

• Chapter 8. State Data Analyses: This chapter details and discusses the methodologies 

used in the analyses and the results of the analyses of the pavement performance data 

obtained from three State transportation departments—CDOT, LADOTD, and WSDOT. 

• Chapter 9. Summary, Conclusions, and Recommendations: This chapter summarizes 

the report, presents the conclusions, and makes recommendations regarding practices 

going forward as well as suggestions for future studies.

Readers should note detailed data and results from this study are available through LTPP Customer 

Support Services by telephone at (202) 493–3035 or by email at ltppinfo@fhwa.dot.gov.

11

CHAPTER 2. LITERATURE REVIEW 

The research team conducted an extensive literature review in support of this study. The review 

focused on various topics, including the following:

• Pavement distress severity levels.

• Threshold values.

• Definitions and methodologies used to determine good, fair, and poor pavement 

conditions. 

• Effectiveness of various pavement maintenance and rehabilitation treatments and their 

predicted and measured performance.

• Selection of pavement preservation and rehabilitation strategies and their impacts on the 

pavement service life.

• Advantages and shortcomings of pavement treatment benefits, including the RSL concept.

• LTPP experimental design and the in-place pavement sections.

• Research findings from previous studies of the LTPP data.

PAVEMENT DISTRESS SEVERITY LEVELS 

The LTPP and the majority of State transportation department pavement distress data are 

collected based on three severity levels: low, medium, and high. The distress severity rating can 

be problematic because it is a function of the judgment of the surveyor who is observing the 

pavement or, in the case of many State transportation departments, the surveyor who is 

reviewing and digitizing the electronic pavement surface images. Such judgment is a function of 

the degree of training and experience of the surveyors. Further, the same pavement segment may 

not be reviewed by the same surveyor each year or each data collection cycle. In addition, the 

crack severity level is a function of the crack opening, which is a function of the pavement 

temperature at the time of data collection. Thus, a crack may be labeled “high severity” in 

one year and medium severity the next year or vice versa. Figure 1 and figure 2 depict an 

example of the time-series data for each transverse crack severity level for LTPP test section 

A330 of the SPS-3 experiment in California. 
12

1 ft = 0.305 m.

Figure 1. Graph. Time-series transverse cracking data for each severity level and the sum 

of all severity levels for LTTP SPS-3 test section A330 in California.

1 ft = 0.305 m.

Figure 2. Graph. Cumulative time-series transverse cracking data showing individual 

transverse crack severity level and the sum of all severity levels for LTTP SPS-3 test 

section A330 in California.
13

The data in the two figures indicate the following:

• The length of transverse cracks for any given severity level changes from one year to the 

next without the application of any pavement treatment. To illustrate, the length of the 

low severity transverse crack in figure 1 is about 197 ft (60 m) in year 1, 82 ft (25 m) in 

year 5, 49 ft (15 m) in year 7, more than 328 ft (100 m) in year 11, and 197 ft (60 m) in 

year 13. The medium severity crack length is approximately 33 ft (10 m) in year 1,131 ft 

(40 m) in year 7, only about 16 ft (5 m) in year 11, and about 82 ft (25 m) in year 13. 

Finally, the length of the high severity transverse cracks is about 230 ft (70 m) in year 6, 

164 ft (50 m) in year 8, 427 ft (130 m) in year 11, and 377 ft (115 m) in year 13.

• The variability of the crack lengths of the three severity levels could be attributed to 

several reasons, the three most important of which are the following:

o The pavement temperature at the time of data collection influences the data. Higher 

temperature causes the crack width to decrease, resulting in an observed change in 

severity level. This problem cannot be addressed unless the pavement temperature is 

measured during the survey, and an accurate temperature-dependent crack width 

model is developed. Note that the LTPP surveyors do collect pavement temperature 

data during a survey while most State transportation departments collect temperature 

data only on a limited basis.

o The pavement surveyor judges and labels some cracks as low severity in one survey 

year and medium or high severity in subsequent survey year. This inconsistency 

could be addressed through computerized crack-rating quality control and/or 

enhanced observer training.

o The high variability of the individual severity levels does not allow accurate modeling 

of the crack propagation over time. In fact, the data indicate that the medium and 

high-severity transverse crack lengths decrease and then increase over time without 

any pavement treatment. A previous study sponsored by the FHWA expressed the 

pavement cracking data as the sum of the three severity levels.(2) This yielded much 

less data variability, as evidenced by the exponential model of the total transverse 

crack length shown in figure 1.

The crack severity level data could be used to roughly estimate the amount of work needed to 

preserve the pavement section. For example, cracks in the medium- and high-severity levels need to 

be sealed or patched. Low-severity cracks are typically not sealed or patched. For rigid pavements, 

low-severity transverse cracks may be subjected to dowel bar retrofit, while medium-and highseverity cracks typically are not.(3) Similar patterns can be found in the State transportation 

departments’ cracking data as shown in figure 3 and figure 4 for locations along a portion of 

Highway 24 in Colorado.
14

1 ft = 0.305 m.

Figure 3. Graph. Time-series transverse cracking data for each severity level and the sum 

of all severity levels, Highway 24, direction 2, BMP 329.9, in Colorado.

1 ft = 0.305 m.

Figure 4. Graph. Cumulative time-series transverse cracking data showing individual 

transverse crack severity level and the sum of all severity levels, Highway 24, direction 2, 

BMP 329.9, in Colorado.
15

ENGINEERING THRESHOLDS (CRITERIA)

Some State transportation departments express pavement conditions and distresses using one or 

more of the following methods (see figure 5 and figure 6):(4,5)

• A descriptive scale, such as very good, good, fair, poor, and very poor.

• A distress index based on a continuous rating scale (i.e., 0 to 10 or 0 to 100). One end of 

the scale defines failed pavement, and the other end defines excellent pavement condition 

and/or no distress, such as in the new pavement shown in figure 5. Some State 

transportation departments use the rating scale to calculate one distress index for each 

type of distress (i.e., individual distress indices), while others use a composite pavement 

index. A composite index is typically based on several types of distress and/or condition. 

Examples of composite pavement indices include Pavement Condition Index (PCI), 

Pavement Quality Index (PQI), overall pavement index, and so forth.

• Along the rating scale, one or more threshold values are typically established to flag 

pavement sections for possible treatment actions. One threshold value could be based on 

the need for maintenance, another on the need of preservation action, and a third could be 

based on rehabilitation. Depending on the functionality of the threshold value 

(maintenance, preservation, or rehabilitation), a distress index value below the 

established threshold value indicates the need to maintain, preserve, or rehabilitate the 

pavement section in question. The rehabilitation threshold value typically separates 

acceptable from nonacceptable pavement conditions.
16

1 mi = 1.61 km.

1 ft = 0.305 m.

Figure 5. Chart. Rating and descriptive scales and distress points.
17

Figure 6. Graph. Descriptive rating scale for pavement condition.

It is important to note that if the threshold value is established based on engineering criteria, the 

pavement condition rating will be such that the relative condition of the pavement segment is 

constant for a given condition. The engineering criterion should be selected based on the 

experience of the transportation department and should address the extent of the condition or 

distress at which the pavement section in question is deemed in need of repair (maintenance, 

preservation, or rehabilitation) within the constraints of the department. An example of 

engineering criterion for transverse cracking could be 600 ft (183 m) of cracking or 50 transverse 

cracks (crack spacing of about 10.5 ft (3.2 m) along a 0.1-mi (0.16-km)-long asphalt pavement 

segment). Based on the engineering criterion, distress points can be assigned to each occurrence 

of the distress (each transverse crack) and the rating scale threshold value. To illustrate, consider 

the continuous rating scale of 0 to 100 (100 indicates no transverse cracks) and its threshold 

value of 60 points as shown in figure 5. An engineering criterion of 50 transverse cracks per 

0.1 mi (0.16 km) implies that the asphalt pavement score is 60 (it loses 40 distress points) when 

the pavement segment accumulates 50 transverse cracks. Based on a linear accumulation of 

distress points, each transverse crack is worth 0.8 distress points.(4) If the agency decided to 

change the threshold value from 40 to 50 but to maintain the engineering criterion of 

50 transverse cracks, then 50 cracks would cause the pavement section to lose 50 distress points 

and each crack would be worth 1 distress point. Stated differently, the engineering criteria for 

establishing the threshold value should be based on the extent of the distress rather than a 

number on the rating scale.

Finally, the engineering criteria express the conditions of the pavement and could be based on 

the user or the agency. Examples of roadway user-based criteria are ride quality (IRI) and rut 

depth. Examples of agency-based criteria are cracking and faulting. One other factor to note is 

that the engineering criteria for certain distress or condition types could be global or could be 

established based on pavement class, traffic volume, regional needs, and so forth. Nevertheless, 
18

the methods used to develop the engineering criteria should be well documented, and the criteria 

should be studied and calibrated as more pavement condition and distress data become available.

Many State transportation departments, such as the LADOTD, the CDOT, the Michigan 

Department of Transportation, and the WSDOT, have developed engineering criteria for each 

distress type and severity level. Examples of such criteria for alligator cracking and the 

associated deduct points are listed in table 1.

(6)

Table 1. Engineering criteria and deduct points for alligator cracking.(6)

Severity

Alligator Cracking Deduct Points Extent (ft2)

0–51 51–701 701–1,301 1,301–2,401 2,401–3,168 > 3,168

Low 0 1–16 16–21 21–25 25–28 28

Medium 0 1–21 21–29 29–36 36–49 48

High 0 1–29 29–43 43–50 43–61 61

1 ft2 = 0.093 m2.

PAVEMENT DISTRESS AND CONDITION INDICES

Pavement distress or condition indices are often based on one or more condition or distress types. 

For example, the Alligator Cracking Index (an individual index) is based on the severity levels 

(low, medium, and high) and the extent of the alligator cracks, whereas a combined pavement 

distress or condition index (such as PCI) consists of two or more condition or distress types. The 

combined index expresses the sum of the distress points assigned to each distress or condition 

type and severity level divided by the number of pavement segments (see figure 7). Hence, a 

combined pavement distress index expresses the average pavement condition and not the actual 

condition based on individual distress types.(4,7)

Figure 7. Equation. DI.

Where:

DI = Distress index.

DP = Sum of the distress points along the project.

N = Number of 0.1-mi (0.16-km)-long segments along the project (N = L/0.1).

L = Project length in mi (km).

Finally, the distress points or the pavement condition indices do not express the true nature of the 

pavement conditions. For example, immediately after construction, the cumulative distress points 

of a pavement project subjected to a 2-inch (51-mm) asphalt overlay are exactly the same as the 

cumulative distress points for another project subjected to a 6-inch (152-mm) asphalt overlay. 

The pavement surface conditions of both projects are free of distresses. Stated differently, neither 

the distress points nor the condition indices express the design life of the overlay or the impact of 

the type of pavement preservation or rehabilitation on the pavement service life. Further, the 

differences between the distress points and the pavement distress index before and after 

treatment cannot and should not be used to express the benefits of the applied pavement 

DI =

 DP

N

= 

0.1

L

 DP 

Σ
19

maintenance or rehabilitation treatments. Consider three pavement sections having the same 

distress points and distress index that were subjected to 2-, 4-, and 6-inch (51-, 102-, and 

152-mm) asphalt overlays, respectively. The differences in the distress points and distress index 

before and after treatment were exactly the same although the costs of the overlays were 

substantially different and so were their design service lives (DSLs) and future pavement 

performance. The DSL of the treatment and the pavement rate of deterioration must be 

accounted for in the calculation of the true benefits of the treatments.(2)

DESCRIPTIVE PAVEMENT CONDITIONS 

Descriptive terms (such as good, fair, and poor) are also used to express the various categories of 

the pavement conditions. Although the terms hide important details, they are universal and easily 

communicated to legislators and the general public. The three terms are typically based on the 

pavement appearance and/or ride quality at the time of rating. Descriptive classifications of good, 

normal or fair, and poorly performing AC and portland cement concrete (PCC) pavements were 

previously addressed in four FHWA reports published in 1998, 1999, 2011, and 2012. (See 

references 8–11.) The shortcomings of the first two reports are that the descriptive terms are based 

on the last collected pavement condition data as shown in figure 8. Figure 9 depicts the actual 

time-series IRI data for three in-service pavement sections located in the State of Washington. 

Over the 10-year period, these sections were not subjected to any pavement treatment. Figure 9

clearly shows the following:

• Data along a 2.4-mi (3.9-km)-long pavement segment of road 2 indicate that the 

descriptive term changed from good to fair to poor in only 3 years. Thus, the descriptive 

terms do not accurately reflect the true pavement performance.

• Data along a 3.6-mi (5.8-km)-long pavement segment of road 3 changed performance 

descriptions over time from poor to fair (labeled normal in the reports) and then to good.

• Data along a 4.8-mi (7.7-km)-long pavement segment of road 1 show that the pavement 

description fluctuated between good and fair and then between fair and poor. Once again, 

such descriptive terms do not reflect the true in-service pavement performance over time.

• After construction, Roads 1 and 2 were considered good, and then at the elapsed time of 

4 years, the description of road 1 was fair while road 2 was poor. The pavement rate of 

deterioration was not reflected in the characterization.

The three descriptive terms could be improved to better express the pavement conditions if they 

were based on the pavement’s rates of deterioration.
20

1 inch/mi = 0.0158 m/km.

Figure 8. Graph. Pavement condition classification system.

1 inch/mi = 0.0158 m/km.

Figure 9. Graph. Shortcomings of the recommended classification system when dealing 

with real but good data (not the worst-case scenario).

40

90

140

190

240

290

0 2 4 6 8 10

IRI (inch/mile)

Elapsed time (year)

Poor

Fair

Good

Threshold
21

On the other hand, the latter studies describe the terms good, fair, and poor and their potentially 

associated treatment categories as follows:(11)

• Good: Pavement infrastructure that is free of significant defects and has a condition that 

does not adversely affect its performance. This level of condition typically requires only 

preventive maintenance activities.

• Fair: Pavement infrastructure that has isolated surface defects or functional deficiencies. 

This level of condition typically could be addressed through minor rehabilitation, such as 

overlays and patching of pavements that do not require full-depth structural improvements.

• Poor: Pavement infrastructure that is exhibiting advanced deterioration and conditions 

that affect structural capacity. This level of condition typically requires structural repair, 

rehabilitation, reconstruction, or replacement.

Once again, the significant issue with these types of definitions is that they do not consider the 

changes in conditions and distresses over time. The terms do convey the current conditions of a 

pavement well, but pavement health is not best demonstrated by a snapshot in time. The 

pavement conditions and distresses generally deteriorate with time, and the pavement health 

depends on the current conditions and the rates of deterioration over time. The specific terms 

could still serve their purpose, but the criteria used to assign the rating should be modified to 

account for the effects of time. Consideration of condition and rates of deterioration facilitates 

planning and pavement management, while condition alone has limited use as a tool for 

managing pavement unless the deterioration rate (curve) is estimated based on the available data.

PAVEMENT PERFORMANCE MODELING AND PREDICTION

The performance of a pavement segment is often illustrated by the progression of pavement 

condition or distress over time, as shown in figure 10. The level of performance at any given 

time is equivalent to the level of pavement condition or distress at that time compared with the 

threshold value. Therefore, the performance of a pavement segment over its service life is 

defined by the level of service over time or by the accumulation of damage over time.(12)
22

1 inch/mi = 0.0158 m/km.

Figure 10. Graph. A typical pavement performance curve.

Most State transportation departments collect pavement condition and distress data. Some use 

the data to observe the condition of the pavement, while others use the time-series pavement 

condition and distress data to predict future pavement conditions. The combination of both 

practices allows the development of current and future strategies for management of the 

pavement network.

Many State transportation departments have studied the effectiveness of various pavement 

treatments using historical pavement performance data. Based on the various studies, the 

minimum and maximum treatment service lives listed in table 2 were published in the various 

sources listed for each treatment type. These estimated averages are adequate to be used in the 

analysis at the network level. For project-level analysis, more accurate estimates are required. 

Such estimates could be based on predictions of past and future pavement conditions through the 

modeling of pavement condition and distress data before and after treatment to create pavement 

performance curves.
23

Table 2. Estimated and reported pavement treatment service life.

Treatment Type Reference

Estimated Treatment Service Life 

Expectancy (Years)

Minimum Average Maximum

Thin (< 2.5-inch) hot-mix 

asphalt (HMA) overlay

16–22 2 8 12

Thick (≥ 2.5-inch) HMA overlay 23 6 10 17

Single chip seal 16–18, 24, and 25 1 6 12

Double chip seal 17, 18, 21, 24, and 

26

4 9 15

Thin (< 2.5-inch) mill and fill 21 and 23 4 8 20

Thick (≥ 2.5-inch) mill and fill 23 6 10 17

Cold-in-place HMA recycling 17, 26, and 27 5 10 20

Crack sealing 16 and 17 2 3 10

Microsurfacing 16, 18, 20, 22, 28, 

and 29

4 6 10

1 inch = 25.4 mm.

Several predictive pavement performance models have been developed to estimate the pavement 

performance curve based on parameters such as traffic, weather, and pavement type. These models

include straight-line extrapolation, regression, polynomial constrained least squares, application of 

Shaped curves, use of probability distributions, and Markov chain models.(13) One such example, 

for thin HMA overlays, is presented in figure 11. The parameters were determined for different 

performance indicators (IRI, pavement condition rating (PCR), and rut depth) as well as different 

road types (interstate, noninterstate highway, and nonhighway).(14)

Figure 11. Equation. PI.

Where:

PI = Performance indicator for a pavement segment in a given year.

CAATT = Cumulative average annual daily truck traffic (in millions) predicted for the pavement 

segment from the time of treatment to the given year.

CAFDX = Cumulative annual freeze index (in thousands of degree-days) predicted from the time 

of treatment to the given year.

1, 2, and 3 = Statistical parameters.

The most common method for modeling pavement condition and distress data as a function of 

time is by ordinary least squares regression. It should be noted that a minimum of three timeseries data points are required to model the nonlinear data. The method used to determine the 

parameters of the selected mathematical function (see figure 12 through figure 14) consists of 

minimizing the sum of squared errors. This method works when the data of the particular 

pavement segment indicate deterioration over time. If the method does not capture the 

progression of condition or distress over time, other models may be required.(15)

β

PI = e

β1+β2×CAATT+β3×CAFDX

β β β
24

Figure 12. Equation. RD.

Figure 13. Equation. IRI.

Figure 14. Equation. Crack.

Where:

RD = Rut depth.

, , , , k, , and = Regression parameters.

Crack = Crack length, area, or percent.

t = Elapsed time in years.

Such models can be used to estimate the time until a certain threshold value is reached. Threshold

is the prespecified condition or distress level indicating unacceptable pavement condition or 

distress has been reached and that the pavement segment is in need of maintenance, preservation, 

or rehabilitation depending on the level at which the prespecified threshold value is set.

Another method of modeling pavement condition and distress data is the clusterwise regression 

procedure, which was introduced by Spath and later modified by others. (See references 15 and 

30–35.) Clusterwise regression involves splitting the data into subgroups based on their 

characteristics and fitting separate models to each subgroup. The resulting pavement performance 

models could be more accurate because they model small subsets of data with similar trends. 

However, the resulting models are discrete (each model represents a certain time period).

PAVEMENT PRESERVATION BENEFITS

Most procedures for estimating pavement preservation benefits are based on the prediction of 

future pavement performance, comparison of the pavement performance before and after 

treatment, and immediate changes in the pavement conditions resulting from treatment. Although 

the commonality among all procedures is a prespecified threshold value, the actual value of the 

threshold varies from one procedure to another. Some procedures set one threshold value for each 

type of pavement distress and condition, while others use the combined distress index and an 

overall threshold value. Still others use one threshold value for maintenance, one for preservation, 

and one for rehabilitation and/or reconstruction. Further, some procedures use the term life to 

express the benefits. Such a term should not be taken separately from service life. For example, the 

term pavement design life used by the American Association of State Highway and Transportation 

Officials (AASHTO) Guide for Design of Pavement Structures, in reality, expresses the pavement 

DSL.

(36) The pavement design itself is based on the following threshold values:

• The 1993 AASHTO Design Guide specifies a threshold value based on the Present 

Serviceability Index (PSI) of 2.5 (which is equivalent to an IRI of about 200 inches/mi

RD = γt

ω

IRI = αe

βt

Crack =

k

1 + exp −θ t − μ 

α β γ ω θ μ
25

(3.16 m/km)).(36) The threshold value expresses the minimum ride quality standard. A 

pavement at or below a PSI of 2.5 is providing the user with substandard ride quality. The 

ride quality could be restored by many preservation and/or rehabilitation actions (such as 

thin or thick overlays). Stated differently, when the prespecified threshold is reached, it 

implies that the minimum standard is reached while the pavement is not in need of 

reconstruction. If the threshold value, on the other hand, is set at the reconstruction limit, 

then reconstruction is needed.

• The new AASHTO Guide for Mechanistic-Empirical Pavement Design of New and 

Rehabilitated Pavement Structures (referred to as the MEPDG) is based on one threshold 

value for ride quality (IRI), one for rut depth, one for faulting, and one for each type of 

cracking, similar to that listed in table 3.

(37)

Table 3. Threshold values that could be used in the AASHTO MEPDG.

Pavement 

Condition or 

Distress Type Threshold Value Explanation

Alligator cracking 1,267 ft2

/0.1 mi 

(73 m2/0.1 km )

Twenty percent of lane area cracked 

(assuming 12-ft (3.66-m) lane width)

Longitudinal 

cracking

1,056 ft/0.1 mi 

(200 m/0.1 km )

Two cracks along the entire section 

length

Transverse cracking 

(Jointed concrete 

pavements)

396 ft/0.1 mi 

(50 m/0.1 km )

Two thirds of the slabs are cracked 

(assuming 16-ft (4.88-m)-long slab)

Transverse cracking 

(HMA)

350 ft/0.1 mi 

(67 m/0.1 km )

Crack spacing = 12 ft (3.66 m)

Faulting 0.25 inches, average over 0.1 mi 

(6.35 mm, average over 100 m )

Dowel bars have likely sheared or 

concrete around dowels has 

deteriorated and may be spalled

Nevertheless, various procedures were developed for estimating treatment benefits. The 

following are some of these procedures:

• RSL: RSL is the estimated number of years from any given year (usually from the last 

condition survey year) to the date when the conditions or distresses of the pavement section 

reach a prespecified threshold value. It is very important to note that the prespecified 

threshold values could be set at the level when the pavement is in need of maintenance, at 

the pavement preservation level, or at the major rehabilitation or reconstruction level. It is 

strongly recommended that the prespecified threshold value be set by the roadway owner to 

support its pavement preservation system, such as those recommended by the AASHTO 

MEPDG or listed in table 3.

(37) At these levels, a given pavement section requires major 

rehabilitation, and the RSL is zero. State transportation departments can also establish the 

RSLs or the ranges of RSL at which pavement preservation and pavement maintenance can 

be applied. The reason for this recommendation is that the cost of pavement maintenance, 

preservation, or rehabilitation at the specified ranges of RSLs could be included in the 

analyses. This recommendation can be illustrated as follows. Suppose the threshold value 
26

for longitudinal cracking is set at 1,056 ft/0.1 mi (200 m/0.1 km) as listed in table 3. When 

a pavement section reaches the threshold value, the RSL is 0 and the cost of preservation is 

much higher than the cost of an earlier preservation when the RSL is 5 years. Further, when 

the RSL is 10 years or more, the cost of preservation is much lower yet and it may be 

limited to the cost of the required maintenance treatment only. Thus, the transportation 

department could establish a continuous scale of costs versus RSLs such as that shown in 

figure 15.

1 lane-mile = 1.61 lane-km.

Figure 15. Graph. Relationship between RSL and cost of managing pavements.

For each pavement section, the following steps are required for calculating the RSL:

o Download from the database the pavement surface age and all consecutive pavement 

condition and distress data points collected over a time period where no treatment 

was applied.

o Use the condition and distress data points and the corresponding data collection times 

to obtain the equation of the best fit curve using the proper mathematical function.

o Input to the best fit equation the threshold value of the condition or distress in 

question and calculate the time in years between construction and the time when the 

pavement condition will reach the preestablished threshold value. The RSL is the 

difference between the calculated time and the pavement surface age.

In the case of a new pavement structure or a newly rehabilitated pavement, the estimated 

RSL must be positive and restricted to be less than or equal to the DSL of the pavement 

or the DSL of the treatment, as stated in figure 16.

(4) The reason is that for a few years 

after treatment, the pavement may or may not show any distress, and hence, the estimated 
27

RSL is very large and meaningless. The restriction could be dropped when a significant 

number of data points indicating pavement deterioration are available. At that time, the 

RSL could be greater or less than the DSL, and the information could be used as 

feedback to the pavement design and construction processes.

Figure 16. Equation. RSL.

Where:

t (PC = Th) = Time (the number of years) at which the pavement condition reaches the 

threshold value (Th).

SA = Pavement surface age in years.

DSL = Pavement design service life in years.

The important point is that the RSL does not advocate worst-first as perceived by a few 

people. It is a management tool that allows State transportation departments to manage 

their pavement asset based on engineered criteria and a long-term preservation program. 

It should be noted that the accuracy of RSL is a function of the accuracy and variability 

of the pavement condition and distress data. In addition, the accuracy of the estimated 

RSL decreases as the value of that RSL increases (i.e., predicts much further out in time).

The RSL of a given pavement network can be calculated as the weighted average RSL of 

the total number of pavement sections, n, within the network using figure 17:

Figure 17. Equation. RSLnetwork.

Where:

i = ith pavement segment.

n = Total number of pavement segments or sections in the network.

RSL = Remaining service life.

SL = Segment length.

It should be noted that any pavement segment that falls below the threshold value has an

RSL of zero. In general, no negative RSL should be assigned to any pavement regardless 

of its condition. For a newly designed and constructed or rehabilitated pavement segment, 

its RSL is equal to the design life.(4)

• Remaining service interval (RSI): RSI is similar to RSL but with some differences. RSI 

is a new pavement performance measure, which, at the time of this report, was being 

analyzed on another research study sponsored by FHWA. The final algorithm of the RSI 

was not used in the current study because it was not available during the study.

0 ≤ RSL = t PC = Th − SA ≤ DSL − SA 

RSLnetwork =

 RSLi

 SLi 

n

i=1

 RSLi SLi 

n

i=1
28

• Service life extension (SLE): SLE is the gain in service life resulting from a pavement 

treatment, as shown in figure 18.

(5) The accuracy of SLE is a function of the accuracy of 

the two estimated RSLs before and after treatment. SLE is a useful tool for determining 

the time benefit resulting from a pavement treatment.

1 inch/mi = 0.0158 m/km.

Figure 18. Graph. Schematic of the definition of SLE.

• Treatment life (TL): TL is the time between the treatment date and the date when the 

pavement conditions or distresses reach the lesser of the threshold value or the before 

treatment pavement condition or distress, as shown in figure 19.

(5) TL involves the same 

limitations as the predicted RSL after treatment (note that the TL does not require any 

RSL prediction before treatment), except with a shorter prediction in time. TL is a good 

tool to determine the time until the before treatment conditions return. In the case of 

worse pavement condition or distress after treatment, the TL is taken as the negative of 

the time for the before-treatment conditions or distresses to reach the after-treatment 

conditions, as shown in figure 20.

(5)
29

1 inch/mi = 0.0158 m/km.

Figure 19. Graph. Schematic of the definition of TL.

1 inch/mi = 0.0158 m/km.

Figure 20. Graph. Schematic of the definition of negative TL.

• Total benefits (TB): TB is the ratio of the benefit area to the do-nothing area, as depicted 

in figure 21.

(22) TB accounts for the improved condition over a given time, the area bound 

by the performance curve and a threshold value; however, it has some significant flaws. 

TB can be misunderstood because two pavement sections can have the same area ratio 
30

but completely different performance. Stated differently, any ratio can be obtained by the 

division of an infinite set of two numbers such as 1 and 3, 2 and 6, 6 and 18, and so forth. 

The different perspective of the TB is that the benefit area is normalized relative to the 

do-nothing area. The do-nothing area, on the other hand, is a function of the pavement 

conditions and rate of deterioration before treatment.

Figure 21. Graph. Schematic of the definition of TB.(22)

• Performance jump (PJ): PJ is the immediate improvement in the pavement condition 

resulting from treatment.(38) PJ indicates the temporary improvement resulting from 

treatment but has no way to predict the future conditions or how long the improvement 

will last. An example of PJ is depicted in figure 22.

• Deterioration rate reduction (DRR): DRR (see figure 22) is the change in deterioration 

rate from immediately before to immediately after treatment.(38) The measure is short

term and therefore is not a true measure of performance.
31

1 inch/mi = 0.0158 m/km.

Figure 22. Graph. PJ and DRR.

PAVEMENT TREATMENT TYPES

The number of available pavement treatment types is large and ever growing as new techniques 

and materials are developed. Each State transportation department has a group of treatments it 

chooses to apply based on its experience and the results achieved over time. The selection of a 

particular pavement treatment from this pool of options is often specific to each State 

transportation department. The next section provides a discussion of the selection process.

PAVEMENT TREATMENT SELECTION

Many State transportation departments have developed plans and methodologies for selecting 

pavement treatments. The most common are decision trees and matrices. These are often 

developed from past experience and tend to focus on one or two options. An example of a 

decision tree is shown in figure 23 and its reference table 4, and an example of a matrix is shown 

in table 5. The values in table 4 indicate the trigger values corresponding to roadway functional 

classifications for use in figure 23. The table contains trigger values based on PQI, present 

serviceability rating (PSR), and surface rating (SR). These trees/matrices are rarely updated and 

often neglect new technology. Nonetheless, they are typically based on the following data:(17)

• Pavement surface type and/or construction history and environmental conditions.

• An indication of the functional classification and/or traffic level.

• At least one type of PCI, including distress and/or roughness. More specific information 

about the type of deterioration present, either in terms of an amount of load-related 

deterioration or the presence of a particular condition or distress type.

• Geometric data indicating whether pavement widening or shoulder repair are required.
32

Figure 23. Illustration. Example of decision tree for continuously reinforced concrete pavement (CRCP).
33

Table 4. Trigger values for functional classifications.

Functional Classification Trigger Value

PSR SR PQI

Rural principal interstate 3.0 2.7 3.0

Rural principal arterial 3.0 2.7 2.9

Rural minor arterial 2.8 2.5 2.8

Rural major collector 2.8 2.5 2.6

Rural minor collector 2.8 2.5 26

Rural local 2.7 2.4 2.6

Urban interstate 3.1 2.7 3.0

Urban principal arterial freeway 3.1 2.7 2.9

Urban principal arterial 2.8 2.5 2.9

Urban minor arterial 2.7 2.4 2.8

Urban collector 2.6 2.4 2.6

Urban local 2.5 2.4 2.6
34

Table 5. Example of decision matrix.

Problem

Possible Cause Maintenance Rehabilitation

Structural failureMix compositionTemperature or moisture changesConstructionPatching & routine maintenanceFog sealSurface treatmentSlurry sealSurface recyclingThin overlayOpen-graded surfaceStructural overlayStructural recyclingReconstruction

Alligator cracking X — — — X — X X — — — X X X

Edge joint cracks X — X X X — — — — — — — — —

Reflection cracks — — — — X — X X — — X X X —

Shrinkage cracking — X X — — — X X X — X X X —

Slippage cracks — — — X X — — — — — — — — —

Rut depth X X — X — — — — X X — X X X

Corrugation X X — X — — — — X X — X X X

Depressions X — — X X — — — — — — — X X

Upheaval — — X — X — — — — — — — X X

Potholes X — X X X — — — — — — X — —

Raveling — X — X — X X X X X — — — —

Flushing asphalt — X — X — — X — X — X — — —

Polished aggregate — X X — — — X — X X X — — —

Loss of cover 

aggregate

— X — X — — X — — — — — — —

— Indicates not applicable.
35

Pavement Preservation Costs

This subsection reviews pavement preservation costs and lifecycle cost analyses (LCCAs). 

Owing to a lack of available cost data required for detailed analyses, no such analyses are 

included in this report. However, the information is provided to assist in such analyses if more 

data become available.

The costs of any pavement treatment can be divided into two categories: agency costs and user 

costs. The agency cost is the physical cost of the pavement project, including design and 

construction less the residual value of the pavement section at reconstruction. This is often 

referred to as direct costs.(39) User costs are much more difficult to estimate than agency costs 

because they are not based on specific monetary value but on vehicle operating costs (VOC), 

delay costs, and accident costs. The three types of user costs and how they relate to normal and 

work zone conditions are listed in table 6 and discussed in the next few subsections.(40)

Table 6. Review of user cost components.

Component Normal Operation Work Zone Conditions

VOC Based on total delay-hours and 

driving cost caused by 

accidents

Based on total delay-hours caused by 

work zone and accidents in the work 

zone

Delay Total delay-hours caused by 

accidents

Total delay-hours (caused by work zone 

and accidents in work zone)

Accidents Number and severity of 

accidents

Number and severity of work zone 

accidents

One problem that arises when estimating user costs is the transformation of delay, accidents, etc.,

to a monetary value.(41) Some believe that user costs should be defined as “user benefit” and 

expressed qualitatively as improvements in performance or safety.(42,43) The user benefits of one 

treatment compared with another or with the do-nothing alternative could be used to choose 

between treatments with similar agency costs. In other words, if two treatment options satisfy the 

pavement needs and have similar agency costs, then the deciding factor would be the user costs. 

This would greatly simplify the process, which is often considered complicated and deficient, 

especially when applicable data are not available for the various detailed user cost models.(44)

However, LCCA should be completed to evaluate both the agency and user costs over the 

pavement lifecycle and to select the most cost-effective treatment strategy. For completion, these 

costs and LCCA are discussed in the next few subsections.

LCCA

Recently, State transportation departments have faced many constraints, including public demand 

for quality pavement and budget shortfalls. Hence, the considerations become the following:

(45)

• What pavement preservation alternatives should be used, and how often should a given 

pavement section be preserved?

• How many miles of each pavement class should be preserved annually?
36

• What is the optimum time or the optimum pavement conditions and distresses at which 

pavement preservation actions should be taken?

• What are the associated agency and user costs and benefits of each pavement treatment?

• What are the optimum and most cost-effective short- and long-term pavement 

preservation strategies that can be applied to keep the pavement network healthy in a 

cost-effective manner?

These questions cannot be properly and accurately answered unless LCCA is conducted. 

Such analysis should address the agency and the user costs and must be based on accurate 

and up-to-date data so that the costs and benefits of various pavement preservation 

alternatives can be compared.

The Need for LCCA

In general, highway pavements are designed and constructed to provide services for a limited 

time called the “service life.” Over time, the combined effects of traffic loads and environmental 

factors accelerate the pavement deterioration and reduce its level of serviceability. Maintenance, 

preservation, and rehabilitation treatments are designed and applied to pavement sections to slow 

their rates of deterioration and to extend their service lives. The application of most pavement 

treatments requires traffic control (lane closures and/or detours), which significantly affects 

traffic flow, increases travel time, and increases VOC. The costs and benefits of pavement 

treatments are composed of many elements, including the following:

• Agency costs of the pavement treatment, which consist of many attributes, including

the following:

o Material and contractual costs.

o The cost of traffic control in the work zone, which is defined as an area along the 

highway systems where maintenance and construction operations adversely affect the 

number of lanes open to traffic or affect the operational characteristics of traffic flow 

through the work zone.(46)

o Quality assurance and quality control costs.

o Costs of future treatments.

• Agency benefits, which could be measured by the service life of the treatment or the SLE 

of the treated pavement sections.

• User costs, which are composed of many attributes, including the following:

(47,48)

o Time delay user costs or work zone user costs, which are defined as the associated 

costs of time delays due to lane closures because of roadway construction, 

rehabilitation, and maintenance activities.(49)
37

o Costs incurred by those highway users who cannot use the facility because of either 

agency or self-imposed detour requirements.(50)

o VOC in terms of fuel, wear and tear, and depreciation over the delay periods.

o Accident costs.

o Environmental costs resulting from air pollution caused by excessive use of gasoline 

or diesel fuel owing to lower speed and time delay, including noise pollution.

• User benefits, which are composed of improved serviceability and ride quality that would 

lower the VOC and improve traffic flow.

Methods of LCCA

Because LCCA considers all planned pavement treatments of a given analysis period, the service 

life and the value of money over time should be considered. One hundred dollars in 2014 likely 

bought much more than $100 will in 2024. Hence, the following two common methods are 

incorporated in LCCA to account for this:

• Net present worth (NPW): NPW or net present value is a common economic indicator. 

NPW is the monetary value of an action accounting for the transformation of the value of 

money over time using the discount rate (see figure 24). The use of NPW allows fair 

comparison of actions taken at different times by converting to a common unit of 

measure.(50)

Figure 24. Equation. NPW.

Where: 

NPW = Net present worth.

N = Total number of preservation treatments.

i = Discount rate.

n = Number of years into the future.

k = Preservation action number.

The discount rate reflects the rate of inflation adjusted to the opportunity cost to the 

public. The opportunity cost is often indicated by a comparison with the discount rate of 

the conservative U.S. Treasury bill. The historical inflation rate trend from 1999 to 2014 

indicates a range of –0.35 to 3.58 percent with an average of 2.39 percent. Table 7 lists 

common discount rates used by State transportation departments in the 1990s. The 

discount rate should reflect historical trends in the nation or region where the analysis is 

conducted.(50) Alternatively, the discount rate could be determined from the Consumer 

NPW = initial cost + Preservation Costk

 

1

 1 + i 

nk

 

N

k=1
38

Price Index (CPI). The average CPI discount rate from 2001 to 2010 was about 

2.54 percent.

(45,51)

Table 7. Historical discount rates.1

Year

Analysis Period (Years)

3 5 7 10 30

1992 2.7 3.1 3.3 3.6 3.8

1993 3.1 3.6 4.0 4.3 4.5

1994 2.1 2.3 2.5 2.7 2.8

1995 4.2 4.5 4.6 4.8 4.9

1996 2.7 2.7 2.8 2.8 3.0

1997 3.2 3.3 3.4 3.5 3.6

1998 3.4 3.5 3.5 3.6 3.8

Average 3.1 3.3 3.4 3.6 3.8

1Effects of discount rates on $100 from 2014 to 2024 using the average CPI of 2.54 percent. One hundred 

dollars in 2014 has the same purchasing power as $128 in 2024.

• Equivalent uniform annual cost (EUAC): The EUAC method is also widely used as a 

common economic indicator in LCCA and is typically derived from NPW as calculated 

in figure 25. The use of either value allows fair comparison of actions taken at different 

times by converting to a common unit of measure.(50)

Figure 25. Equation. EUAC.

Where: 

EUAC = Equivalent uniform annual cost.

NPW = Net present worth

i = Discount rate.

n = Number of years into the future.

PAVEMENT PRESERVATION EFFECTIVENESS

In the past, some State transportation departments and almost all local road agencies allowed 

their pavement assets to deteriorate to levels requiring major rehabilitation or reconstruction. For 

many years, their treatment policies were based on a worst-first policy in which severely 

deteriorated pavement sections were subjected to preservation treatments while the condition of 

the rest of the pavement network continued to deteriorate. Recently, many State transportation 

departments have initiated and implemented comprehensive pavement preservation programs at 

the entire road network level.

The programs are based on cost-effective treatment of sections of the pavement network in relatively 

good condition to restore their conditions, decrease their rates of deterioration, and enhance the 

safety of the motorists. Over time, the preservation program becomes a part of the annual pavement 

EUAC = NPW 

 1 + i 

n

 1 + i 

n − 1

 
39

treatment strategy of the State transportation department.

(52) The pavement preservation program 

typically consists of light pavement treatments, such as crack sealing, nonstructural overlay, light 

rehabilitation actions, mill and fill, and so forth. The alternative to pavement preservation is the old 

practice of letting the pavement network deteriorate until expensive rehabilitation or reconstruction 

actions are necessary. Several studies have been conducted on the effectiveness of pavement 

preservation and are summarized in the following subsections.

The effectiveness of pavement treatments can be measured in the short term and/or the long

term. Short-term benefits are defined by the immediate improvement to the pavement conditions 

and rates of deterioration, while long-term benefits are defined over the service life of the 

pavement section by the performance and extension in service life. The costs can also be short

term (individual treatment) or long term (LCCA).

Pavement Preservation Cost Effectiveness at the Project Level

Pavement preservation can be applied through a series of pavement treatments over the 

pavement lifecycle (a treatment strategy). The alternative to pavement preservation is allowing 

the pavement to deteriorate until reconstruction is required, the worst-first or do-nothing 

scenario. The cost effectiveness of pavement preservation at the project level can be quantified 

using LCCA. The analysis could be conducted on the various alternative pavement preservation 

treatments that could be applied to a pavement section over time and on the do-nothing scenario 

followed by reconstruction. Comparison of the results from the various strategy analyses 

indicates the cost savings or extra expenditures of performing preservation over the life of the 

pavement segment.

The cost effectiveness of pavement preservation at the project level has been well documented.

Most literature agrees that pavement preservation can be conducted at minimal cost and create 

major savings over the life of the pavement. One study found that the cost savings of pavement 

preservation was as high as $5 saved for every $1 spent on preservation.(53) Another reports 

savings of $4 to $10 for every $1 spent on preservation.(54) Other benefits include improving ride 

quality and creating a pavement network with consistent needs from year to year.(55)

Pavement Preservation Effectiveness at the Network Level

The effectiveness of pavement preservation at the network level is more difficult to quantify than 

at the project level. Funds designated for preservation reduce the funds available for 

rehabilitation and reconstruction. In other words, pavement preservation is thought to decrease 

the lifecycle cost of a given pavement project, but the effect on the network is often unknown. In 

addition, the public and legislators may not understand why pavements in seemingly good 

condition are being treated, while others in poor conditions are not. State transportation 

departments should document and communicate the effects of preservation maintenance on the 

health of the pavement network and on the lifecycle cost in a clear and consistent manner. 

Educating the public and the legislature is necessary to establish and maintain a successful 

pavement preservation program.(55)

The short- and long-term benefits and effectiveness of pavement preservation at the project and 

network levels should be quantified. Short-term benefits include improving ride quality and 
40

addressing safety issues, while long-term benefits (cost savings) are not realized until years or 

decades into the future. Therefore, pavement preservation strategies must be optimized through 

projection of needs and funds into the future. In this way, the effects of performing or deferring 

various pavement projects can be evaluated.(55,56)

TREATMENT TRANSITION MATRIX (T2M)

Pavement treatment effectiveness is often described with a single value or a range of values, such 

as 5 to 10 years gained or an average 7-year service life. The probabilities of the various results 

are not typically reported. The probabilistic effectiveness of treatments can be quantified and 

communicated using an innovative matrix format called T

2M.(3) T2M shows the following:

• Distribution of the pavement CSs before and after treatment.

• Transitions of the pavement CSs from before treatment to after treatment resulting from

the treatment.

• List of the treatment benefits.

• Long-term results of the pavement treatment. 

Table 8 shows an example of a T

2M that lists the results of single chip seal applications in 

Colorado. The cells display this information in the following convenient way:

• Columns A through D list the following before treatment information: pavement CSs 

(RSL bracket numbers), RSL ranges, and the number and percent of 0.1-mi (0.16-km)-

long pavement segments in each before treatment CS.

• Columns E through I list the following after treatment information: CSs (RSL bracket 

numbers), RSL ranges, the number of 0.1-mi (0.16-km)-long pavement segments 

transitioned from the given before treatment CS to each after treatment CS, and the total 

number of 0.1-mi (0.16-km)-long pavement segments transitioned to each after 

treatment CS.

• Columns J through L list the following pavement treatment benefits: average TL, SLE, and 

after treatment RSL of all 0.1-mi (0.16-km)-long pavement segments transitioned from a 

given before treatment CS to all after treatment CSs; and the overall average TL, SLE, and 

after treatment RSL. 
41

Table 8. T2M for single chip seal in Colorado.

A B C D E F G H I J K L

Condition/Distress Type: Condition/Distress Causing the Minimum RSL Before and After Treatment

Before Treatment Data

After Treatment Data

CS or RSL Bracket Number and Range in 

Years and Number of the 0.1-Mi 

(0.16-Km)-Long Pavement Segments 

Transitioned From Each Before Treatment 

RSL Bracket to the Indicated After 

Treatment RSL Brackets

Treatment Benefits in Terms 

of TL, SLE, and RSL of the 

Treatment (Years)

CS or 

RSL 

Bracket 

Number

RSL 

Bracket 

Range 

(Years)

0.1-Mi 

(0.16-Km)-Long 

Pavement 

Segments

1 2 3 4 5

0 to 2 3 to 5 6 to 10 TL SLE RSL

11 to 

15

16 to 

Number Percent 25

1 0 to 2 2,329 58 125 453 1,230 267 254 4 8 9

2 3 to 5 746 18 3 88 379 121 155 3 7 11

3 6 to 10 365 9 1 52 157 55 100 2 4 12

4 11 to 15 141 3 0 8 52 27 54 2 1 14

5 16 to 25 452 11 1 24 128 55 244 1 –5 15

Total 4,033 100 130 625 1,946 525 807 — — —

Average — — — — — — — 3 6 10

— Not applicable.

Bold indicates the number of pavement segments where no change in the CS resulted from the treatment.
42

PRESERVATION TIME SELECTION

The effectiveness of pavement treatments is often determined simply based on the benefits 

gained, as mentioned previously. The benefits, however, do not indicate effectiveness relative to 

cost, which is the main constraint for all State transportation departments. Most literature agreed

that treatments applied to pavements in better condition produce more benefits, and the cost of the 

treatment was a function of the conditions.(38,57,58) Further, the time-value relationship of money 

affects the cost of the treatment and its cost effectiveness. Therefore, benefits must be compared 

relative to costs to determine the cost effectiveness of the treatment and to select the treatment 

timing.(41) Performing pavement treatments at the optimum time provides the greatest benefit-tocost ratio. The idea of optimum timing is not new; in fact, the concept was built into the 

AASHTO 1993 design guide.(36) Few methodologies for the determination of optimum treatment 

timing for preventive maintenance and rehabilitation actions were developed.(22) This 

methodology is designed to optimize treatment timing based on the treatment benefit (calculated 

by the area under the performance curve). However, the most cost-effective treatments should 

consider the pavement longevity and should be based on the ratio of dollars to years of service.(3)

To improve lifecycle costs, State transportation departments should base their preservation 

strategies on maximizing the longevity of the pavement network rather than maximizing the 

condition of the network.

THE LTPP PROGRAM

The LTPP Program was established under the Strategic Highway Research Program (SHRP) in 

1987. Since 1991, FHWA has managed and funded the LTPP Program. The program houses 

two fundamental groups of experiments: SPS and GPS. The LTPP Program has addressed 

myriad studies of pavement-related issues ranging from validation of pavement design 

procedures to traffic and material variability, and pavement maintenance, preservation, and 

rehabilitation actions. The conclusions of these studies are documented in countless publications 

in the forms of research reports, product briefs, and techbriefs, which have substantially 

contributed to the development of advanced pavement technology and highlighted the 

importance of the LTPP Program and its associated database.

OBJECTIVES AND SCOPE OF THE LTPP PROGRAM

The overall objective of the LTPP Program is to collect, store, and make various data elements 

relating to pavement performance available to researchers, scientists, and the general public. 

These include the pavement structures and conditions, traffic volume and load, and 

environmental conditions for various pavement sections located along the existing North 

American highway systems. Over a more than 20-year period, the data have been used by 

researchers, practitioners, and other stakeholders to assess the long-term performance of 

pavements under various loading and environmental conditions and with different structural and 

material compositions. The specific established objectives of the LTPP Program include the 

following:

(59)

• Evaluate the existing pavement design methodologies.
43

• Develop improved design methodologies and strategies for the rehabilitation of 

existing pavements.

• Develop improved design equations for new and reconstructed pavements.

• Determine the effects of loading, environment, material properties and variability, 

construction quality, and maintenance levels on pavement condition, distress, and 

performance over time.

• Determine the effects of specific design features on pavement performance.

• Establish a national long-term pavement database with detailed information suitable for 

various assessments and studies.

LTPP TEST SECTIONS

The LTPP Program consists of about 2,500 500-ft (152.4-m)-long, mostly in-service test 

sections located in all 50 U.S. States, Puerto Rico, and 10 Canadian provinces. The test sections 

are divided between the two main studies, SPS and GPS. Some of the SPS test sections were 

reconstructed to investigate certain pavement engineering factors, while others were specially 

preserved to study the impacts of some preservation treatments. In contrast, the GPS test 

sections consist of sections of existing roads that were subjected to various typical maintenance 

and preservation treatments. Thus, eight types of existing in-service pavements make up the 

GPS and are monitored throughout North America. More details on the SPS and GPS test 

sections can be found throughout the remainder of this report.

SPS

SPS is a long-term program designed to study specifically constructed, maintained, or 

rehabilitated pavement sections incorporating controlled sets of experimental design and 

construction features. The main objective of the SPS experiments is to provide more detailed 

and complete sets of data to extend and refine the results obtained from the GPS experiments. 

There are nine SPS experiments grouped by the five categories listed in table 9.

(59)
44

Table 9. The SPS categories and experiments.

Category Experiment Title

Pavement 

structural factors 

SPS-1 Strategic Study of Structural Factors for Flexible 

Pavements 

SPS-2 Strategic Study of Structural Factors for Rigid Pavements 

Pavement 

maintenance 

SPS-3 Preventive Maintenance Effectiveness of Flexible 

Pavements

SPS-4 Preventive Maintenance Effectiveness of Rigid Pavements 

Pavement 

rehabilitation 

SPS-5 Rehabilitation of Asphalt Concrete (AC) Pavements 

SPS-6 Rehabilitation of Jointed Portland Cement Concrete 

(JPCC) Pavements 

SPS-7 Bonded Portland Cement Concrete (PCC) Overlays of 

Concrete Pavements 

Environmental 

effects 

SPS-8 Study of Environmental Effects in the Absence of Heavy 

Loads

Asphalt aggregate 

mixture 

specifications 

SPS-9P Validation and Refinements of Superpave Asphalt 

Specifications and Mix Design Process 

SPS-9A Superpave Asphalt Binder Study 

SPS-10 Warm-Mix Asphalt (WMA)—in design stage

The SPS experiments involve monitoring the newly constructed pavement sections and the 

existing pavement sections that were subjected to maintenance or rehabilitation treatments after 

assignment to the SPS. The SPS is divided into various SPS experiments numbered SPS-1 

through -9. Each experiment includes multiple test sites, and each test site contains between

2 and 12 pavement test sections depending on the experiment. Following the original 

assignment of test sections in 1992, numerous supplemental test sections were constructed by 

different State transportation departments to study aspects of particular interest to them.(59)

FHWA is initiating new sites for the study of WMA (SPS-10) and is currently considering new 

pavement preservation experiments in addition to the existing SPS experiments. 

GPS

GPS is also a long-term program designed to study a series of experiments on selected inservice pavement structures with the objective of establishing a national pavement performance 

database. Pavement sections believed to be built with proper materials and good engineering 

design were selected as part of the GPS program.

(59)

The pavement structures included in the GPS were constructed or reconstructed up to 15 years 

before the start of the LTPP Program. Unfortunately, detailed data were often not available for the 

period between the original construction time and the time when they were selected for the LTPP 

Program. However, it was believed that some beneficial insights might be drawn without this 

data. Finally, some SPS test sections have been reclassified as GPS test sections upon the 

application of rehabilitation treatments. Table 10 lists the titles of each of the GPS 

experiments.(59)
45

Table 10. The GPS experiments.

Experiment Title

GPS-1 Asphalt Concrete (AC) Pavement on Granular Base 

GPS-2 AC Pavement on Bound Base 

GPS-3 Jointed Plain Concrete Pavement (JPCP) 

GPS-4 Jointed Reinforced Concrete Pavement (JRCP) 

GPS-5 Continuously Reinforced Concrete Pavement (CRCP) 

GPS-6A Existing AC Overlay of AC Pavement (existing at the start of the program) 

GPS-6B AC Overlay Using Conventional Asphalt of AC Pavement—No Milling 

GPS-6C AC Overlay Using Modified Asphalt of AC Pavement—No Milling 

GPS-6D AC Overlay on Previously Overlaid AC Pavement Using Conventional 

Asphalt 

GPS-6S AC Overlay of Milled AC Pavement Using Conventional or Modified 

Asphalt 

GPS-7A Existing AC Overlay on Portland Cement Concrete (PCC) Pavement 

GPS-7B AC Overlay Using Conventional Asphalt on PCC Pavement 

GPS-7C AC Overlay Using Modified Asphalt on PCC Pavement 

GPS-7D AC Overlay on Previously Overlaid PCC Pavement Using Conventional 

Asphalt 

GPS-7F AC Overlay Using Conventional or Modified Asphalt on Fractured PCC 

Pavement 

GPS-7R Concrete Pavement Restoration Treatments With No Overlay 

GPS-7S Second AC Overlay, Which Includes Milling Or Geotextile Application, on 

PCC Pavement With Previous AC Overlay 

GPS-9 Unbonded PCC Overlay on PCC Pavement 

Summary of Previous Findings

In this study, previously published reports regarding the LTPP Program and data analyses were 

scrutinized. The topics of these reports include the effects of design factors on pavement 

performance measures and the selection of appropriate and cost-effective treatment type. 

However, the findings of these reports did not adequately address the relationships between the 

maintenance and rehabilitation actions and the performance of the various pavement sections or 

the optimal timing for treatment application. Nevertheless, some of the relevant reported 

findings are enumerated and summarized in the following subsections.

Impacts of Pavement Treatment on Pavement Performance

This subsection summarizes reported findings related to the impacts of pavement treatments on 

the collected pavement condition and distress data of various LTPP experiments.

For the SPS-3 experiment, the following findings were reported:

• Thin asphalt overlay was found to be the most effective maintenance treatment followed 

by chip seal and slurry seal treatments in terms of roughness, rut depth, and fatigue 

cracking.(60)
46

• Crack sealing had no significant effect on long-term roughness, rut depth, or 

fatigue cracking.(60)

• Crack sealing had only marginal impact on longitudinal and transverse cracking. This is 

mainly because sealed cracks are counted as separate distresses in the LTPP distress 

survey procedures.(61)

For the SPS-4 experiment, the findings were inconsistent. Some researchers reported the 

following findings:

• Sealed joints performed better than unsealed joints, while other researchers reported that 

there were no significant differences between sealed and unsealed joints.(61–63)

• Silicone joint sealant materials performed better than compression seals and hot pours in 

terms of the overall failure (adhesion loss and joint spalling).(28)

• The lack of a significant quantity of data is a drawback in the analyses. Survey 

measurements of sealed joints/cracks were collected for 34 test sites, while undersealed 

test section data were available for only 10 sites.(63)

For the SPS-5 experiment, the following findings were reported:

• Thick overlays performed better than thin overlays with respect to transverse and 

fatigue cracking.(63)

• Inconsistent results were reported for longitudinal cracking, rut depth, and IRI. Some 

researchers reported that there was no apparent effect of thick and thin overlays on rut 

depth or IRI, while others reported that thicker overlays provided better IRI.(63–65)

• Virgin and recycled HMA used in overlays were found to have no significant impact on 

transverse, longitudinal, or fatigue cracking, rut depth, or IRI.(60,64) On the other hand, it 

was reported that recycled HMA performed better than virgin HMA with respect to 

fatigue and transverse cracking in dry climates and/or low traffic roadways.(63) Further, 

virgin HMA performed better than recycled HMA with respect to rut depth.(63)

• The type of pavement surface preparation performed before overlay had no significant 

effect on rut depth or IRI.(60,64)

• Inconsistent results were reported regarding the effects of pavement surface preparation 

before overlay on long-term cracking performance. It was reported that intense and 

minimal pavement surface preparations made no significant difference in long-term 

cracking performance, whereas others stated that intensely prepared pavement sections 

performed better than minimally prepared sections with respect to fatigue and 

longitudinal cracking.(61,63)
47

For the SPS-6 experiment, the following findings were reported:

• The 8-inch (203-mm) AC overlay was the most effective rehabilitation option followed by 

the 4-inch (102-mm) AC overlay and concrete pavement restoration with and without 

diamond grinding.(60) However, on the contrary, it was reported that rehabilitation strategies 

without AC overlays were best to mitigate the crack initiation and propagation.(63)

• Pavement rut depth on composite pavement was independent of overlay thickness, preoverlay repairs, and mixture type.(60) 

• No significant difference in long-term cracking performance was detected among 

the following:

(60)

o Test sections subjected to minimal versus intensive pre-overlay preparation. 

o Test sections with and without sawed and sealed joints. 

o Test sections with 4-inch (102-mm) overlays, with sawed-and-sealed joints, and 

cracked and seated sections. 

o Test sections with 4- and 8-inch (102- and 203-mm) overlays.

• Fractured PCC test sections with an AC overlay performed better in roughness than 

those nonfractured test PCC sections subjected to the same AC overlay. Further, the 

nonfractured sections that were subjected to AC overlay performed better than 

nonfractured PCC sections that were subjected to diamond grinding and patching 

without AC overlay.(66)

• Pavement roughness was independent of whether the pavement sections were subjected 

to sawing and sealing before the AC overlay.(60)

Impacts of Design Variables on Pavement Performance

The findings of various studies regarding the impacts of various design factors on pavement 

performance are summarized in the next six subsections.

Climatic Variables

One study suggested that dowel bars should be used in JPCP to reduce joint faulting in WF

climates.(8)In dryer climates, the joint spacing should be reduced to decrease transverse 

cracking potential due to high thermal gradients. This is because the precipitation has the 

following two effects on the pavement material temperatures:

• Precipitation cools or heats the pavement surface relative to the subsurface temperature, 

thereby reducing the difference in temperature with depth.
48

• Water generally requires much more energy to change temperature than air, binder, and 

aggregate materials. Therefore, a higher water content or more frequent saturation 

reduces the magnitude and rate of heating and cooling of the pavement layers.

In addition, IRI was found to be higher for similar pavements located in colder and wetter 

climates than those in other climates. Further, higher initial roughness led to higher rates of 

deterioration. The researchers stated that the results should be reviewed with caution because 

the PCC durability was not included in the analysis, which might have affected the results.(8)

One study indicated that faulting in undoweled JPCP test sections in DF regions was similar to 

those sections in DNF regions.(67) The mean faulting values were 0.126, 0.079, 0.063, and 

0.039 inches (3.2, 2.0, 1.6, and 1.0 mm) in the WF, WNF, DF, and DNF regions, respectively. 

On the other hand, the doweled JPCP test sections showed no significant differences in joint 

faulting between the WF and the WNF regions. Doweled joint faulting occurred mostly in the 

DF regions, followed by the DNF and the WF regions. Test sections in the WNF regions 

showed the lowest faulting values.

On the other hand, an initial evaluation of SPS-2 test sections indicated that for doweled joints 

in rigid pavements, faulting was most prevalent in the DF region, followed by the DNF, and the 

WF regions.(68)In addition, the total longitudinal crack length was found to be longer in the 

DNF region, followed by the DF and the WF regions.

A strong relationship was reported between IRI and climatic conditions for flexible 

pavements.(69) Higher roughness was measured in pavement sections located in areas with 

higher precipitation, higher freezing index, and/or higher number of freeze-thaw cycles. The 

researchers also stated that adequate frost protection was an important factor for good pavement 

performance. In hot climates, roughness values were strongly related to the number of days with 

temperatures above 90 °F (32 °C). In addition, the roughness was lower for pavement sections 

in hot regions with higher precipitation than for those with less precipitation. The researchers 

related this finding to the cooling effect that precipitation may have on asphalt pavements, 

thereby reducing deformations resulting from high temperatures. On the other hand, rigid, 

jointed pavements were found to have higher roughness in climates with higher precipitation.

Roadbed Soils

One study indicated that better subgrade support (higher backcalculated modulus of subgrade 

reaction, k-value) resulted in fewer transverse cracks with deteriorated edges and in lower 

roughness (IRI) for JPCP, JRCP, and CRCP.(70)

Another study concluded that PCC pavements constructed over fine-grained roadbed soils had 

higher joint faulting than those constructed on coarse-grained roadbed soils.(8) This was likely due 

to increased soil erosion and reduced water permeability. Likewise, JPCP sections constructed on 

coarse-grained roadbed soils had lower IRI than those constructed on fine-grained roadbed soils. 

The researchers recommended that a thick layer of granular material be placed and compacted 

beneath the aggregate base course to improve drainage and reduce faulting, especially for 

undoweled pavements. The study also concluded that PCC slabs supported on strong foundations, 

such as stabilized bases or granular roadbed soils, often had a lower initial roughness.
49

A study based on the SPS-8 experiment data found that the most prevalent early pavement 

distress was longitudinal cracking outside the wheelpath.(71) Further, this distress was most 

commonly observed in sections located in the WF region and on an active roadbed soil (frostsusceptible or swelling soils due to freeze-thaw cycles). It was also observed that flexible and 

rigid pavements constructed on active roadbed soils had the highest mean initial roughness 

values and the highest rates of deterioration compared with pavements constructed on fine and 

coarse-grained roadbed soils. This study were in agreement with other studies that a good 

working platform such as stabilized base and granular subgrade contributed to a smoother 

pavement after construction.(8,70)

Joint Load Transfer

A common finding from a few studies was that the presence of dowel bars had a significant 

impact in reducing joint faulting.(8,67,70)In fact, after 10 years in service, JPCP sections with 

dowel bars showed 50 percent less joint faulting than those without dowel bars. In wet and/or 

freeze climates, the use of dowel bars appeared to negate the effects of cold temperatures and 

increased moisture that could often lead to erosion and pumping of fines. It was also found that 

the use of doweled joints could have more impact on pavement performance than design 

features such as subdrainage, tied-concrete shoulders, and joint spacing.(67) Further, it was found 

that properly sized dowel bars could eliminate corner breaks and transverse cracking near the 

joints as well as minimize joint faulting.(72)

In an FHWA report, the impacts of various parameters on the variability of the load transfer, as 

quantified by the load transfer efficiency (LTE) measured over time, were documented.(73) The 

researchers reported the following findings:

• For undoweled JPCP joints, the variability of LTE along a pavement section was 

inversely correlated to the average LTE. As the average LTE of a pavement section 

increased, the variability decreased. 

• The LTE variability was not affected by joint spacing, base type, or shoulder type (PCC 

or AC).

• The LTE variability was higher in pavements with subsurface drainage systems than in 

pavements without subsurface drainage systems.

• The variability of the average LTE for pavements with granular roadbed soils was 

higher than that of pavements with silty clay roadbed soils.

• The variability of the average LTE was not affected by the amount of annual precipitation, 

the number of annual freeze-thaw cycles, or the average mean annual temperature.

• The variability of the average LTE decreased as the annual freezing index increased.

• No direct relationship was found between pavement age and the variability of the 

average LTE measurements over time.
50

• The variability of the average LTE was higher in pavements with tied concrete shoulders 

than in pavements with an asphalt shoulder.

Drainage

In a National Cooperative Highway Research Program Research Results Digest, it was stated 

that for properly designed doweled joints in JPCP, joint faulting was fairly low, and permeable 

bases had relatively small effect on reducing joint faulting.(74) Edge drains were found not to 

have a significant effect on joint faulting when dense-graded bases were used. For undoweled 

JPCP, joint faulting in general was higher, and permeable bases had a significant effect in 

reducing joint faulting. However, the permeable bases should be designed and maintained to 

reduce or eliminate the migration of fines from the lower materials. Similarly, slab cracking was 

found to be reduced in pavements constructed on asphalt-treated permeable bases. D-cracking 

was also found to be less prevalent in pavement sections constructed on permeable bases, likely 

because of reduced base saturation and introduction of water and various compounds into the 

concrete slab. Note that all of these findings were based on limited data. 

Base Type

In separate studies sponsored by FHWA, it was reported that JPCP constructed over a stabilized 

base had less faulting and smoother surfaces than those constructed with an untreated aggregate 

base, especially in undoweled JPCP.(8,75)JPCP with an asphalt-stabilized base or lean concrete 

base had significantly lower initial roughness when compared with other base materials. In 

addition, JPCP sections constructed with granular and asphalt-stabilized bases had significantly 

lower percentages of cracked sections than JPCP with cement-treated or lean concrete bases. 

The cracking was not associated with an increased roughness.

In an LTPP Program sponsored study using the SPS-2 experiment data, it was found that 

pavement sections with permeable asphalt-treated bases developed the fewest transverse and 

longitudinal cracks.(76) On the other hand, pavement sections with lean concrete base developed 

the most transverse and longitudinal cracks during the first 10 years of pavement service life. 

This confirmed earlier findings.(8,75)

Slab Width 

Two studies concluded that increasing slab width by 2 ft (0.6 m) reduced faulting of concrete 

pavements by reducing the critical deflections at the corner of the slab from heavy truck 

axles.(8,67) The mean faulting data for undoweled sections (10 years old or less) indicated about 

50 percent less faulting with a widened slab. It was stated that this outcome agreed with 

previous non-LTPP field performance data. No difference was found between the faulting of 

doweled widened slab sections and doweled conventional-width JPCP. However, JPCP sections 

with widened lanes did not show any transverse cracking. In addition, the initial evaluation on 

SPS-2 data revealed that widened slabs have less initial roughness.(76)

SUMMARY, CONCLUSIONS, AND RECOMMENDATIONS

This chapter presented a review of the state of the practice of various State transportation 

departments with regard to several aspects of pavement condition and distress data analyses and 
51

treatment selection. A summary of the results of previous studies conducted using the LTPP 

database was also included. The following topics were covered:

• Pavement distress severity levels.

• Pavement condition and distress descriptions.

• Pavement performance modeling and treatment benefit calculations.

• Treatment types and selection.

• Preservation costs and LCCA.

• Effectiveness of pavement treatments at the project and network levels.

• Pavement treatment time selection.

• LTPP Program and its objectives.

• SPS and GPS test sections of the LTPP Program.

• A summary of previous findings, including the impacts of pavement treatments and 

various design factors on pavement performance.

The various topics were evaluated, and the research team reached the following conclusions and 

made the following recommendations regarding execution of the current study:

• The pavement cracking data were typically collected and stored based on three severity 

levels (low, medium, and high). For most cases, the data could not be analyzed because 

of the high data variability from one year to the next. Therefore, it was recommended 

that the sum of crack lengths or crack areas of all severity levels be used in this study for 

time-series modeling.

• Various methods exist to rate pavement conditions based on threshold values, indices, 

and descriptive terms. It was recommended to rate pavement based on its conditions and 

rates of deterioration over time to facilitate planning and pavement management. The 

dual condition rating systems described in chapter 3 address this issue.

• Pavement performance could be established and estimated over time through modeling 

the collected condition and distress data. The performance curves could be used to 

estimate future conditions and the time period to reach certain threshold values. This 

methodology was recommended for use in this study.

• The benefits of pavement treatments could be quantified in several ways based on 

changes in conditions, rates of deterioration, and a combination thereof. In this study, 

the treatment benefits were calculated based on the dual condition rating systems 

detailed (see chapter 3).
52

• Numerous pavement treatments could be selected to address the pavement conditions. 

The selections could be based on the conditions, the rates of deterioration, the causes of 

distress, or combinations thereof. It was recommended to select treatments that 

addressed most pavement conditions and their causes.

• Pavement treatment costs consist of agency and user costs. The costs of various 

pavement treatments and series of treatments (treatment strategy) could be evaluated 

using LCCA. 

• Pavement treatment cost effectiveness could be evaluated at the project and network 

levels. It was recommended to perform LCCA at the project level and strategy 

optimization at the network level to improve the overall cost effectiveness of the 

pavement asset management.

• For pavement projects that received the same treatment type, T2Ms could be developed 

to display the distribution of the pavement conditions before and after treatment and to 

estimate the treatment effectiveness and the selection of the optimum treatment time. In 

this study, T2Ms were populated using the State data.

• The SPS and GPS experiments were designed such that pavement treatments were 

applied to pavement sections representing an assortment of different structures located 

in different environments and subjected to varying traffic levels. In this study, all treated 

LTPP test sections that had adequate time-series pavement condition and distress data 

were analyzed to evaluate the effectiveness and the impacts of the treatments on 

pavement performance.

• Numerous LTPP studies were evaluated that dealt with the different impacts of 

treatments on pavement conditions and distresses. Few studies considered pavement 

conditions and rates of deterioration over time. It was recommended to include the 

before and after treatment pavement conditions and rates of deterioration in studying the 

pavement performance.

• Previous LTPP studies focused on the effects of environment, roadbed soil, joint load 

transfer, drainage, base type, and slab width on pavement performance. Few conclusions 

regarding the effects of these factors were made. It was recommended to analyze the 

effects of the environmental regions on the effectiveness of the treatment using the 

before treatment and after treatment pavement condition and distress and the associated 

rates of deterioration.
53

CHAPTER 3. PAVEMENT CONDITION CLASSIFICATION

To address the objectives of this study, as described in chapter 1, a dual pavement rating system 

was developed that considered both the pavement condition and its rates of deterioration. The 

rationale for the development of the system is expressed in chapter 2 and further detailed in this 

chapter. An accurate pavement condition rating system represents pavement behavior best when 

based on the current and future pavement conditions. The main benefit of including the 

estimation of future conditions is the ability of pavement managers to plan, budget, and create 

long-term treatment strategies to preserve the pavement network. Pavement condition ratings 

based on the current conditions alone only allow decisions to be made for the given data 

collection cycle. As interpreted by AASHTO Transportation Asset Management Guide—A 

Focus on Implementation, the Moving Ahead for Progress in the 21st Century Act of 2012

(MAP-21) sets forth the following directive for asset management:(77)

Asset management is a strategic and systematic process of operating, maintaining, 

and improving physical assets, with a focus on engineering and economic analysis 

based upon quality information, to identify a structured sequence of maintenance, 

preservation, repair, rehabilitation, and replacement actions that will achieve and 

sustain a desired state of good repair over the lifecycle of the assets at minimum 

practicable cost. (from section 1103)

The two key statements are the following:

• “Asset management is a strategic and systematic process of operating.” A strategic 

and systematic process implies the ability to model and estimate future conditions and 

times for corresponding actions on a regular basis.

• “Identify a structured sequence of maintenance, preservation, repair, rehabilitation, 

and replacement actions that will achieve and sustain a desired state of good repair 

over the lifecycle of the assets.” The sequence of maintenance, repair, and rehabilitation 

implies current and future actions. Hence, accurate prediction of future conditions and 

timing of future actions are essential to the implementation of MAP-21.

The measured pavement distresses and conditions generally increase over time as the pavement 

deteriorates as a result of traffic loads and environmental conditions. Periodically over the 

pavement lifecycle, preservation and rehabilitation treatments are applied to reduce the pavement 

distresses and improve its conditions, as illustrated in figure 26. The costs of these treatments 

generally increase as the pavement deteriorates and the distresses or conditions worsen. 

Pavement condition ratings based on current distresses and conditions are most commonly found 

to be problematic because they do not include the pavement rates of deterioration. The current 

condition alone does not support LCCA. Two pavement sections in equally good condition this 

year may or may not be in similar good or fair condition 2 or 3 years later. Consequently, an 

accurate pavement rating system should include the current distresses and conditions as well as 

the pavement rates of deterioration. 
54

Figure 26. Graph. Typical pavement condition or distress over the pavement lifecycle.

Figure 27 illustrates the progression of roughness, as described by the IRI, of SPS-1 test sections 

0102 and 0103 in Iowa. The figure also provides an example rating system based on the current 

condition (IRI). The data in this figure indicate the following:

• The initial IRIs for test sections 0102 and 0103 were, respectively, about 55 and 

45 inches/mi (0.9 and 0.7 m/km) (good condition)).

• The IRIs in the third year were about 63 and 85 inches/mi (1.3 and 1.0 m/km) for test 

sections 0102 and 0103, indicating good and fair conditions, respectively.

• Approximately 2 years later, the IRI of test section 0102 was more than 120 inches/mi 

(2 m/km) (poor condition)) whereas the IRI for test section 0103 was only about 

65 inches/mi (1 m/km) (good condition)).
55

1 inch/mi = 0.0158 m/km.

Figure 27. Graph. IRI versus elapsed time for LTTP SPS-1 test sections 0102 and 0103

in Iowa.

These observations indicate that test section 0102 changed from good to fair condition in about 

1 year and from fair to poor in about 4 years, whereas 8 years after construction, test section 

0103 was in the middle of the fair condition range. This example indicates that the latest 

measured IRI data of any test section should not be used alone to predict future condition and 

consequently to plan possible treatment actions. The time-series data must be used. The reason is 

that almost all pavement sections deteriorate over time, and their rates of deterioration vary 

substantially from one pavement section to the next. Hence, to effectively and comprehensively 

plan pavement preservation actions for a pavement network, the rate of deterioration with respect 

to each condition and distress type of each pavement section must be known. Therefore, an 

accurate pavement rating system should be based on each measured pavement condition and 

distress type and the corresponding rates of deterioration. 

PAVEMENT CONDITION RATING SYSTEM

A balanced and comprehensive pavement condition rating system should be based on the 

two types of pavement conditions, functional and structural. In this study, the functional rating 

was based on ride quality (IRI) and safety (skid resistance and rut depth) and was expressed by 

the RFP. (A detailed definition of this measurement is provided later in this section.) For a given 

pavement section and when supported by the available data, three RFPs should be calculated; 

one based on IRI, one on rut depth, and one on skid resistance. The shortest RFP was assigned to 

the pavement section in question to flag that section for potential treatment actions. 

The structural rating was based on cracking and rut depth or faulting and was expressed by RSP.

(A detailed definition of this measurement is provided later in this section.) For any given 
56

pavement section, six RSPs should be calculated; one for each of transverse, longitudinal, 

alligator, edge, and block cracking, and one for either rut depth for flexible pavements or faulting 

for rigid pavements. The shortest of the six RSPs was assigned to the pavement section in 

question to flag it for potential treatment actions.

Based on this discussion, RFP is defined as the shortest time period in years from the time of the 

last data collection to the time when a functional condition or distress reaches its corresponding 

prespecified threshold value. Although in this study two RFP measures (IRI and rut depth) were 

used to define RFP, the pool of functional measures could be expanded by State transportation 

departments to include, as an example, skid resistance. Nevertheless, in this study, after 

calculating two RFPs (one for rut depth and one for IRI), the shortest RFP was assigned to the 

pavement section in question to flag that section. The other RFP was retained in the database. 

RSP, on the other hand, is defined as the shortest time period in years from the time of the last 

data collection to the time when a structural distress reaches its corresponding prespecified 

threshold value. In this study, for each test section, six RSPs were calculated, and the shortest 

RSP was assigned to the pavement section in question to flag it for potential action Once again, 

the pool of structural measures used in this study for calculating RSPs could be expanded by the 

State transportation departments. It is important to note that once a pavement section is flagged 

for potential action, all available functional and structural data should be downloaded and 

examined before a treatment strategy is selected. Also note that the recommended threshold 

values for each pavement condition and distress are presented in a later section in this chapter.

These definitions indicate that RFP and RSP are not combined condition indices. Each condition 

and distress type is analyzed separately, and the results are retained for further analyses. It is the 

minimum RFP or RSP assigned to the pavement section that flags that section for potential 

actions. In addition, RFP and RSP do not indicate the treatment to be applied to a pavement 

section. Rather, they flag mechanisms for identifying pavement sections that are in need of 

further attention. Once again, the stakeholder should review all available data for the flagged 

pavement sections and examine the distress, condition, and other related data. After this 

examination, the stakeholder can select treatment alternatives that address all or most defects and 

their causes. Ideally, each treatment alternative should then be subjected to LCCA and its impact 

on the entire pavement network should be determined before the treatment is selected.

The RFP and RSP concept differs from the condition indicators evaluated in chapter 2 of this 

report. RFP and RSP account for the pavement rates of deterioration. The RFP and RSP are 

calculated based on nonlinear mathematical functions that model the progression of the condition 

or distress over time. These equations are flexible and can be selected by the users or can be 

replaced with the equations of their choosing.

RFP and RSP is a dual rating system; RFP can be considered a pavement rating for the users, 

whereas RSP is an agency rating. The rating scale of the dual rating systems and the 

corresponding descriptive terms are listed in table 11. The scale is divided into three CSs 

numbered 1, 2, and 3 that correspond to poor (red), fair (yellow), and good (green) conditions, 

respectively, and to the three RFP and RSP ranges listed in table 11. Note that the main reason 

for using the same year ranges for RFP and RSP is ease of communication.
57

Table 11. Pavement condition rating based on three CSs.

CS RFP Range 

(Years)

RSP Range 

Code Color Descriptor (Years)

1 Red Poor < 4 < 4

2 Yellow Fair 4 to < 8 4 to < 8

3 Green Good > 8 > 8 

Note: The dual rating systems could be used to select treatment categories at the network level. For example, 

preservation treatments should generally be applied to pavement sections with fair or better CSs. Heavy 

preservation treatment, or more likely rehabilitation, should generally be applied to pavement sections with poor 

RSP CSs. The treatment selection should be verified at the project level.

The dual rating system shown in table 11 was expanded to the five-level CS dual rating system 

presented in table 12. The main advantage of this five-level rating system is that the condition of 

the pavement sections in one CS or within a given RFP or RSP range is more uniform. It is 

recommended to use the three-level rating system for communication while the five-level rating 

system should be used for analyses and management. Note that this is possible because the poor 

and good ratings of the three-level CS system encompass the two additional CSs from the fivelevel CS system, while the fair CSs are equivalent.

Table 12. Pavement condition rating based on five CSs.

CS RFP Range 

(Years)

RSP Range 

Code Color Descriptor (Years)

1a Red Very poor < 2 < 2

1b Pink Poor 2 to < 4 2 to < 4

2 Yellow Fair 4 to < 8 4 to < 8

3a Light Green Good 8 to < 13 8 to < 13

3b Green Very good > 13 > 13

The main advantage of using RFP and RSP together is that each should decrease 1 year for every 

calendar year (i.e., RFP and RSP are linear functions of time although they are modeled as nonlinear 

functions of the pavement distress and conditions). To illustrate this point, consider the power 

function (see figure 28) that is typically used to model the rut depth data as a function of time. 

Figure 28. Equation. Rut depth.

The time at which the rut depth is equal to the prespecified threshold value (Th) can then be 

calculated as shown in figure 29:

Figure 29. Equation. t.

The time t in figure 29 is constant and is equal to the time in years between the end of 

construction of the last treatment action and the time when the RD threshold value is reached. 

RSP is then calculated as the time t minus the surface age of the pavement section in question as 

stated in figure 30.

Rut depth = RD = αt

β

;the time t = exp

 

1

β

 ln 

RD

α

 

t = exp

 

1

β

 ln 

Th

α

 

= e

 

1

β

 ln 

Th

α

 
58

Figure 30. Equation. RSP.

Where:

RD = rut depth.

and = Statistical parameters of the nonlinear function of figure 28.

Th = Threshold value for rut depth (typical value is 0.5 inch (12.5 mm)).

SA= Surface age (years).

DSL = Design service life of the last treatment (years).

ln = natural logarithm.

e = Exponential function.

Finally, RSP should be positive and is limited to the DSL minus the SA of the pavement section 

in question. Such limitation is required until at least three time-dependent RD data are measured 

and available in the database. 

Because the time t in figure 30 is constant, RSP decreases by 1 year as the surface age of the 

pavement section increases by 1 year. To illustrate, consider the idealized rut depth power 

function of figure 28 and the corresponding idealized data shown in figure 31. The solid circles 

and curve in figure 31 simulate the idealized measured data whereas the dotted curve simulates 

the predicted rut depth data. At time 0 (end of construction), no rut depth data were available,

and RSP of the pavement section was equal to the DSL of 15 years minus the pavement SA of 

0 years. Similarly, 1 and 2 years after construction, RSP was equal to the DSL minus the 

pavement SA. In the future, when the third measured rut depth data point becomes available, the 

data could be modeled using a power function, and the time at which the rut depth reaches the 

prespecified threshold value of 0.5 inch (12.5 mm) can be estimated using figure 29. RSP at that 

time was equal to the calculated time to threshold minus the pavement surface age. This 

procedure was repeated when a new data point became available, and a new RSP was calculated 

as displayed in table 13. RSPs and SAs listed in table 13 were plotted in figure 32. It can be seen 

that RSP decreased by 1 year as SA increased by 1 year. Similarly, the IRI data measured as a 

function of time along LTPP test section 0102 in Iowa and depicted in figure 27 (repeated herein 

for easy reference as figure 33), were modeled with an exponential function, and six RFPs were 

calculated based on sets of three, four, five, six, seven, and eight time-series data points. The 

results are presented in table 14 and depicted in figure 34. Once again, the actual measured IRI 

data when modeled using an exponential function yielded RSPs that decreased by 1 year as the 

pavement SA increased by 1 year. Once again, similar results were obtained from the analyses of 

other LTPP test sections using various cracking data. They are included in chapters 6 and 7.

0 ≤ RSP = t − SA = e

1

β

ln 

Th

α

 

− SA ≤ DSL − SA 

α β
59

1 inch = 25.4 mm.

Figure 31. Graph. Example of idealized rut depth data and function versus elapsed time.

Table 13. Progressive calculation of RSP of the idealized rut depth shown in figure 31.

Number of 

Available 

Data 

Points

Surface 

Age

(Years)

Values from

Figure 28

RSP

(Years) Calculation Equation

1 0.01 N/A N/A 14.99 See figure 30

2 1 N/A N/A 14

3 2 N/A N/A 13

3 2 0.2217 0.3 13 See figure 30

4 3 0.2217 0.3 12

5 4 0.2217 0.3 11

6 5 0.2217 0.3 10

7 6 0.2217 0.3 9

8 7 0.2217 0.3 8

9 8 0.2217 0.3 7

10 9 0.2217 0.3 6

N/A = Not applicable.

α β
60

Figure 32. Graph. RSP versus the pavement surface age for an idealized power function.

1 inch/mi = 0.0158 m/km.

Figure 33. Graph. IRI versus elapsed time for LTPP SPS-1 test sections 0102 and 0103

in Iowa.
61

Table 14. RFP of LTTP test section 0102 in Iowa based on the IRI data shown and depicted 

in figure 34.

Number 

of Data 

Points

Pavement 

Surface 

Age 

(Years)

IRI 

(Inches/Mi)

IRI

See Figure 28

RFP (Years)

(See Figure 30)

3 2.88 85.75 52.771 0.1721 3.99

4 4.32 103.62 53.864 0.1559 3.13

5 5.37 122.53 54.067 0.1535 2.17

6 6.13 136.45 54.251 0.1518 1.47

7 6.98 146.89 54.933 0.1464 0.82

8 8 180.64 54.721 0.1478 –0.25

1 inch/mi = 0.0158 m/km.

Figure 34. Graph. RFP versus pavement surface age for LTPP test section 0102 in Iowa.

The three- and five-level CS systems in terms of RFP and RSP are depicted for an idealized and 

untreated pavement section shown in figure 35 and figure 36. The threshold values in the figures 

are 172 inches/mi (2.73 m/km) and 3,168 ft2of alligator cracking per 0.1 mi (180 m2of alligator 

cracking per 0.1 km), respectively. 

α β
62

1 inch/mi = 0.0158 m/km.

Figure 35. Graph. RFP CSs.

1 ft2/mi = 0.0581 m2/km

Figure 36. Graph. RSP CSs.

In addition, RFP and/or RSP can be used for the following purposes:

• Expressing the rating of a pavement section or a pavement network (see table 11 and 

table 12).
63

• Estimating the cost of pavement preservation, as shown in figure 37. 

• Understanding the consequences of delayed actions, as shown in figure 38.

1 lane-mile = 1.61 lane-km.

Figure 37. Graph. Conceptualized cost of pavement preservation versus RSP.

1 lane-mi = 1.61 lane-km.

Figure 38. Graph. Correlations among RSP, cost of preservation, and descriptive pavement 

classification.
64

Figure 37 and figure 38 show that as RSP decreases, the pavement condition worsens and the 

cost of pavement preservation increases. For example, the average cost per lane-mile of 

maintaining the pavement when RSP was 13 or more years was about $50,000 per lane mi 

($31,250 per lane km). If the pavement deteriorated to fair condition (RSP between 4 and 

8 years), the average cost of pavement preservation would increase to $400,000 per lane-mi 

($250,000 per lane km) (or eight times more). Such average cost data could be obtained from 

past pavement project records and shared with legislators and the general public. When the 

relationship between costs and the RSPs or the pavement classification descriptive terms is 

established, the data could be used to arrive at the optimum pavement preservation strategy at the 

network level and for LCCA at the project level.

The RSP concept and the benefits of preservation treatments can be demonstrated by considering 

a flexible pavement section that was designed and constructed to last 15 years (DSL = 15 years). 

After construction, the section was in very good condition. (RSP was estimated at 15 years.) 

Over time, the section deteriorated (started showing some cracks), and the pavement condition 

dropped from very good to good in 6 years and to fair in 10 years, as shown in figure 39 and 

figure 40. When the pavement condition reached fair status (RSP = 5 years), a thin (less than 

2.5-inch (63.5-mm) HMA overlay was applied, and the pavement surface condition was restored 

to very good. Over the next 7-year period, the pavement surface condition deteriorated again from 

very good to good and then to fair. At 17 years after the original construction, another thin 

overlay was applied, and the surface condition was restored to very good status. Five years after 

the second overlay, the pavement surface condition dropped from very good to good. At that time 

(22 years after construction), the section was subjected to thin mill-and-fill treatment, and the 

pavement surface condition was restored once again to very good condition. To summarize, the 

first HMA overlay was applied 10 years after the original construction (when the condition of the 

pavement surface reached the fair CS). A second overlay was applied when the surface of the first 

overlay reached the fair CS. Finally, a thin mill-and-fill treatment was applied when the second 

overlay treatment was still in good condition. As illustrated in figure 39 and figure 40, the HMA 

overlays provided a better surface condition initially and decreased the rate of deterioration of the 

lower layers (the original HMA layers). Indeed, the original asphalt layer was still in fair 

condition 22 years after construction.
65

Figure 39. Graph. Example of pavement condition over time with two thin HMA overlays 

and one thin mill-and-fill action.

Figure 40. Graph. Example RSP over time with two thin HMA overlays and one mill-andfill action.

It should be noted that the timing for the first and second overlays or any other treatment type 

should be selected after LCCA is conducted. Any pavement section can be treated at any time 

during its service life. Some sections may be treated when RSP is 15 years, while others may be 

treated when RSP is 8 years, and still others when RSP is at 3 years. Once again, the time and the 
66

type of the treatment should be selected based on the results of LCCA. In this regard, the 

following data are required for LCCA:

• For each applicable treatment type, the costs of the treatment when the pavement section 

is in each of the five CSs listed in table 12.

• The expected TL (the time in years until the pavement condition after treatment reaches 

the same status as that before treatment).

The preferred treatment type(s) and time of treatment are those that yield the minimum cost and 

maximum benefits.

RECOMMENDED THRESHOLD VALUES

Threshold values are defined herein as the magnitude of a measurable pavement condition or 

distress that constitutes the minimum level of pavement functionality acceptable to the agency 

and users or the minimum acceptable level of structural integrity. All threshold values should be 

established based on suitable engineering criteria of appropriate pavement performance 

measures. For example, functionality thresholds should be established based on ride quality and 

safety (such as IRI, skid resistance, and rut depth), whereas, structural thresholds should be 

established based on each cracking type, rutting or faulting. The units of measurement for the 

threshold values should be the same as those used in measuring the corresponding pavement 

condition and distresses. The engineering criteria for the threshold should include the impacts of 

their values on pavement condition and distress, the lifecycle cost, and the optimum timing for 

pavement preservation. The recommended threshold values for the calculation of RFP and RSP 

are presented in table 15 and table 16. The reasons for the selection of these values are shown in 

each table. Note that the recommended threshold values are flexible and can be adjusted based 

on the agency and user needs and constraints and on the posted speed limit or road class. 

Table 15. Threshold values describing RFP.

Pavement 

Condition 

Type

Threshold Values Used in the Analyses

AASHTO MEPDG(37)

Threshold 

Value Explanation

IRI 172 

inches/mi 

(2.7 m/km)

Minimum acceptable ride 

quality at 55 mi/h 

(90 km/h); driver speed 

and comfort may be 

reduced above this value

Interstate: 160 inches/mi

(2.5 m/km)

Primary: 200 inches/mi

(3.16 m/km)

Secondary: 200 inches/mi

(3.16 m/km)

Skid 

resistance

To be 

determined 

by the agency

Depends on the method of 

measurement and 

pavement type

Not included

Rut depth 0.5 inches 

(12.7 mm)

Maximum allowable 

depth to control 

hydroplaning potential in 

wet conditions at 55 mi/h

(90 km/h)

Interstate: 0.40 inches (10.6 

mm)

Primary: 0.50 inches (12.7 mm)

Others (< 45 mi/h (72 km/h)):

0.65 inches (16.5 mm)
67

Table 16. Threshold values describing RSP.

Pavement 

Condition or 

Distress 

Type

Threshold Values Used in the Analyses

Value Explanation AASHTO MEPDG(37)

Alligator 

cracking

1,267 ft2/0.1 mi 

(73 m2/0.1 km)

Twenty percent of the 

lane area cracked 

(assuming 12-ft (3.66-m) 

lane width)

Interstate: 10-percent lane area

Primary: 20-percent lane area

Secondary: 35-percent lane area

Longitudinal 

cracking

1,056 ft/0.1 mi 

(200 m/0.1 km)

Two cracks along the 

entire section length

Not included

Transverse 

cracking 

(JPCP)

264 ft/0.1 mi 

(50 m/0.1 km)

Two thirds of the slabs 

are cracked (assuming 

16-ft (4.88-m)-long slab

Interstate: 10 percent

Primary: 15 percent

Secondary: 20 percent

Transverse 

cracking 

(HMA)

350 ft/0.1 mi 

(67 m/0.1 km)

Lane is divided into 12-ft 

(3.66-m) squares, 

assuming 12-ft (3.66-m) 

lane width and even 

crack spacing of 12 ft 

(3.66 m)

Interstate: 500 ft/mi (94.7 m/km)

Primary: 700 ft/mi (132.6 m/km)

Secondary: 700 ft/mi (132.6 

m/km)

Faulting 0.25 inches

(6.35 mm), average 

over 0.1 mi (100 m)

Dowel bars have likely 

sheared, or concrete 

around dowels has 

deteriorated and may be 

spalled

Interstate: 0.15 inches (3.8 mm)

Primary: 0.20 inches (5.8 mm)

Secondary: 0.25 inches (6.4 mm)

Deflection See chapter 7 Certain deflection or 

differential deflection 

could indicate structural 

or load transfer issues or 

void beneath the slab

Not included

The threshold values listed in table 15 are representative of the average minimum level of 

serviceability that the State transportation departments strive to provide their users. Therefore, 

these values are somewhat subjective; where one State transportation department may strive to 

provide pavement with no more than 150 inches/mi (2.4 m/km) IRI, another may set the 

maximum acceptable pavement roughness at 225 inches/mi (3.6 m/km) IRI for its pavements. 

Neither value is wrong, right, or otherwise; the value should be determined by the State 

transportation department by considering the users’ needs and expectations as well as the 

practicality and the lifecycle costs associated with maintaining its pavement network given the 

agency constraints. Safety-related threshold values, such as rut depth and skid resistance, should 

also be determined by the State transportation department based on an assessment of the typical 

driving conditions, speeds, and vehicle characteristics and their role in a risk assessment analysis. 

For example, the potential for hydroplaning increases in wet climates, on roads with minimal 

cross-slope for surface drainage, and in areas with higher speed limits. The maximum allowable 
68

rut depth should be determined to provide reasonably safe travel for most roadway travelers in an 

economically feasible manner.

Further, the threshold values do not imply that the State transportation department must or 

should wait to take action until the pavement sections reach the threshold values, nor do they 

imply that a roadway must be closed to traffic if the threshold is surpassed. The threshold value 

is a management tool that helps planners and managers evaluate, assess, and make reasonable 

and potentially cost-effective decisions regarding the conditions and serviceability of the 

pavement network.

On the other hand, the engineering criteria for most of the structural threshold values listed in 

table 16 are much more difficult to establish for various reasons, including the following:

• The lack of sufficient long-term pavement performance databases that can be used to 

analyze the impact of the threshold levels on the lifecycle cost and the health of the 

pavement network. The most critical information that is insufficient to support the 

analyses is cost data.

• The total yearly cost of preserving the pavement network.

• The constraints of the road authorities regarding budget level, political pressure, 

increased demand, and increasing cost over time.

• The relationship between the engineering criteria (the threshold values) and the road 

class. A typical highway authority manages several classes of roads that have various 

traffic demands. If different threshold values are established for different road classes, 

communicating the values becomes problematic.

Nevertheless, the pavement community generally agrees that pavement preservation and 

maintenance actions applied over time are more cost effective than allowing pavements to 

deteriorate until reconstruction is required. Reconstruction is rarely required because pavements 

can be preserved indefinitely. The problem lies in where the tipping points occur in the economics 

of preservation, maintenance, and rehabilitation. Typical pavement structures are subjected to 

reconstruction after numerous cycles of pavement preservation, maintenance, and rehabilitation. 

The number of these cycles and the corresponding lifecycle costs are functions of the pavement 

preservation strategy used and the timing of the various pavement preservation cycles. 

The establishment of the RSP threshold values should be based on the assumption that RSP 

would flag the pavement sections for preservation actions at the proper time. For example, 

consider a flexible pavement section that has begun to develop block cracking (top-down type 

cracks) owing to surface aging. Several surface treatments, such as mill and fill or HMA overlay, 

could reduce or eliminate the block cracking and its rate of propagation. If the threshold value 

for block cracking is set very high or if RSP is allowed to decrease to zero and beyond the 

threshold value, the block cracks extend in depth and the cost effectiveness of these treatments 

generally decreases until the conditions (the tipping point) for reconstruction are reached. This 

tipping point could be when the cracks pass the mid-depth of the asphalt layer or when they 

penetrate the entire asphalt layer. The specific condition(s) under which reconstruction becomes 

most cost effective depends on many factors, including the following:
69

• User costs: User costs can be summarized as the travel costs associated with driver delay 

and the vehicle operating costs VOCs (fuel and vehicle wear). The magnitude of the user 

costs for a given pavement project has many factors, including the type and length of 

traffic control and detouring, the conditions of the roadway, and the traffic volume.

• Availability of funds: State transportation department funds are limited, and some 

pavement projects have higher priority than others for various reasons. Hence, pavement 

treatments may be applied sooner or later than optimum, and the cost effectiveness of the 

treatment may be affected. For example, a given pavement section may have reached the 

tipping point, without budget constraints, the proper fix can be applied. With budget 

constraints (short funding) a less-expensive stopgap treatment may be applied.

• Ancillary work required: Federal and State regulation and policy often require 

standardization of ancillary transportation items when a pavement project is undertaken. 

The requirements can be contingent on the type of work being performed. For example, a 

roadway reconstruction requires update of vertical and horizontal curves, bridge clearance, 

guard rails etc., while an HMA overlay may not require any ancillary updates.

• Pavement location (urban or rural): The location of the pavement segment affects the 

costs of equipment mobilization and worker travel. Likewise, the amount of traffic and 

the number of access points also affects the costs. Highly trafficked roadways may 

necessitate detour routes that require improvements to handle the increased traffic, while 

the cost of traffic control can also be affected by traffic volumes and the number of 

driveways and entrance/exit ramps present.

• Pavement treatment benefits: The benefit of a given treatment depends on many 

factors, such as the before treatment conditions and rates of deterioration, construction 

and material quality, and the anticipated traffic and environmental loading. The inherent 

variability in the materials and construction quality often yield differing treatment 

benefits within the boundaries of a given pavement project.

The concept of structural integrity thresholds is even further complicated by the false notion that 

pavements that have reached the threshold value must be reconstructed. Some pavement sections 

reach the threshold for structural integrity yet may still provide acceptable level of service. For 

example, for a concrete pavement that has 100 percent of slabs with two or more transverse 

cracks, it would likely be more cost effective to reconstruct than to perform full-depth patching 

at each transverse crack. However, if the cracks are not faulted, the roadway may still have an 

acceptable IRI. In this scenario, the pavement has an RFP greater than 0 years while its RSP is 

0 years. The preferred alternative in this scenario would be to schedule pavement reconstruction 

for when RFP reaches 0 years. In other words, an RFP of 0 years implies that action is needed 

but an RSP of 0 years does not necessarily imply that reconstruction is needed immediately. 

However, the latter is a function of the threshold value and the type of distress. For example, a 

pavement rut of typical depth can be removed using certain treatments. However, if the rut is due 

to shear failure in the lower pavement layers, reconstruction may be required to eliminate the 

causes of rutting. Likewise, if the threshold value is set too high (for example, 100-percent 

alligator cracking), reconstruction may be required.
70

The concept of long-life pavement can also add an extra nuance to the RSP concept. The idea 

behind long-life pavement is to construct a significant pavement structure that will resist 

structural deterioration due to traffic and environmental loading, throughout the pavement cross

section. Pavement deterioration in long-life pavement would be limited to areas near the 

pavement surface (upper few inches), which can be perpetually replaced (i.e., with mill-and-fill 

treatment). In this scenario, RSP is virtually constant because any structural deterioration is 

periodically repaired.

One last important note on RFPs and RSPs is the concept of negative RFP or RSP. A value of 

zero implies that either the pavement is providing less than the standard level of service or the 

pavement structure has deteriorated to the point (depending on the threshold value) where major 

rehabilitation or reconstruction may be the most cost-effective treatment option. A negative 

value of RFP or RSP indicates the number of overdue years passed after the due date for major 

rehabilitation or reconstruction. This information is not of particular use to pavement managers, 

because a pavement section with 0 or –5 years RFP or RSP yields the same conclusion—

reconstruction or heavy rehabilitation might be needed. For this reason, RFP or RSP could be 

limited to 0. Note that there is no technical upper limit on RFPs or RSPs. However, the 

maximum value should be reasonably set based on the average DSL of the pavement structure.

Finally, the previous discussions are primarily based on the use of data observed or measured to 

characterize the pavement surface. Unfortunately, the damage has already occurred by the time 

the distresses appear on the pavement surface. An early indicator of impending surface distress 

would support early actions and the selection of cost-effective pavement treatments. Such an 

early indication could come from the pavement deflection data measured using an FWD. The 

measured deflection and the rates of change over time could indicate the beginning of pavement 

deterioration before surface manifestation. Hence, it may be possible to establish deflection 

thresholds and incorporate FWD data into the RSP concept, as discussed in chapter 8. It is 

envisioned that a relationship may exist between deflection and its rate of change and the 

initiation and growth of pavement surface defects. For example, consider the increasing 

deflection likely to be measured on a pavement segment experiencing the initiation and 

propagation of alligator cracking, as shown in figure 41. The envisioned data in the figure 

indicate that the pavement deflection increases as the cracks initiate and propagate upward.
71

1 lb = 0.454 kg.

1 mil = 25.4 microns.

Figure 41. Illustration. Envisioned change in deflection through the progression of alligator 

cracking.

Unfortunately, such a relationship between crack initiation and increases in the measured 

pavement deflection has not received significant attention. The main reason is that the crack 

initiation time is not known; most cracking data start when the cracks appear on the pavement 

surface. Further, no deflection threshold value has been established that indicates the initiation of 

cracks. The difficulty in developing a deflection threshold is that each pavement structure is 

designed to have a different deflection under the same load. For example, a section of interstate 

freeway may be designed to deflect about 4 to 8 mil (101.6 to 203.2 microns) under 9,000 lb 

(4,086 kg) (half of an 18-kips single-axle load) while a suburban arterial may be designed to 

deflect 20 to 32 mil (508 to 813 microns) under the same load. Further, the frequency of 

pavement loading has an enormous impact on the initiation of alligator cracking and deflection.

Because no applicable relationships between deflection and pavement surface distress have been 

developed or published, in this study, the LTPP deflection data were analyzed to explore the 
72

potential for developing such relationship. The measured pavement deflections of several LTPP 

flexible and rigid pavement test sections were analyzed in an attempt to establish threshold 

values based on the pavement structural integrity. Results of the analyses are discussed in 

chapter 8 of this report. It should be noted, however, that most, if not all, State transportation 

departments collect FWD data at the project level as needed. The data are not collected on a 

regular basis at the network level.

FLEXIBILITY OF THE PAVEMENT RATING SYSTEMS

The dual pavement condition rating systems are designed to be adaptable to the needs and 

constraints of the users. The dual systems are based on three types of information: (1) time-series 

pavement condition and distress data, (2) threshold values, and (3) applications of the results, 

which can be adjusted by any State transportation department to work for almost any dataset and 

for many different tasks. The three information types are the following:

• Data: The data of the dual condition rating systems are the pavement condition and 

distress types included in the development of the rating. Recall that the dual rating is 

based on both functionality and structural integrity. The user may decide how to describe

the pavement function and structural integrity. For example, this report uses the IRI to 

describe ride quality, as is used by the LTPP Program and most State transportation 

departments. However, an agency may choose to use another measurement or index, such 

as the ride quality index in place of IRI. The rating process would be essentially the same 

with different data.

Likewise, the pavement conditions and distresses used to comprise the functional and 

structural integrity rating could have a wide range from user to user. For example, this 

report uses IRI and rut depth to rate the pavement function and uses alligator, 

longitudinal, and transverse cracking, and either rut depth or faulting to describe 

structural integrity. However, an agency may choose to use only traffic load or wheelpath

related distresses such as alligator cracking and transverse cracking (rigid pavement). 

Finally, an agency may choose to include additional data that were not included in the 

ratings in this report (such as edge cracking and block cracking). The data elements 

collected by the State transportation departments are not consistent and some may have 

more or less available data for use in the dual rating systems.

For example, pavement surface friction data are not often available at the network level. 

However, a State transportation department with significant friction data may choose to 

include the data in its functional rating or in a safety rating system. The addition or 

subtraction of the rating systems data does not affect the process of the rating systems. 

The rating is based on the minimum RFPs and RSPs, respectively, regardless of the 

number of elements.

• Thresholds: The dual pavement condition rating systems use pavement condition and 

distress threshold values and CSs. Both of these are flexible in nature and can be molded 

to fit the needs of any State transportation department. Important considerations include 

the following:
73

o The pavement condition and distress types and thresholds presented in table 15 and 

table 16 are not set in stone. They are based on those values available in various 

literature, the state of the practice of State transportation departments, and the 

experience and opinion of the research team.

o Thresholds can be modified and calibrated to the needs of the interested State 

transportation departments. For example, the recommended IRI threshold of 

172 inches/mi (2.7 m/km) listed in table 15 is based on providing a comfortable ride 

on roads with speed limits of 55 mi/h (90 km/h) or greater. An agency may choose a 

higher or lower value such as 160 or 200 inches/mi (2.5 or 3.16 m/km) based on the 

road users’ inputs or the managers of the agency and in some cases the legislators. 

o Similarly, the interested agency may choose to use different threshold values for the 

structural integrity. The recommended values in this report, listed in table 16, are 

based on crack saturation or the point where preservation treatments other than heavy 

rehabilitation or reconstruction are no longer cost effective. These threshold values 

are highly variable and depend on numerous factors. Hence, the values are anticipated 

to be modified by the interested agencies based on their specific scenarios.

o The ranges in years of RFP and RSP listed in table 11 and expanded in table 12 are 

designed to describe both the pavement conditions and rates of deterioration and to 

provide sufficient time for planners and managers to scope pavement sections for the 

application of cost-effective pavement preservation treatments. Again, the interested 

agency may choose to modify these ranges to fit its needs. For example, the RSP 

range in years for the poor rating of 0 to less than 4 years is based on the required 

time to select, program, finance, and bid a major rehabilitation or reconstruction 

project. That is, if a project has an RSP of less than 4 years and it was selected for 

major rehabilitation, the time required to finish the paperwork to approve, design, 

plan, finance, establish specifications, and bid the project varies from 2 to 5 years 

depending on the State transportation department. Hence, 4 years is recommended so 

that by the time of construction, RSP is near 0 years. This scenario implies that any 

interested State transportation department could modify the ranges of the rating scale 

to fit its needs and based on its own practice. However, if changes or modifications 

are made, the process of the rating systems would be essentially the same with 

different ranges of RFP and/or RSP.

• Applications: The applications of the dual rating systems are open and unlimited. 

Because the terms good, fair, and poor and the corresponding colors green, yellow, and 

red are easy to interpret and can be understood by the majority of stakeholders, the public 

could be informed of the CSs of the entire network or specific routes or sections. 

Legislators could use the ratings to determine future funding levels and directives. 

Planners and upper managers could use them to allocate funds or to select regions or 

routes for treatment. Pavement managers could use them to flag pavement sections for 

treatment or to assess the future needs of the pavement network. The specific uses of the 

dual rating systems are numerous and can be established by the interested agency. 

The dual rating systems cannot be used alone for the selection of treatment categories unless the 

boundaries of these categories are established. Figure 42 depicts the classic S-shaped curve for 
74

alligator cracking (expressed in the figure as percent of the total area). The figure also shows the 

following boundaries for three treatment categories:

• Window 1 (W1): Do nothing or light maintenance where the extent of alligator cracking 

varies from 0.0 to 2 percent. This extent corresponds to RSP CS 3 (more than 8 years).

• Window 2 (W2): Potential preservation actions where the extent of alligator cracking 

varies from 2 to about 18 percent. This extent corresponds to RSP CS 2 (4 to 8 years). 

• Window 3 (W3): Potential heavy rehabilitation or reconstruction actions where the 

extent of alligator cracking exceeds 18 percent. This extent corresponds to RSP CS 1 

(0 to 4 years). 
75

Figure 42. Graph. Idealized S-shaped curve for alligator cracking showing three windows (threshold values) for various 

treatment actions.
76

The final selection of the treatment category and treatment type within a given category should 

be accomplished after the actual pavement condition and distress data and results of the forensic 

investigation of the causes of distresses are carefully examined.

The previous discussion implies that the RSP and RFP spectrum could be divided into various 

ranges to aid in the selection of pavement preservation type and estimation of cost. For example, 

an RSP longer than 8 years implies light maintenance, whereas RSPs between 4 and 8 years 

imply preservation treatments (i.e., no preservation treatment should be applied to pavement 

section having an RSP of less than 4 years). Finally, an RSP shorter than 4 years implies 

rehabilitation and/or reconstruction. The interested State transportation department could assign 

an average cost to each of these RSP ranges based on its cost data. When such cost estimates can 

be accomplished based on the ranges of RSP, the data indicate that early preservation (at high 

RSP) yields the least lifecycle cost.

SUMMARY, CONCLUSIONS, AND RECOMMENDATIONS

This chapter presented the basis for and development of the dual pavement condition rating 

systems. Topics covered included the condition classification, dual pavement condition rating 

systems, recommended threshold values, and a discussion of the flexibility of the systems. The 

various topics were evaluated, and the research team made the following recommendations:

• Pavement condition rating should be based on current conditions and distresses as well as 

the pavement’s rates of deterioration. It is recommended that accurate planning and 

management be based on the pavement conditions and rates of deterioration.

• Dual pavement condition rating systems were developed to express the pavement 

conditions and distresses over time based on the new RFP and RSP concept. RFP and RSP 

express the time until certain functional and structural thresholds are reached. It is 

recommended that the dual rating system be adopted and calibrated by the stakeholders.

• Threshold values were provided for calculation of RFP and RSP. The values were based 

on minimum level of service to the user (functional), and loss of structural integrity 

(structural). It is recommended to adopt these or similar threshold values and evaluate 

them as more pavement condition and distress data become available.

• The dual pavement condition rating systems were found to be flexible and could be easily 

adopted. It is recommended that the dual rating system be adopted and tailored to fit 

agency needs and constraints.

• The RSP and RFP spectrum could be divided to three or five ranges. The estimated 

average cost to fix a unit length pavement section could be assigned to each range (see 

figure 37 and figure 38). This would assist the agency in establishing an optimum 

preservation strategy based on LCCA.
77

CHAPTER 4. DATA MINING AND SYNTHESIS

DATA SOURCES

The data used in this study were obtained from the LTPP database Standard Data Release 28.0. 

The database contains six volumes consisting of the primary dataset, data compilation views, 

FWD measurements, profile data, traffic data, and LTPP Traffic Analysis Software tables. Each 

of the six volumes contains various data elements for the more than 2,500 pavement test sections 

included in the LTPP Program. At the time of this study, about 1,700 test sections had been deassigned or decommissioned from the LTPP Program over time, but nearly 800 remained active 

under the various experiments. Planning and scheduling has been taking place under the 

direction of FHWA to establish additional experiments and test sections to study different/new 

topics. Table 17 and table 18 list the number of active test sections under the SPS and GPS 

experiments, respectively, at the time of this report. The data from both active and de-assigned 

test sections were extracted from the database and arranged in special format for analyses. The 

detailed data extraction is presented in later sections in this chapter. Results of the analyses are 

presented and discussed in chapters 5, 6, and 7.

Table 17. Active LTPP SPS test sections as of January 2014.

SPS Number

1 2 3 4 5 6 7 8 9 Total SPS

Number of 

active test 

sections

53 186 0 0 53 18 0 59 43 412

Table 18. Active LTPP GPS test sections as of January 2014.

In addition, the pavement management databases from three State transportation departments—

CDOT, WSDOT, and LADOTD—were requested and received. From each database, several 

pavement projects were identified, and their data were downloaded and formatted for analyses. 

Each of the selected projects was subjected to certain treatments in the past. The data for each 

project included the location reference systems, the time-series pavement conditions and 

distresses, the time and types of treatments that were performed in the past, and, in some cases, 

the cost of the treatments. The data were analyzed and the results are discussed in chapter 9.

AUTOMATED AND MANUAL PAVEMENT DISTRESS DATA

The monitoring module within the primary dataset of the LTPP database contained time-series 

pavement distress data (rut depth, cracking, and so forth). The data were collected using 

two different survey procedures: manual (visual observations) and semi-automated (videotape). 

GPS Number

1 2 3 4 5 6 7 8 9 Total GPS

Number of 

active test 

sections

13 8 67 16 30 174 51 0 13 372
78

Table 19 and table 20 list the number of manual and semi-automated surveys conducted for each 

test section in the SPS-1 experiment. The data for all other test sections in the SPS and all test 

sections in the GPS experiments were submitted to FHWA and are available from the LTPP 

Customer Support Services.(79) After detailed examination of the manual and the semi-automated 

pavement distress and condition data, the manual data were selected for data modeling and 

analyses. The semi-automated data were not used for the following reasons:

• The number of available manual data points was much greater than for the semiautomated data. The manual data had been collected over the entire duration of the LTPP 

Program, while the semi-automated data were only collected between 1989 and 2004. 

Hence, less semi-automated data were collected.

• The two sets of data were not compatible enough to be combined and analyzed as a 

function of time. The few semi-automated data points generally did not align with the 

trends indicated by the manual data over time.

• The variability of the time-series semi-automated pavement distress and condition data 

was much greater than that of the manual data.

It is important to note that similar findings were reported by another team of researchers.

(76)
79

Table 19. Number of manual and semi-automated surveys for test sections 0101–0112 in LTPP SPS-1 experiment.

State (Code)

0101 0102 0103 0104 0105 0106 0107 0108 0109 0110 0111 0112

MSAMSAMSAMSAMSAMSAMSAMSAMSAMSAMSAMSA

Alabama (1) 14 5 14 6 9 5 9 5 9 6 9 5 4 8 9 6 9 6 9 5 9 5 9 6

Delaware (10) 9 4 13 4 9 4 9 4 10 4 9 4 9 4 9 4 9 4 9 4 9 4 9 4

Florida (12) 10 2 10 2 10 2 10 2 10 2 10 2 10 2 10 2 10 2 10 2 10 2 10 2

Iowa (19) 7 7 6 7 5 7 6 7 6 7 6 7 7 7 6 7 6 7 6 7 6 7 6 7

Kansas (20) 3 2 3 2 8 7 7 7 8 7 8 7 3 2 8 7 8 7 8 7 8 7 8 7

Nevada (32) 26 6 8 6 8 6 11 6 8 6 11 6 11 6 11 6 11 6 11 6 11 6 11 6

New Mexico (35) 9 4 9 4 9 4 9 4 9 4 9 4 9 4 9 4 9 4 9 4 9 4 9 4

Ohio (39) 1 1 2 1 4 3 11 6 3 1 8 6 2 1 4 4 4 4 4 4 7 6 11 6

M = Manual.

SA = Semiautomatic.

Table 20. Number of manual and semi-automated surveys for test sections 0113–0124 in LTPP SPS-1 experiment.

State (Code)

0113 0114 0115 0116 0117 0118 0119 0120 0121 0122 0123 0124

MSAMSAMSAMSAMSAMSAMSAMSAMSAMSAMSAMSA

Arizona (4) 24 6 23 6 10 6 10 6 10 6 10 6 10 6 10 6 10 6 10 6 10 6 10 6

Arkansas (5) 8 5 8 5 8 5 8 5 8 4 8 5 6 5 6 5 6 5 8 5 8 5 8 5

Michigan (26) — — — — 13 3 13 3 13 3 6 2 — — 6 2 6 2 — — 13 3 13 3

Montana (30) 13 2 20 2 13 2 13 2 13 2 13 2 13 2 13 2 13 2 13 2 13 2 13 2

Nebraska (31) 2 2 13 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3

Oklahoma (40) 1 5 2 5 4 5 11 5 3 5 8 5 2 5 4 5 4 5 4 5 11 5 11 5

Texas (48) 13 3 13 3 13 3 13 3 13 3 13 3 13 3 13 3 13 3 13 3 13 3 13 3

Virginia (51) 12 1 19 4 8 4 8 4 8 4 8 4 8 4 9 4 11 4 8 4 8 4 8 4

Wisconsin (55) 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5

— No data available.

M = Manual.

SA = Semiautomatic.
80

DATA EXTRACTION

To facilitate the analyses of this study, the following specific data items were extracted from the 

LTPP database for each test section of the SPS and GPS experiments and formatted for timeseries analyses:

• Inventory.

• Time-series pavement condition and distress.

• Time and type of pavement rehabilitation, preservation, and maintenance actions.

• Traffic.

• FWD data, including those collected along the test sections in the SMP.

• Climatic region.

Inventory Data

All inventory data, including construction history of the test sections, their opening dates to 

traffic, lane widths, number of lanes, pavement layer types and thicknesses, and subgrade 

information, were obtained from the inventory module. Some of the tables specifically used for 

this purpose were INV_AGE, INV_GENERAL, INV_ID, INV_LAYER, INV_SUBGRADE.

Time-Series Pavement Condition and Distress Data

The time-series pavement condition and distress data used in the analyses (presented in 

chapter 5) included transverse, longitudinal, and alligator cracking; rut depth; IRI; and faulting. 

These data were extracted from their respective files and reorganized in a spreadsheet format for 

analyses. The pavement condition and distress data were obtained from the following LTPP 

tables under the monitoring module of the LTPP database:

• Cracking (MON_DIS_AC_REV, MON_DIS_CRCP_REV, and MON_DIS_JPCC_

REV): The cracking data were classified and stored in the database using three severity 

levels—low, medium, and high—as described by the LTPP Distress Identification 

Manual.

(80) The difficulty with such data was that the crack severity rating was a function 

of several variables, including the following:

o The pavement temperature at the time of the distress survey. The crack width, which 

is part of the severity level assignment, is a function of the pavement temperature. In 

general, the crack width increases as the temperature decreases.

o The subjective judgment of the surveyor who reviewed and observed the cracks. Such 

subjective judgment is a function of the degree of training and experience of the 

surveyors. Further, the same pavement segment is likely to be surveyed by different 

surveyors over time. Thus, a crack may be labeled high severity in one year and 

medium the next year or vice versa. Figure 43 and figure 44 depict, respectively, the 

time-series low, medium, and high severity transverse cracking data of SPS-3 test 

section A330 and SPS-5 test section 0502 in California. Examination of the figures 

indicated that the sum of the time-series low, medium, and high severity cracking 

data was more consistent over time than the individual severity levels of cracking and 
81

therefore more suitable for modeling. Further, various attempts were made to analyze 

the data per severity level. In each attempt, the data for a significant number of test 

sections were eliminated from the analyses because of their high variability over time. 

In addition, the transverse cracking data were classified and stored in the LTPP 

database as either unsealed or sealed cracking at each severity level, while the 

longitudinal cracking data were classified and stored at each severity level as sealed 

and unsealed cracks in the wheelpath or non-wheelpath. The sealed and unsealed 

cracks had the same effect on the pavement structural integrity; the only difference 

was that sealed cracks retarded water infiltration, which might slow the rate of 

deterioration. Further, the wheelpath and nonwheelpath longitudinal cracks differed

in their potential causes. Wheelpath longitudinal cracks in flexible pavement were

likely to be either the start of top-down cracking due to pavement-tire interaction or 

the first appearance of alligator cracks on the pavement surface. In fact, for some test 

sections, the extent of longitudinal cracking, from one survey cycle to the next

decreased substantially as alligator cracking was recorded for the first time, indicating 

that the longitudinal cracks were reclassified as alligator cracks. Therefore, for 

flexible pavements, longitudinal cracking in the wheelpath were combined with 

alligator cracking to facilitate the analyses of the data. Note that the selection of 

treatment type was based on the severity and location of the cracks, but the condition 

rating of the pavement was not affected by such information.

1 ft = 0.305 m.

Figure 43. Graph. Transverse cracking versus elapsed time for LTTP SPS-3 test section 

A330 in California.
82

1 ft = 0.305 m.

Figure 44. Graph. Transverse cracking versus elapsed time for LTTP SPS-5 test section 

0502 in California.

• Roughness (MON_PROFILE_MASTER): The time-series pavement roughness data 

were computed into IRIs and stored in the database as left wheelpath IRI and right 

wheelpath IRI. The average of the two values was considered equivalent to the effect of 

roughness on the traveling vehicle. Hence, the average IRI was considered in the analyses.

• Faulting (MON_DIS_JPCC_FAULT): For transverse joints, pavement faulting data 

were stored as edge faulting and wheelpath faulting. The wheelpath faulting data were 

used in the analyses because faulting at the edges could be influenced more by warping 

and/or curling. In addition, the average faulting among all joints within a given test 

section was calculated and used in the analyses.

• Rut Depth (MON_T_PROF_INDEX_SECTION): In the initial stages of the LTPP 

Program, rut depth measurements were made using a 4-ft (1.2-m) straightedge reference 

under the assumption that wheel-path depressions were not wider than 4 ft (1.2 m). These 

rut depth measurements can be found in the MON_RUT_DEPTH_ POINT table. 

However, in many instances, the wheel-path depressions were wider than 4 ft (1.2) m. 

Hence, transverse profile measurements were chosen by the LTPP Program over 

straightedge measurements to account for this.

(59) The transverse profile data were used 

by the LTPP Program to calculate the mean and the maximum rut depth in each 

wheelpath. The average of the two means and the average of the two maximum rut depth 

data were calculated, and the former was used in the analyses in this study. 
83

Pavement Rehabilitation, Preservation, and Maintenance Data

Pavement rehabilitation, preservation, and maintenance data were extracted from the 

MAINT_REHAB module of the LTPP database. The treatments performed on the LTPP test 

sections were classified and stored under different tables, namely MNT_IMP and RHB_IMP. 

After downloading the data, the research team organized them such that the treatment 

information for each LTPP test section could be easily retrieved and analyzed. For all LTPP test 

sections that received one or more treatments and for each treatment type, the pavement 

condition and distress data were organized in two different groups—before treatment and after 

treatment. Such grouping was crucial to accurately model the pavement performance before and 

after treatment and to estimate the treatment benefits.

Traffic Data

The research team extracted the traffic data for each test section from the traffic module of the 

LTPP database. The equivalent single-axle load (ESAL) data in the TRF_ESAL_COMPUTED 

table were used to group the various test sections. The groupings were used to assess the impact 

of various variables on pavement performance, the longevity of the pavement sections, and the 

effectiveness of the pavement treatments.

FWD Data

The research team extracted FWD data from the FWD measurements folder of the LTPP 

database to analyze the possible relationships between the measured deflection and pavement 

distress and/or conditions. The peak pavement deflection measured at each of the seven or nine 

sensors for different loads (different drop heights) were extracted and organized for analyses. 

Further, the time-series LTEs were also extracted. Finally, the FWD data of all test sections 

included in the SMP were extracted and organized for analyses. Because, for each test site 

included in the SMP, the FWD data were collected at different time and temperature, the data 

were analyzed to do the following:

• Check the accuracy of existing temperature correction procedures and develop a global 

temperature correction function. 

• Determine whether the time-dependent FWD data could be used to establish a threshold 

value to trigger structural treatment or as an indicator of the structural integrity of the 

pavement sections.

Climatic Data

North America has four climatic regions: DF, DNF, WF, and WNF. The climatic regions were 

obtained from the TRF_ESALS_INPUTS_SUMMARY table of the traffic module in the LTPP 

database. The criterion established by the LTPP Program to identify wet and dry climates was

based on annual precipitation. Regions with annual precipitation of less than 20 inches (50.8 cm)

per year were considered dry. The classification of freeze or no freeze was based on the Freezing 

Index. Test sites in regions where the annual Freezing Index was greater than 150 degree-days 

were considered in a freezing climatic region. 
84

STATUS OF THE CONDITION AND DISTRESS DATA

As stated in the previous section, for each LTPP test section in the SPS and GPS experiments, 

and for each pavement treatment type, all available before treatment and after treatment

condition and distress data were downloaded and organized in spreadsheet format for analyses. 

Table 21 summarizes the alligator, longitudinal, and transverse cracking data of all SPS-1 test 

sections located in Montana (State code 30). Each row in the table represents one pavement test 

section. The columns indicate the treatment types and the number of pavement condition and 

distress surveys (i.e., number of time-series data points) that have been conducted before and 

after each treatment. For example, test section 0113 was subjected to crack sealing, aggregate 

seal coat, and two additional crack sealing treatments since its assignment to the LTPP Program. 

The number 5 under the first before treatment column indicates that five time-series data points 

(surveys) were available in the LTPP database taken before the first crack sealing treatment was 

applied. The number 2 under the after treatment/before treatment column indicates that two data 

points were available in the database taken after the first crack sealing treatment and before the 

aggregate sealing treatment. The numbers under the other after treatment/before treatment

columns indicate the number of data points available in the LTPP database that were taken after 

the previous treatment and before the next treatment. Finally, four time-series data points were

available in the LTPP database taken after the last crack sealing treatment. Note that the number 

of before treatment data points taken before the first crack sealing application and the number of 

after treatment data points taken after the last crack sealing treatment were greater than three, and 

hence, the data can be modeled as a function of time. 

A similar summary for each SPS and GPS experiment in each State was made. The summaries 

and the information listed in table 21 were used to identify the test sections and the treatments 

for which three or more time-series condition or distress data points were available that were 

collected before and/or after a particular treatment. 
85

Table 21. Number of cracking data points available before treatment and after treatment for LTTP SPS-1 test sections in 

Montana.

State 

(Code)

SHRP 

ID BT

Treatment 

Type AT/BT

Treatment 

Type AT/BT

Treatment 

Type AT/BT

Treatment 

Type AT

Montana

(30)

0113 5 CS 2 ASC 1 CS 1 CS 4

0114 10 CS 3 ASC 2 CS 1 CS 4

0115 5 CS 2 ASC 1 CS 1 CS 4

0116 5 CS 2 ASC 1 CS 1 CS 4

0117 5 CS 2 ASC 1 CS 1 CS 4

0118 5 CS 2 ASC 1 CS 1 CS 4

0119 5 CS 2 ASC 1 CS 1 CS 4

0120 5 CS 2 ASC 1 CS 1 CS 4

0121 5 CS 2 ASC 1 CS 1 CS 4

0122 5 CS 2 ASC 1 CS 1 CS 4

0123 5 CS 2 ASC 1 CS 1 CS 4

0124 5 CS 2 ASC 1 CS 1 CS 4

CS = Crack sealing.

ASC = Aggregate seal coat.

BT = Before treatment.

AT = After treatment.

AT/BT = After treatment for the previous treatment and before treatment for the last treatment.
86

STATUS OF THE MAINTENANCE AND REHABILITATION DATA

As stated earlier, the maintenance and rehabilitation actions and their time of application were 

compiled for analysis. The number of test sections in the LTTP SPS and GPS experiments with 

and without treatments are summarized in table 22 and table 23. 

Table 22. Number of LTTP SPS test sections with available treatment data in the database. 

Treatment Status

Number of Sections for each SPS Experiment Number 

Designation

1 2 3 4 5 6 7 8 9 Total

With treatments 163 106 408 192 202 169 38 13 102 1,393

Without treatments 82 101 37 28 2 1 1 40 35 327

Total 245 207 445 220 204 170 39 53 137 1,720

Table 23. Number of LTTP GPS test sections with available treatment data in the database.

Treatment Status

Number of Sections for each GPS Experiment 

Number Designation

1 2 3 4 5 6 7 9 Total

With treatments 197 121 96 56 50 50 27 12 609

Without treatments 36 23 37 13 35 15 8 13 180

Total 233 144 133 69 85 65 35 25 789

Test sections in the LTTP SPS-1 through -7 and LTTP GPS-6, -7, and -9 were included in the 

analyses to assess the impacts of design variables and treatment benefits. The available data for 

the untreated test sections were used as control sections or to estimate the service period of the 

test section.

ANALYSES PROCEDURES

Most of the proposed analyses for the evaluation of the effectiveness of the various pavement 

treatments are based on the determination and evaluation of the relationships between the before 

and after treatment pavement performance. Such pavement performance is a function of the 

available time-dependent pavement condition and distresses data and the corresponding rates of 

pavement deterioration. To model, with some degree of certainty, the condition and distress data 

over time using nonlinear mathematical functions, a minimum of three time-series data points are 

required before and/or after treatment. Two or fewer data points do not define the parameters of 

the nonlinear mathematical functions representing the data. Examination of the available data 

points in the LTPP database indicates that, for a significant number of test sections, only two data 

points were available before and/or after treatment. To enhance the number of available data and 

to increase the number of test sections that could be analyzed, several actions were proposed in 

the interim report, discussed during the project meeting in Washington, DC, and implemented in 

the analyses. The actions taken are described in the following subsections.
87

Addition of One Data Point Immediately After Certain Treatments

Often, the pavement conditions and distresses were not measured immediately after construction 

or after treatment application. Depending on the treatment type, the condition and distress values 

after some treatment actions can be logically and reasonably assumed. Therefore, for all newly 

constructed SPS-1 and -2 test sections and for all other test sections where AC overlay or milland-fill treatments were applied, one can reasonably assume that at 0.01 years (3 days) after 

construction, the initial value of the rut depth, faulting, and the total length of each crack type 

would be negligible. Because a 0.0 data point is not allowed in the mathematical functions 

reported in the literature and used in modeling the data, the initial pavement distress and 

condition at the elapsed time of 0.01 years after construction were assigned the following 

insignificant values:

• Rut depth: 0.01 mm for the LTPP data and 0.01 inches for the State data.

• Transverse cracks: 0.01 m for the LTPP data and 0.01 ft for the State data.

• Longitudinal cracks: 0.01 m for the LTPP data and 0.01 ft for the State data.

• Alligator cracks: 0.01 m2for the LTPP data and 0.01 ft2for the State data.

This assumption supports the addition of one extra data point for use in the analyses of pavement 

performance. Unfortunately, no initial IRI can be reasonably assumed. To illustrate, in 

Oklahoma, skin patching was applied to 12 SPS-1 test sections. The LTPP database contained

more than three time-series pavement condition and distress data points that were collected after 

the skin patching was performed, but only two data points were available that were collected 

before the treatment. Because all SPS-1 test sections were newly constructed, one data point 

could be assumed indicating that at 0.01 years after construction, the magnitudes of rut depth, 

crack length, and faulting were the same as those listed in the previous bullets. The addition of 

such data points made the analyses of the before treatment pavement performance possible. Once 

again, such an assumption was reasonable and logical because for flexible pavements, the 

smooth-drum rollers typically used in the compaction of the original HMA or overlays or milland-fill treatments produce smooth and flat pavement surfaces with no rutting or cracking. The 

LTPP treatments that were considered for this action are listed in table 24. This addition of a data 

point immediately after treatment was applied only to pavement segments where only two before 

treatment and/or after treatment data points were available. If fewer than two data points were

available, the procedure would not yield three data points and hence it was not used. The 

addition of such data points significantly enhanced the number of available pavement segments 

for analyses. Note that no data points were added to any pavement segment that was subjected to 

any treatments not listed in table 24.
88

Table 24. Condition or distress type eligible for data addition for different treatments.

Pavement Treatment Type

LTPP 

Treatment 

Code

Pavement Condition or Distress 

Type Eligible for Data Addition IRI Rut 

Depth

Longitudinal CrackingTransverse CrackingAlligator CrackingFaulting

Grinding surface 12 — — — — — X

Reconstruction (removal and 

replacement)

18 — X X X X X

AC overlay 19 — X X X X X

PCC overlay 20 — N/A X X N/A X

Surface treatment, single layer 28 — — X X X —

Surface treatment, double layer 29 — — X X X —

Surface treatment, three or more layers 30 — X X X X —

Aggregate seal coat 31 — — X X X —

Hot-mix recycled AC 43 — X X X X X

Cold-mix recycled AC 44 — X X X X X

Heater scarification, surface recycled 

AC

45 — X X X X X

Recycled PCC 48 — N/A X X N/A X

Mill off AC and overlay with AC 51 — X X X X X

Mill off AC and overlay with PCC 52 — N/A X X N/A X

Mill existing pavement and overlay 

with hot-mix recycled AC

55 — X X X X X

Mill existing pavement and overlay 

with cold-mix recycled AC

56 — X X X X X

— Indicates not eligible for data point addition.

N/A = Not applicable.

Table 25 summarizes the status of the cracking data of all SPS-1 test sections located in the DNF

region. Similar tables for all pavement condition and distress data types and other LTPP 

experiments and climatic regions were submitted to FHWA and are available from the LTPP 

Customer Support Services.(79)
89

Table 25. Summary of cracking data for LTPP SPS-1 test sections located in the DNF region.

A B C D E F G H I J K L M N

Climatic 

Region State (Code)

Treatment Data Number of Treatment Applications Number of Treatment 

Applications That Can Be 

Analyzed Before and After 

Treatment

Type

Number 

of Test 

Sections Total

Three or More Data 

Points

One Assigned Data Point 

After Treatment (0.01 

Years)

BT 

and

AT

BT 

Only

AT 

Only

BT and

AT

BT 

Only

AT 

Only

BT and

AT

BT 

Only

AT 

Only

DNF

Arizona (4)

CS 6 8 2 4 2 0 0 0 2 4 2

FDP 1 1 0 1 0 0 0 0 0 1 0

PHP 1 1 0 1 0 0 0 0 0 1 0

SS 6 6 4 0 2 0 0 0 4 0 2

New Mexico (35) GS 2 2 0 2 0 0 0 2 2 0 0

Oklahoma (40) MPSP 1 1 1 0 0 0 0 0 1 0 0

SP 12 12 0 0 12 0 12 0 12 0 0

Texas (48)

ACOL 12 12 0 1 1 0 0 0 0 1 1

ASC 11 11 0 0 0 0 0 11 0 0 11

GS 3 3 0 0 3 0 3 0 3 0 0

MOAC 12 12 12 0 0 0 0 0 12 0 0

MPSP 11 11 0 11 0 0 0 0 0 11 0

ACOL = AC overlay.

ASC = Aggregate seal coat.

CS = Crack sealing.

FDP = Full-depth patching.

GS = Grinding surface.

MOAC = Mill and overlay with AC.

MPSP = Machine premix spot patching.

PHP = Pot holes patching.

SP = Skin patching.

SS = Slurry seal.

BT = Before treatment.

AT = After treatment.
90

The following describes the data that appear in each of the columns in table 25:

• Column A: Climatic region.

• Column B: State and State code.

• Column C: Treatment type.

• Column D: Number of SPS-1 test sections.

• Column E: Number of times treatments were applied. When the number in column E is 

greater than the number in column D, it implies that at least one test section received the 

treatment more than one time.

• Column F: Number of treatment applications for which three or more time-series data 

points are available that were collected before and after treatment.

• Column G: Number of treatment applications for which three or more time-series data 

points are available that were collected before treatment only.

• Column H: Number of treatment applications where three or more time-series data 

points are available that were collected after treatment only.

• Column I: Number of treatment applications where one data point can be logically 

assumed to have been collected immediately after treatment (as stated earlier as 

0.01 year), which yields three time-series data points before and after treatment.

• Column J: Number of treatment applications where one data point can be logically 

assumed to have been collected immediately after treatment (i.e., the addition of the 

0.1-year point described earlier in this section), which yields three time-series data points 

that were collected before treatment only.

• Column K: Number of treatment applications where one data point can be logically 

assumed to have been collected immediately after treatment (i.e., the addition of the 

0.1-year point described earlier in this section), which yields three time-series data points 

that were collected after treatment only.

• Column L: Number of test sections that can be analyzed before and after treatment.

• Column M: Number of test sections that can be analyzed before treatment only.

• Column N: Number of test sections that can be analyzed after treatment only.

At the time of this report, there were 1,555 LTPP test sections (supplemental sections were not 

included) in the SPS-1 through -7 and in the GPS-6, -7, and -9 experiments. The majority of 

these test sections (1,301) were treated at least 1 time during their assignment period. The total 

number of treatment applications was 2,674 (some test sections received more than 1 treatment). 
91

For new construction (SPS-1 and -2 test sections) and for overlay and mill-and-fill treatments, 

one rut depth, cracking length, and faulting data point was added at 0.01 years after construction.

Table 26 lists the number of test sections that could be analyzed before and after treatment, the 

number of test sections that could be analyzed before treatment only, and the number of test 

sections that could be analyzed after treatment after the addition of this data point.

Table 26. Summary of treatments applied to LTTP SPS-1 through -7 and GPS-6, -7, and -9

sections analyzed in this study.

Number 

of Test 

Sections

Number of 

Treated 

Test 

Sections

Number of 

Treatment 

Applications

Pavement 

Distress/ 

Condition

Number of Treatment 

Applications Analyzed

BT and 

AT BT Only

AT 

Only

1,555 1,301 2,674

Cracking 278 463 925

IRI 468 558 911

Rut depth 394 453 747

Faulting 42 70 108

Total Analyzed 1,182 1,544 2,691

BT = Before treatment.

AT = After treatment.

Using the Control Section Data for Before Treatment Conditions

Several of the LTPP experiments, including SPS-3 through -6, were designed with a control 

section (untreated) adjacent to the test sections (which were subjected to various treatment types). 

The control section was subjected to almost the same traffic and environmental loading and had 

almost identical structure and subgrade support characteristics. For this reason, the performance 

data of each control section could be used to represent the before treatment performance data of 

the adjacent test sections when only two or fewer before treatment data points are available in the 

database for the given test section. For example, SPS-3 test section A310 (see figure 45) in 

Maryland had only one cracking data point (not shown in the figure) collected before an overlay 

treatment was performed. Six cracking data points were available after the overlay was 

performed. To analyze the before treatment conditions of test section A310, the performance data 

of the control section A340 (see figure 46), which was not subjected to treatment, was used to 

represent test section A310 before-treatment performance.
92

1 ft = 0.305 m.

Figure 45. Graph. Total longitudinal cracking versus time for LTTP SPS-3 test section 

A310 in Maryland.

1 ft = 0.305 m.

Figure 46. Graph. Total longitudinal cracking versus time for LTPP SPS-3 control section 

A340 in Maryland.
93

In some cases, where no control sections were assigned, the linked GPS test sections associated 

with the SPS sections were used as control sections.

(60) Linked GPS test sections were under the 

GPS experiment and were located adjacent to the SPS test sections. They had traffic loading and 

structure similar to the SPS test sections to which they were linked. Some of the linked GPS 

sections were also treated. However, the before treatment data (see figure 47) could still be used 

as the before treatment data for the SPS-3 test section.

1 ft = 0.305 m.

Figure 47. Graph. Total longitudinal cracking versus elapsed time for LTPP GPS-1634 

linked test section to SPS-3 experiment in Maryland.

Cracking data collected before the overlay could also be used as before treatment data for test 

section A310. Comparisons of the pavement condition and distress data between the control and 

the linked section that were related to the conditions of the test section were made to verify 

whether the data of the control and/or linked sections were indeed similar to the available before 

treatment data points of the test section. Finally, if the data of the control section represented the 

before treatment data of the test section, the performance of the two sections were compared to 

determine the benefits of the treatment applied to the test section

For some test sections, such as LTTP SPS-3 test section A350 in New York, the reported before 

treatment longitudinal cracking was about 984 ft (300 m), as shown in figure 48. The 

longitudinal cracking data of the associated control section A340 indicated 197 to 328 ft (60 to 

100 m) of cracking, as shown by the open symbols in figure 48. It should be noted that the 

control section was not subjected to any treatment. Nevertheless, in this and similar cases, the 

data from the control sections were not used because they were not representative of the before 

treatment pavement performance of the test section in question. Note that the use of the control 
94

section data in place of the before treatment data significantly increased the number of available 

test sections for analysis.

1 ft = 0.305 m.

Figure 48. Graph. Total longitudinal cracking versus elapsed time for LTTP SPS-3 test 

section A350 and A340 control section in New York.

Although a treated pavement section may have had three data points collected before treatment 

and/or after treatment, it may or may not have been accepted for analysis. The before treatment 

and/or the after treatment time-series data of some of these test sections indicated that the 

pavement condition and/or distress was improving over time without the application of any 

treatment, as shown in figure 49. 
95

1 inch/mi = 0.0158 m/km.

Figure 49. Graph. IRI versus elapsed time for LTTP SPS-1 test section 0119 in Texas.

In the absence of a pavement treatment, most pavement sections deteriorate over time. When the 

pavement condition and/or distress data indicated improvement over time, without the 

application of treatment, the data precipitated negative parameters of the pavement performance 

model (negative slope) (i.e., improved condition and/or distress without treatment). Such 

pavement condition and distress trends could occur for various reasons including the following:

• Human error and/or inaccuracy while collecting the data. The subjectivity of assigning 

distress severity levels and estimating the extent of the distress could generate 

inaccuracies in the time-series data. Properly calibrated sensor measured data would not 

exhibit this problem because no human subjectivity was involved.

• Data inaccuracy due to the employed equipment, such as calibration, malfunction, or 

changing equipment type between surveys over time.

• Environmental conditions from one data collection cycle to the next. For example, the 

crack opening was wider on cold days than on warmer days as a result of thermal 

expansion and contraction. This could change the assigned crack severity level and/or the 

observance of the length of the cracks. Likewise, temperature differential could cause 

curling on certain days, which would influence the measured pavement roughness in 

terms of IRI.

When the pavement condition and/or distress showed improvement over time without any 

treatment application, the data yielded infinite RFPs and/or RSPs, and the pavement performance 

could not be assessed. For example, the data in figure 49 and the associated exponential equation 

indicate improvement of the IRI over time and a negative exponential power component. That is, 

according to the given data and the equation, the IRI would never reach the threshold value, and 
96

therefore RFP was infinite, which was not practical. Consequently, any time-series condition or 

distress data showing improvement over time without the application of treatment was not 

included in the analyses of the pavement performance.

Severity Level

Finally, the analyses of cracking data in this study were based on the sum of the data for low, 

medium, and high severity levels.

SUMMARY AND CONCLUSIONS

This chapter presented the various data elements of the more than 2,500 test sections included in 

the LTPP Program that were downloaded from the 6 data volumes housed in the LTPP database 

Standard Data Release 28.0. The data were organized in a special format and readied for 

analyses. In addition, the pavement management databases from three State transportation 

departments—CDOT, WSDOT, and LADOTD—were requested and received. From each 

database, several pavement projects that were subjected to certain treatments in the past were 

identified, and their data were downloaded from the respective databases and formatted for 

analyses. Based on thorough and exhaustive review of the data, the research team reached the 

following conclusions:

• At the time of this study, 800 test sections were still active, whereas 1,700 were 

decommissioned.

• The manually collected pavement distress data were more numerous and consistent than 

the semi-automated data. The manual data were used in the analyses of the structural 

pavement performance of each test section.

• The database for some of the test sections contained only two data points over time 

before and or after treatment. For those test sections that were subjected to overlay or 

mill-and-fill treatments, an initial data point was added to rut depth and to each cracking 

type data.

• The addition of one initial data point was based on engineering logic and increased the 

number of test sections that could be analyzed.

• A total of 2,674 treatments were applied to 1,301 test sections of the 1,555 test sections 

included in the SPS-1 through -7 and GPS-6, -7, and -9 experiments.

• For each treated test section, the data were organized for the analyses of pavement 

performance before, after, or before and after treatment, depending on the data availability.
97

CHAPTER 5. LTPP DATA ANALYSES OF FLEXIBLE PAVEMENTS

For all LTPP test sections in SPS-1 through -7 and GPS-6, -7, and -9, the time-series pavement 

conditions, distresses, and some of the FWD data were downloaded and organized in spreadsheet 

format for analyses. The data from these LTPP test sections and a few pavement sections from 

the CDOT, LADOTD, and WSDOT databases were modeled using the proper mathematical 

function and were subjected to analyses. The procedures and the results of the analyses of the 

LTPP flexible pavement condition and distress data are presented in this chapter, while the 

procedure and results for rigid pavement condition and distress data are presented in chapter 6. 

Results of the analyses of the FWD data are presented and discussed in chapter 7. Finally, the 

results of the analyses of the CDOT, LADOTD, and WSDOT data are presented and discussed in 

chapter 9. The information in this chapter is arranged in the following sections:

• Modeling of the Time-Series Pavement Condition and Distress Data.

• Impacts of Climatic Regions, Drainage, and AC Thickness on Pavement Condition and 

Distress Using the LTPP SPS-1 Test Sections.

• Summary, Conclusions, and Recommendations, LTPP SPS-1.

• Results of the Analyses of the LTPP SPS-3 Test Sections. 

• Summary, Conclusions, and Recommendations, LTPP SPS-3.

• Results of the Analyses of the LTPP SPS-5 Test Sections. 

• Summary, Conclusions, and Recommendations, LTPP SPS-5.

• Results of the Analyses of the LTPP GPS-6 Test Sections. 

• Summary, Conclusions, and Recommendations, LTPP GPS-6.

• ORCSE Method.

• Summary, Conclusions, and Recommendations, ORCSE Model.

MODELING THE TIME-SERIES PAVEMENT CONDITION AND DISTRESS DATA 

The time-series pavement condition and distress data of all test sections in the SPS-1 through -7 

and GPS–6, -7, and -9 experiments were downloaded, organized, and modeled using the proper 

mathematical functions based on the type of pavement condition (IRI) or distress (rut depth, and 

cracking). The mathematical functions listed in table 27 and shown in figure 50 were selected 

based on reported trends and mechanisms of pavement deterioration.(37,81,78) For example, rutting 

typically occurs early in the asphalt pavement’s life, and its accumulation rate decreases over time 

as the pavement materials densify under traffic loads.
98

Table 27. Description of the mathematical functions used in the analyses of the pavement 

distress and condition data.

Pavement Condition or Distress Function Type

IRI (inches/mi (m/km)) Exponential function

Rut depth (inches (mm)) Power function

Cracking (length, area, or percent) Logistic (S-shaped)

Figure 50. Graph. Exponential, power, and logistic (S-shaped) curves.

Therefore, a power function is typically used to model the time-series rut depth data. On the 

other hand, pavement roughness usually increases exponentially as the pavement ages, 

deteriorates, and becomes uneven causing increases in the dynamic effects of traffic loads. 

Hence, an exponential function is typically used to model the pavement roughness (IRI). Finally, 

the propagation of pavement cracks usually follows three stages. In the first stage, a few cracks 

appear in the early pavement life; their number and length increase exponentially. In the second 

stage, the number and length of cracks increase almost linearly over time. In this stage, a few 

new cracks are initiated and most existing cracks approach their maximum possible lengths (lane 

width or the pavement section length). In the third stage, the number of cracks and their length 

reach equilibrium as shown by the logistic curve in figure 50. Given this scenario, the modeling 

of crack propagation over time could be achieved using two different functions, depending on 

the availability of the data. If the cracking data are available over a short period of time after 

construction (stage one data only), an exponential function could be used to model the data. On 

the other hand, if the cracking data are available when the pavement is old (stage three only), a 

power function could be used. The modeling of the crack propagation using the logistic function 

cannot be confidently achieved unless at least four data points are available spanning the three 

crack propagation stages. To reduce the effect of the problem and to increase the number of test 
99

sections included in the analyses, one crack saturation point was assumed for each type of 

cracking. The assumed crack saturation points used throughout this study are listed in table 28. 

The assumption of the crack saturation points was based on engineering logic. For example, the 

saturation point for alligator cracking is the entire surface area of the pavement section, whereas 

the saturation point for longitudinal cracking is three cracks along the entire pavement section 

length. Note that the square, circle, and triangle symbols in figure 50 represent measured data. 

The solid portions of the curves are fit to the measured data while the dashed portions are 

forecasted based on the mathematical function fitting the data. It should be noted that the three 

mathematical models fit the LTPP pavement condition and distress data for all test sections and 

State data.

Table 28. Crack saturation values used in the analyses of the pavement cracking data.

Cracking Type

Saturation Value

Reason

Per 500 ft

(152.4 m)

LTPP Test 

Section

Per 

0.1 km

Per 

0.1 mi

Alligator cracking 5,906 ft2

(549 m2)

360 m26,336 ft2100-percent of section 

cracked (12-ft (3.66-m)

lane width)

Longitudinal cracking1 1,500 ft 

(457.2 m)

300 m 1,584 ft Three cracks along the 

entire section length

Transverse cracking 

(length), flexible 

pavements

500 ft

(152.4 m)

100 m 528 ft One crack every 12 ft 

(3.65 m)

Number of transverse 

cracks, flexible 

pavements1

42 28 44 One crack each 3.65 m 

(12 ft)

Transverse cracking 

(length), rigid pavements

375 ft 

(114 m)

75 m 396 ft One crack per slab (16-ft 

(4.87-m) joint spacing)

Number of transverse 

cracks, rigid pavements1

31 21 33 One crack per slab (16-ft 

(4.87-m) joint spacing)

1Data included for convenience. The analyses were conducted using the measured crack lengths and alligator 

cracked areas.

After selecting the proper mathematical function, the least squares regression technique was used 

to determine the statistical parameters of the selected mathematical functions. The least squares 

regression technique is based on minimizing the sum of the squared differences (error) between 

the calculated and the measured data.

(3) To expedite the analyses, a MATLAB®-based computer 

program was written to complete the following functions for each pavement condition and 

distress dataset of each LTPP test section and for each pavement treatment type (see the program 

flowchart in figure 51):

• Read all available time-series data from a Microsoft® Excel spreadsheet.

• Separate the data into two parts, before and after treatment.
100

• Check the available number of time-series data points before and after treatment. 

• If three or more data points are available that were collected before and/or after the 

treatment, use the proper mathematical function to fit the data and obtain the statistical 

parameters of the function.

• Organize the results (section identification, treatment date and type, climatic region, 

traffic, the last collected before treatment data point, the first collected after treatment 

data point, the number of before treatment and after treatment data points, and the 

statistical parameters) in a Microsoft® Excel spreadsheet format.
101

Figure 51. Illustration. Flowchart of the MATLAB® program.
102

The MATLAB® output data were then subjected to further analyses to estimate the following 

parameters for each test section and for each treatment type:

• The before treatment RFP, RSP, and CS.

• The after treatment RFP, RSP, and CS.

• Any changes in RFP or RSP resulting from the pavement treatment. The change in RFP 

or RSP is defined as the differences between the before treatment RFPs or RSPs and the 

after treatment RFPs or RSPs.

• The time period for the reoccurrence of the previous pavement conditions (the last

collected data point before the application of the preservation treatment).

• Instantaneous change in the pavement conditions and distresses due to treatment (also 

called PJ).

IMPACTS OF CLIMATIC REGIONS, DRAINAGE, AND AC THICKNESS ON 

PAVEMENT PERFORMANCE USING THE LTPP SPS-1 TEST SECTIONS

Recall that the main objective of the SPS-1 experiment is to study the effects of the conditions in

climatic regions and the following structural factors on pavement performance:

(65)

• Presence or absence of a drainage layer.

• AC thickness (4 or 7 inches (102 or 178 mm)).

• Base type (dense-graded aggregate base, asphalt-treated base, permeable asphalt-treated 

base, or a combination thereof).

• Base thickness (8, 12, or 16 inches (203, 305, or 406 mm).

The analyses of the impacts of the various variables were accomplished in the following steps:

• Step 1: For each pavement test section in the SPS-1 experiment, each of the timedependent pavement condition (IRI) and distress (rut depth, and alligator, transverse, and 

longitudinal cracking) data were used to calculate RFP and RSP of that section from the 

time of construction to the time when the pavement condition or distress reached the 

appropriate threshold values. The reason for calculating RFP and RSP from the 

construction data (surface age is 0 years) was that the dates of construction and last data 

collection for different test sections were not the same. This implies that the reference 

time for all SPS-1 test sections was taken as the date of construction.

• Step 2: For each pavement condition and distress type, the resulting RFPs and RSPs and 

other inventory data (such as SHRP ID, State, AC thickness, drainage, base type and 

thickness, and so forth) were then organized into a Microsoft® Excel spreadsheet format.
103

• Step 3: For each SHRP ID and for each pavement condition and distress type, the 

minimum and maximum RFPs and RSPs and their averages were calculated and listed in 

the Microsoft® Excel spreadsheet.

• Step 4: The data were then divided into the following climatic regions, groups, and 

subgroups. The main objective of the division was to separate the design variables 

affecting pavement performance.

o 1—Climatic Regions: The results were divided into four climatic regions: WF, 

WNF, DF, and DNF.

o 2—AC Thickness Groups: The results in each climatic region were then divided into 

two groups based on the thickness of the AC (4 and 7 inches (102 or 178 mm)).

o 3—Drainage Subgroup: The results in each AC thickness group were then divided 

into two drainage subgroups (presence and absence of drainage). It should be noted 

that various attempts were made to divide the results in each drainage subgroup into 

base thickness and base type subgroups and into three traffic levels. Unfortunately, 

none of the attempts was successful because any further division yielded an 

insignificant number of test sections in each subgroup such that no decisions could be 

made with any level of certainty. Therefore, the impacts of the base thickness and 

type were not studied any further. 

The impacts of the conditions in the four climatic regions (WF, WNF, DF, and DNF), the AC 

thickness (4 and 7 inches (102 or 178 mm)), and drainable and undrainable bases on the 

pavement performance in terms of RFP and RSP were analyzed. The detailed results of the 

analyses (the calculated and rounded minimum, maximum, and average RFPs or RSPs for the 

SPS-1 test sections) were submitted to FHWA and are available from the LTPP Customer 

Support Services.(79) For convenience, the detailed results are summarized in table 29 through 

table 34.

The data in table 29 through table 34 address the impact of the climatic regions, the AC thickness, 

and drainable and undrainable bases on the pavement performance (in terms of RFPs and RSPs) of 

the SPS-1 test sections. The figures in the tables (which are rounded to whole numbers) indicate 

the differences in years in RFPs or RSPs of the SPS-1 test sections having the column heading 

parameters compared with RFPs and RSPs of the SPS-1 test sections having the row heading 

parameters. Thus, in table 29 through table 33, the diagonal indicated by asterisks represents the 

line of symmetry. If the table were folded along the diagonal, the aligned numbers from above and 

from below the diagonal would be the same but with different sign. The proper reading of the data 

in the tables is illustrated in the two examples following the tables.
104

Table 29. Summary of the results of analyses of the impacts of design factors on RFP of LTPP SPS-1 test sections based on 

IRI (years).

Climatic Region, 

AC Thickness, and

Drainage Subgroup

Climatic Region, AC Thickness, and Drainage Subgroup

WF WNF DF DNF

4-inch AC 7-inch AC 4-inch AC 7-inch AC 4-inch AC 7-inch AC 4-inch AC 7-inch AC

D ND D ND D ND D ND D ND D ND D ND D ND

WF

4-inch

AC

D * –5 0 –2 2 2 2 2 0 2 2 2 2 1 2 2

ND 5 * 4 3 7 7 7 7 5 7 7 7 7 6 7 7

7-inch

AC

D 0 –4 * –1 2 2 2 2 1 2 2 2 2 1 2 2

ND 2 –3 1 * 4 3 4 4 2 4 4 4 4 3 4 4

WNF

4-inch

AC

D –2 –7 –2 –4 * 0 0 0 –2 0 0 0 0 –1 0 0

ND –2 –7 –2 –3 0 * 0 0 –1 0 0 0 0 –1 0 0

7-inch

AC

D –2 –7 –2 –4 0 0 * 0 –2 0 0 0 0 –1 0 0

ND –2 –7 –2 –4 0 0 0 * –2 0 0 0 0 –1 0 0

DF

4-inch

AC

D 0 –5 –1 –2 2 1 2 2 * 2 2 2 2 1 2 2

ND –2 –7 –2 –4 0 0 0 0 –2 * 0 0 0 –1 0 0

7-inch

AC

D –2 –7 –2 –4 0 0 0 0 –2 0 * 0 0 –1 0 0

ND –2 –7 –2 –4 0 0 0 0 –2 0 0 * 0 –1 0 0

DNF

4-inch

AC

D –2 –7 –2 –4 0 0 0 0 –2 0 0 0 * –1 0 0

ND –1 –6 –1 –3 1 1 1 1 –1 1 1 1 1 * 1 1

7-inch

AC

D –2 –7 –2 –4 0 0 0 0 –2 0 0 0 0 –1 * 0

ND –2 –7 –2 –4 0 0 0 0 –2 0 0 0 0 –1 0 *

* Indicates the line of symmetry along the diagonal of the table.

1 inch = 25.4 mm.

D = Drainable base.

ND = Undrainable base.
105

Table 30. Summary of the results of analyses of the impacts of design factors on RFP/RSP of LTPP SPS-1 test sections based 

on rut depth (years).

Climatic Region, 

AC Thickness, and

Drainage Subgroup

Climatic Region, AC Thickness, and Drainage Subgroup

WF WNF DF DNF

4-inch AC 7-inch AC 4-inch AC 7-inch AC 4-inch AC 7-inch AC 4-inch AC 7-inch AC

D ND D ND D ND D ND D ND D ND D ND D ND

WF

4-inch

AC

D * –5 –1 –3 5 5 5 5 4 6 6 6 6 6 6 6

ND 5 * 4 2 10 10 10 10 9 11 11 11 11 11 11 11

7-inch

AC

D 1 –4 * –3 6 6 5 6 5 7 7 7 7 7 7 7

ND 3 –2 3 * 8 9 8 9 7 9 9 9 9 9 9 9

WNF

4-inch

AC

D –5 –10 –6 –8 * 0 0 1 –1 1 1 1 1 1 1 1

ND –5 –10 –6 –9 0 * 0 0 –1 1 1 1 1 1 1 1

7-inch

AC

D –5 –10 –5 –8 0 0 * 1 –1 1 1 1 1 1 1 1

ND –5 –10 –6 –9 –1 0 –1 * –2 1 1 1 1 1 1 1

DF

4-inch

AC

D –4 –9 –5 –7 1 1 1 2 * 2 2 2 2 2 2 2

ND –6 –11 –7 –9 –1 –1 –1 –1 –2 * 0 0 0 0 0 0

7-inch

AC

D –6 –11 –7 –9 –1 –1 –1 –1 –2 0 * 0 0 0 0 0

ND –6 –11 –7 –9 –1 –1 –1 –1 –2 0 0 * 0 0 0 0

DNF

4-inch

AC

D –6 –11 –7 –9 –1 –1 –1 –1 –2 0 0 0 * 0 0 0

ND –6 –11 –7 –9 –1 –1 –1 –1 –2 0 0 0 0 * 0 0

7-inch

AC

D –6 –11 –7 –9 –1 –1 –1 –1 –2 0 0 0 0 0 * 0

ND –6 –11 –7 –9 –1 –1 –1 –1 –2 0 0 0 0 0 0 *

* Indicates the line of symmetry along the diagonal of the table.

1 inch = 25.4 mm.

D = Drainable base.

ND = Undrainable base.
106

Table 31. Summary of the results of analyses of the impacts of design factors on RSP of LTPP SPS-1 test sections based on 

alligator cracking (years).

Climatic Region, 

AC Thickness, and

Drainage Subgroup

Climatic Region, AC Thickness, and Drainage Subgroup

WF WNF DF DNF

4-inch AC 7-inch AC 4-inch AC 7-inch AC 4-inch AC 7-inch AC 4-inch AC 7-inch AC

D ND D ND D ND D ND D ND D ND D ND D ND

WF

4-inch

AC

D * –4 –3 –3 0 0 2 2 –3 –5 –2 –3 –5 –6 –1 –2

ND 4 * 1 1 4 3 5 5 0 –2 2 1 –1 –2 3 2

7-inch

AC

D 3 –1 * 0 3 2 4 4 –1 –3 1 0 –2 –3 2 1

ND 3 –1 0 * 3 3 5 5 0 –2 1 0 –1 –3 2 2

WNF

4-inch

AC

D 0 –4 –3 –3 * –1 1 1 –4 –6 –2 –3 –5 –6 –1 –2

ND 0 –3 –2 –3 1 * 2 2 –3 –5 –2 –3 –4 –6 –1 –1

7-inch

AC

D –2 –5 –4 –5 –1 –2 * 0 –5 –7 –4 –5 –6 –8 –3 –3

ND –2 –5 –4 –5 –1 –2 0 * –5 –7 –4 –4 –6 –8 –3 –3

DF

4-inch

AC

D 3 0 1 0 4 3 5 5 * –2 1 1 –1 –3 2 2

ND 5 2 3 2 6 5 7 7 2 * 3 2 1 –1 4 4

7-inch

AC

D 2 –2 –1 –1 2 2 4 4 –1 –3 * –1 –2 –4 1 0

ND 3 –1 0 0 3 3 5 4 –1 –2 1 * –2 –3 2 1

DNF

4-inch

AC

D 5 1 2 1 5 4 6 6 1 –1 2 2 * –1 3 3

ND 6 2 3 3 6 6 8 8 3 1 4 3 1 * 5 4

7-inch

AC

D 1 –3 –2 –2 1 1 3 3 –2 –4 –1 –2 –3 –5 * 0

ND 2 –2 –1 –2 2 1 3 3 –2 –4 0 –1 –3 –4 0 *

* Indicates the line of symmetry along the diagonal of the table.

1 inch = 25.4 mm.

D = Drainable base.

ND = Undrainable base.
107

Table 32. Summary of the results of analyses of the impacts of design factors on RSP of LTPP SPS-1 test sections based on 

longitudinal cracking (years).

Climatic Region,

AC Thickness, and

Drainage Subgroup

Climatic Region, AC Thickness, and Drainage Subgroup

WF WNF DF DNF

4-inch AC 7-inch AC 4-inch AC 7-inch AC 4-inch AC 7-inch AC 4-inch AC 7-inch AC

D ND D ND D ND D ND D ND D ND D ND D ND

WF

4-inch

AC

D * –1 –1 0 5 6 6 5 1 0 –1 0 6 5 6 7

ND 1 * 0 1 6 7 7 6 1 1 0 1 7 6 6 8

7-inch

AC

D 1 0 * 1 6 7 7 6 2 1 0 1 7 6 7 8

ND 0 –1 –1 * 5 6 6 5 0 0 –1 0 6 5 5 7

WNF

4-inch

AC

D –5 –6 –6 –5 * 1 1 0 –5 –5 –6 –5 1 0 0 2

ND –6 –7 –7 –6 –1 * 0 –2 –6 –6 –7 –6 –1 –1 –1 1

7-inch

AC

D –6 –7 –7 –6 –1 0 * –1 –6 –6 –7 –6 0 –1 –1 1

ND –5 –6 –6 –5 0 2 1 * –4 –5 –5 –5 1 0 1 2

DF

4-inch

AC

D –1 –1 –2 0 5 6 6 4 * 0 –1 –1 5 4 5 7

ND 0 –1 –1 0 5 6 6 5 0 * –1 0 6 5 5 7

7-inch

AC

D 1 0 0 1 6 7 7 5 1 1 * 1 7 6 6 8

ND 0 –1 –1 0 5 6 6 5 1 0 –1 * 6 5 6 7

DNF

4-inch

AC

D –6 –7 –7 –6 –1 1 0 –1 –5 –6 –7 –6 * –1 0 1

ND –5 –6 –6 –5 0 1 1 0 –4 –5 –6 –5 1 * 1 2

7-inch

AC

D –6 –6 –7 –5 0 1 1 –1 –5 –5 –6 –6 0 –1 * 2

ND –7 –8 –8 –7 –2 –1 –1 –2 –7 –7 –8 –7 –1 –2 –2 *

* Indicates the line of symmetry along the diagonal of the table.

1 inch = 25.4 mm.

D = Drainable base.

ND = Undrainable base.
108

Table 33. Summary of the results of analyses of the impacts of design factors on RSP of LTPP SPS-1 test sections based on 

transverse cracking (years).

Climatic Region,

AC Thickness, and

Drainage Subgroup

Climatic Region, AC Thickness, and Drainage Subgroup

WF WNF DF DNF

4-inch AC 7-inch AC 4-inch AC 7-inch AC 4-inch AC 7-inch AC 4-inch AC 7-inch AC

D ND D ND D ND D ND D ND D ND D ND D ND

WF

4-inch

AC

D * 1 2 1 3 3 4 3 –3 2 –3 0 –1 2 2 0

ND –1 * 2 1 2 2 3 2 –4 1 –4 0 –1 1 1 0

7-inch

AC

D –2 –2 * –1 1 0 1 1 –6 0 –5 –2 –3 –1 0 –2

ND –1 –1 1 * 2 1 2 2 –5 1 –4 –1 –2 0 1 –1

WNF

4-inch

AC

D –3 –2 –1 –2 * –1 0 0 –7 –1 –6 –3 –4 –1 –1 –3

ND –3 –2 0 –1 1 * 1 1 –6 0 –5 –2 –3 –1 0 –2

7-inch

AC

D –4 –3 –1 –2 0 –1 * 0 –7 –1 –6 –3 –4 –2 –1 –3

ND –3 –2 –1 –2 0 –1 0 * –7 –1 –6 –3 –4 –1 –1 –3

DF

4-inch

AC

D 3 4 6 5 7 6 7 7 * 6 1 4 3 5 6 4

ND –2 –1 0 –1 1 0 1 1 –6 * –5 –2 –3 0 0 –2

7-inch

AC

D 3 4 5 4 6 5 6 6 –1 5 * 3 2 5 5 3

ND 0 0 2 1 3 2 3 3 –4 2 –3 * –1 1 2 0

DNF

4-inch

AC

D 1 1 3 2 4 3 4 4 –3 3 –2 1 * 2 3 1

ND –2 –1 1 0 1 1 2 1 –5 0 –5 –1 –2 * 0 –1

7-inch

AC

D –2 –1 0 –1 1 0 1 1 –6 0 –5 –2 –3 0 * –2

ND 0 0 2 1 3 2 3 3 –4 2 –3 0 –1 1 2 *

* Indicates the line of symmetry along the diagonal of the table.

1 inch = 25.4 mm.

D = Drainable base.

ND = Undrainable base.
109

Table 34. Summary of the results of analyses of the effects of climatic regions on the performance of the LTPP SPS-1 

test sections.

Condition 

or Distress 

Type

Climatic 

Region

Climatic Regions and the Percent of Test Sections Where RFP/RSP Was Better, Equal, or Worse Than 

Other Climatic Regions

WF WNF DF DNF

Better Same Worse Better Same Worse Better Same Worse Better Same Worse

IRI

WF — — — 58 4 38 56 28 17 58 42 0

WNF 38 4 58 — — — 77 6 17 8 84 8

DF 17 28 56 17 6 77 — — — 17 72 11

DNF 0 42 58 8 84 8 11 72 17 — — —

Rut depth

WF — — — 83 17 0 82 13 5 73 27 0

WNF 0 17 83 — — — 23 68 9 9 91 0

DF 5 13 82 9 68 23 — — — 0 100 0

DNF 0 27 73 0 91 9 0 100 0 — — —

Alligator 

cracking

WF — — — 67 8 25 42 4 54 42 8 50

WNF 25 8 67 — — — 38 8 54 13 0 87

DF 54 4 42 54 8 38 — — — 46 17 38

DNF 50 8 42 87 0 13 38 17 46 — — —

Longitudinal 

cracking

WF — — — 88 8 4 46 4 50 92 8 0

WNF 4 8 88 — — — 8 29 63 42 42 16

DF 50 4 46 63 29 8 — — — 67 29 4

DNF 0 8 92 16 42 42 4 29 67 — — —

Transverse 

cracking

WF — — — 54 33 13 42 12 46 46 21 33

WNF 13 33 54 — — — 38 54 8 7 50 42

DF 46 12 42 8 54 38 — — — 42 46 12

DNF 33 21 46 42 50 7 12 46 42 — — —

— Indicates no data.
110

• Example 1: This example illustrates how to read the data in table 29 through table 33

using example data from table 29. The first four numbers in the first row below the top 

headings imply the following:

o The average RFP of test sections located in the WF region having 4-inch (102-mm)-

thick AC layers and undrainable bases was 5 years shorter (–5) than compatible test 

sections with drainable bases.

o The average RFP of test sections located in the WF region having 7-inch (178-mm)-

thick AC layers and drainable bases was the same (0 years) as those having 4-inch

(102-mm) thick AC layers with drainable bases.

o The average RFP of test sections located in the WF region having 7-inch (178-mm)-

thick AC layers and undrainable bases was 2 years shorter (2 years) than those having 

4-inch (102-mm)-thick AC layers with drainable bases.

o The average RFP of test sections located in the WNF region having 4-inch (102-mm)-

thick AC layers and drainable bases was 2 years longer (2 years) than those located in 

the WF region having 4-inch (102-mm)-thick AC layers and drainable bases.

• Example 2: This example illustrates how to read the data in table 34. The data in the 

table address the impact of the climatic regions on the pavement condition (IRI) and 

distresses (rut depth and alligator, longitudinal, and transverse cracking). The numbers in 

the table indicate the percent of the test sections with column heading parameters that

performed better, the same, or worse compared with the test sections having the row 

heading parameters. For example, for IRI, the six numbers (three numbers in each of the 

top two populated rows in the three columns under the heading WF) and for rut depth, the 

six numbers (three numbers in each of the next two populated rows) under the same 

three columns heading (WF) imply the following:

o In terms of IRI, 38 percent of the test sections located in the WF region performed 

better than those located in the WNF region, 4 percent performed the same, and 

58 percent performed worse.

o In terms of IRI, 17 percent of the test sections located in the WF region performed 

better than compatible sections located in the DF region, 28 percent performed the 

same, and 56 percent performed worse.

o In terms of rut depth, none of the test sections located in the WF region performed 

better than those located in the WNF region, 17 percent performed the same, and 

83 percent performed worse.

o In terms of rut depth, 5 percent of the test sections located in the WF region 

performed better than compatible sections located in the DF region, 13 percent 

performed the same, and 82 percent performed worse.
111

Consistent with this explanation of table 29 through table 33 and table 34, in the following 

subsections, the discussion of the results shown in those tables is organized according to the 

pavement condition and distress. 

IRI

The average calculated RFPs listed in table 29 indicate that the differences between the average 

RFP of test sections located in different climatic regions and that have 4- or 7-inch (102- or 

178-mm)-thick AC layers with drainable and undrainable bases varied from –1 to 7 years. 

Because the 1-year difference was not significant and was within the data variability, it was

considered a value of zero in the following discussion. Nevertheless, the data in table 29 indicate 

the following:

• In the WF region, the average RFP of test sections having 4-inch (102-mm) AC thickness 

and undrainable bases was about 5 years shorter than the average RFP of compatible test 

sections with drainable bases. The 5-year difference decreased to 2 years when the AC 

thickness of the undrainable test sections increased from 4 to 7 inches (102 to 178 mm). 

That is, the average RFP of test sections having 7-inch (178-mm)-thick AC layers and 

undrainable bases was about 3 years longer than test sections having 4-inch (102-mm)-

thick AC layers and undrainable bases. Finally, for test sections having 7-inch (178-inch)-

thick AC layers and drainable bases, the average RFP was the same as test sections having 

4-inch (102-mm)-thick AC layers and drainable bases and 4 years longer compared with

test sections having 4-inch (102-mm)-thick AC layers and undrainable bases.

• Also in the WF region, the average RFP of the test sections having 4-inch (102 mm)-

thick and 7-inch (178-mm)-thick AC layers and drainable and undrainable bases was 2 to 

7 years shorter than compatible test sections located in the other three climatic regions.

• In the WNF, DF, and DNF climatic regions, RFP of the test sections having 4-inch 

(102 mm)-thick and 7-inch (178-mm)-thick AC layers and drainable and undrainable 

bases was 2 to 7 years longer than compatible test sections located in the WF region.

• In the WNF region, RFP/RSP of the test sections having 4-inch (102 mm)-thick and 7-inch 

(178-mm)-thick AC layers and drainable and undrainable bases was almost the same.

• In the DF region, the average RFP of the test sections having 4-inch (102-mm)-thick AC 

layers and drainable bases was about 2 years shorter than test sections having 4-inch 

(102 mm)-thick and 7-inch (178-mm)-thick AC layers and drainable and undrainable 

bases located in the WNF and DNF climatic regions.

• In the DNF region, test sections having 4-inch (102 mm)-thick and 7-inch 

(178-mm)-thick AC layers and drainable and undrainable bases performed almost the 

same. That is, the existence of drainable bases and thicker AC layers did not affect the 

pavement performance in terms of IRI.

The impact of the climatic regions on pavement performance in terms of IRI is summarized in 

table 34. The data in the table indicate the following:
112

• Fifty-eight percent of the test sections located in the WF regions performed worse, 

4 percent performed the same, and 38 percent performed better than compatible test 

sections located in the WNF regions.

• Fifty-six percent of the test sections located in the WF regions performed worse, 

28 percent performed the same, and 17 percent performed better than compatible test 

sections located in the DF regions.

• Seventy-seven percent of the test sections located in the DF region performed better than 

compatible sections located in the WNF region while test sections located in the DNF and 

WNF performed almost the same.

There are several significant initial conclusions regarding pavement performance that could be 

drawn from the findings listed in table 29 and table 34. However, these conclusions would be

based on pavement roughness only. To make these conclusions a part of the global pavement 

performance, the research team decided to include them in the summary, conclusions, and 

recommendations section that follows the discussion of all the other distress types. 

Rut Depth

The average calculated RFPs or RSPs listed in table 30 indicate the following:

• In the WF regions, the average RFP/RSP of the SPS-1 test sections having 4-inch 

(102-mm)-thick AC layers and undrainable bases was 5 years shorter than the average 

RSP of compatible test sections with drainable bases. Further, the average RFP/RSP of 

SPS-1 test sections having 7-inch (178-mm)-thick AC layers and undrainable bases was 

3 years shorter than compatible sections with drainable bases.

• All SPS-1 test sections having 4- or 7-inch (102- to 178-mm)-thick AC layers and 

drainable or undrainable bases located in the WNF, DF, and DNF climatic regions 

performed significantly better than compatible test sections located in the WF regions. 

The differences in RFP/RSP varied from 5 to 11 years.

The impact of the climatic regions on pavement performance in terms of rut depth is summarized 

in table 34. The data in the table indicate the following:

• Eighty-three percent, 82 percent, and 73 percent of the SPS-1 test sections located in the 

WF regions performed worse than compatible test sections located, respectively, in the 

WNF, DF, and DNF regions.

• Almost all SPS-1 test sections located in the DNF region performed the same as 

compatible test sections located in the WNF and DF climatic regions.

The conclusions are included in the summary, conclusions, and recommendations section 

following the discussion of the other distress types.
113

Alligator Cracking

The average calculated RSPs listed in table 31 indicate the following, on average:

• In the WF regions, the average RSP of test sections having 4-inch (102-mm)-thick AC 

layers and undrainable bases was 4 years shorter than the average RSP of test sections 

having 4-inch (102-mm)-thick AC layers and drainable bases. Further, RSP of SPS-1 test 

sections having 7-inch (178-mm)-thick AC layers and drainable and undrainable bases 

was almost the same.

• SPS-1 test sections having 4- and 7-inch (102- to 178-mm)-thick AC layers and drainable 

or undrainable bases located in the WNF region had longer RSPs compared with

compatible test sections located in the WF region.

• All SPS-1 test sections located in the DNF regions had shorter RSPs than those located in 

the WNF region. The differences in RSP varied from 1 to 8 years.

The impact of the climatic regions on pavement performance in terms of alligator cracking is 

summarized in table 34. The data in the table indicate the following:

• Sixty-seven percent of the SPS-1 test sections located in the WF regions showed worse, 

8 percent showed the same, and 25 percent showed better performance than compatible 

test sections located in the WNF region.

• Fifty-four percent and 50 percent of the SPS-1 test sections located in the WF regions 

performed better than compatible test section located, respectively in the DF and DNF 

regions. Forty-two percent performed worse than compatible test sections located in the 

DF and DNF regions. Hence, statistically speaking, SPS-1 test sections located in the WF 

region had slightly better performance than those in the DF and DNF regions.

• Forty-six percent of the SPS-1 test sections located in the DNF regions performed better 

than compatible test sections located in the DF region, whereas 38 percent performed worse.

• Finally, 87 percent of the SPS-1 test sections located in the DNF region performed worse than 

compatible test sections located in the WNF region, and only 13 percent performed better.

The conclusions are included in the summary, conclusions, and recommendations section 

following the discussion of the other distress types.

Longitudinal Cracking

The average calculated RSPs listed in table 32 indicate the following, on average:

• All SPS-1 test sections having 4-inch (102-mm)-thick and 7-inch (178-mm)-thick AC 

layers and drainable and undrainable bases and located in WF climatic region showed 

almost the same RSP in terms of longitudinal cracking.
114

• RSPs of most SPS-1 test sections having 4-inch (102-mm)-thick and 7-inch (178-mm)-

thick AC layers and drainable and undrainable bases located in the WNF, DF, and DNF 

regions were longer than compatible test sections located in the WF region. The 

differences in RSP varied from 1 to 8 years.

• RSPs of almost all SPS-1 test sections having 4-inch (102-mm)-thick and 7-inch 

(178-mm)-thick AC layers and drainable and undrainable bases located in the DF and 

DNF regions were shorter than the RSP of compatible test sections located in the WNF 

region. The differences in RSPs varied from 1 to 7 years.

• Finally, the RSP of all SPS-1 test sections located in the DNF region was longer than the 

RSP of compatible sections located in the DF region. The differences in RSPs varied

from 1 to 7 years.

The impact of the climatic regions on pavement performance in terms of longitudinal cracking is 

summarized in table 34. The data in the table indicate the following:

• Eighty-eight percent and 92 percent of the SPS-1 test sections located in the WF regions 

performed worse than compatible test sections located, respectively, in the WNF and 

DNF regions.

• Statistically speaking, the performance of SPS-1 test sections located in the WF region 

was almost the same as those located in the DF region. The data indicate that 50 percent 

performed better, and 46 percent performed worse.

• Forty-two percent of the SPS-1 test sections located in the DNF regions performed better 

than compatible test sections located in the WNF region, 42 percent performed the same,

and only 16 percent performed worse.

• Sixty-seven percent of the SPS-1 test sections located in the DNF regions performed 

better than compatible test sections located in the DF regions while 29 percent performed 

the same.

Once again, the conclusions are included in the summary and conclusions section following the 

discussion of the other distress types.

Transverse Cracking 

The average calculated RSPs listed in table 33 indicate the following:

• In the WF region, the average RSPs of test sections having 4- and 7-inch (102- and

178-mm)-thick AC layers and undrainable bases were almost the same as compatible 

sections with drainable bases. Further, the average RSP for SPS-1 test sections having 

7-inch (178-mm)-thick AC layers and drainable bases was 2 years longer than test 

sections having 4-inch (102-mm)-thick AC layers and drainable bases.
115

• All SPS-1 test sections located in the WNF regions had 1- to 4-year longer RSPs than 

compatible sections located in the WF region.

• The majority of the SPS-1 test sections located in the DF and DNF regions had shorter 

RSPs than compatible sections located in the WF and WNF regions.

• The majority of the SPS-1 test sections having 4- and 7-inch (102- and 178-mm)-thick 

AC layers and drainable and undrainable bases and located in the DNF region had longer 

RSPs than compatible test sections located in the DF region.

The impact of the climatic regions on pavement performance in terms of transverse cracking is 

summarized in table 34. The data in the table indicate the following:

• Fifty-four percent of the SPS-1 test sections located in the WF regions performed worse, 

13 percent performed better, and 33 percent performed the same as compatible test 

sections located in the WNF region.

• Statistically speaking, the performance of SPS-1 test sections located in the WF and DF 

and DNF regions was the same.

• Forty-two percent of the SPS-1 test sections located in the WNF regions performed 

better, 50 percent performed the same, and 7 percent performed worse than compatible 

test sections located in the DNF region.

• Forty-two percent of the SPS test sections located in the DNF regions performed better, 

46 percent performed the same, and 12 percent performed worse than compatible test 

sections located in the DF region.

Summary, Conclusions, and Recommendations for LTPP SPS-1

The performance of each SPS-1 test section was analyzed using the available time-series IRI; rut 

depth; alligator, longitudinal, and transverse cracking data; and the proper mathematical 

functions. The results of the analyses were then expressed in terms of RFP for IRI, RFP/RSP for 

rut depth, and RSP for each cracking type. The test sections and their performance (RFP and 

RSP) were then tabulated using the SHRP IDs, climatic regions, AC thicknesses, and drainable 

or undrainable bases. Based on the results, the following conclusions were drawn:

• The climate in WF regions had a significant impact on pavement performance in terms of 

IRI, rut depth, and cracking. This conclusion was expected because of the repeated 

volume changes caused by freezing and thawing. These effects were reported previously

by many researchers. Results of the analyses suggest that base drainage and AC thickness 

should be carefully examined in the pavement design in the WF region.

• Use of drainable bases decreased the impact on pavement performance in WF regions. 

This conclusion was expected, and it was reported in the 1993 AASHTO Guide for 

Design of Pavement Structures.

(36)
116

• Increasing the thickness of the AC layer from 4 to 7 inches (102 to 178 mm) increased

the frost protection of the lower layers, and hence it decreased the impact of the WF 

region. However, this option was not a cost-effective one.

• In the WF region, the inclusion of drainable base had slightly more favorable impact on 

pavement performance than increasing the AC thickness from 4 to 7 inches (102 to 

178 mm).

• The other three climatic regions (WNF, DF, and DNF) did not affect the pavement 

performance in terms of rutting potential.

• The DF region had more adverse effects on cracking potential than those in the DNF 

region. This could be attributed to higher oxidation (aging) potential of the AC layer in 

the DF region.

• In the DNF regions, there were significantly higher adverse effects on cracking potential 

than in the WNF region. This could be attributed to higher solar radiation in the DNF 

region, which oxidizes the asphalt binder and makes AC more susceptible to cracking.

• The inclusion of drainable bases in the DF and DNF regions did not affect pavement 

performance in terms of RFP or RSP. This was expected because the volume and 

frequency of available water were low, and most rainfall occurred over short periods of 

time with most water running off the surface and not penetrating the pavement layers.

• In the WNF and DNF regions, the pavement performance in terms of IRI and rut depths 

of most SPS-1 test sections having 4- and 7-inch (102- and 178-mm)-thick AC layers and 

drainable and undrainable bases was very much similar. This scenario was substantially 

different for cracking potential as stated in previous conclusions. Similarly, in the DNF 

and DF regions, the pavement performance in terms of IRI and rut depths of most SPS-1 

test sections having 4- and 7-inch (102- and 178-mm)-thick AC layers and drainable and 

undrainable bases was similar, Once again, this scenario was substantially different for

cracking potential.

IMPACTS OF MAINTENANCE TREATMENTS ON PAVEMENT PERFORMANCE 

USING THE LTPP SPS-3 TEST SECTIONS

The main objective of SPS-3 experiment was to compare the performance of different 

maintenance treatments on flexible pavements compared with the control (untreated) test 

sections. The 81 SPS-3 test sites were initiated between 1990 and 1991 and were distributed 

across the United States and Canada. Each of the SPS-3 test sites consisted of 4 test sections for 

a total of 324 test sections. Fifty-one of the 81 test sites had control sections labeled 340. Each of 

the other 30 sites were linked to a GPS test section (listed in table 35 along with their SHRP ID), 

which could be used as control sections.

(60)
117

Table 35. Linked GPS sections that serve as control sections.(60)

Site ID

Linked GPS 

Section ID

04_A300 4_1036

04_B300 4_1021

04_D300 4_1016

05_A300 5_3071

08_B300 8_2008

12_A300 12_9054

12_B300 12_3997

12_C300 12_4154

16_A300 16_1020

16_B300 16_1021

16_C300 16_1010

28_A300 28_1802

30_A300 30_1001

32_A300 32_1021

32_C300 32_2027

40_B300 40_1015

40_C300 40_4088

47_A300 47_3101

47_B300 47_3075

47_C300 47_1023

48_D300 48_2172

48_G300 48_1169

49_A300 49_1004

49_B300 49_1017

49_C300 49_1006

53_A300 53_1008

53_B300 53_1501

53_C300 53_1801

56_A300 56_1007

56_B300 56_7775

Each of the four SPS-3 test sections in each test site was subjected to one of the following 

treatments (note that the numbers in parentheses are the LTPP designation of the treatment; for 

example, the designation of the joint and crack sealing is 410):

• Thin overlay (310).

• Slurry seal (320).

• Crack seal (330).

• Aggregate seal coat; chip seal (350).

Several variables affect the performance of the treated pavement sections. These include climatic 

region, traffic, subgrade type, and the before treatment pavement condition and distress. 
118

Unfortunately, these variables could be separated to analyze the effects of each on pavement 

performance. The reason is that separating the variables yields statistically insignificant numbers

of test sections to be used in the analyses. 

To illustrate, table 36 lists the number of test sections that were available for analyses based on 

separation of the following variables:

• Four treatment types.

• One pavement condition (IRI).

• Four pavement distress types.

• Four climatic regions (WF, WNF, DF, and DNF).

• Three traffic levels.
119

Table 36. Number of test sections that have before treatment and after treatment pavement condition, distress, and traffic 

data.

Condition or 

Distress Type Treatment Type

Number of Test Sections by Climatic Region and Traffic Level

WF WNF DF DNF

L M H L M H L M H L M H

IRI

Thin overlay 8 4 4 8 2 6 3 4 3 1 0 1

Slurry seal 6 4 4 6 3 6 3 3 2 1 0 1

Crack seal 7 4 4 2 1 6 3 3 2 1 0 1

Aggregate seal coat 5 4 4 7 2 5 3 3 3 0 1 1

Rut depth

Thin overlay 4 2 2 4 2 7 2 1 2 0 0 1

Slurry seal 4 1 2 4 2 8 2 2 2 1 0 1

Crack seal 4 1 3 2 0 6 3 2 2 0 0 1

Aggregate seal coat 1 2 1 5 1 9 2 2 1 0 0 1

Alligator 

cracking

Thin overlay 4 2 4 4 2 5 1 0 0 0 0 0

Slurry seal 1 0 3 5 0 5 0 0 0 0 0 0

Crack seal 1 0 1 3 2 2 0 0 0 0 0 0

Aggregate seal coat 2 0 3 4 0 3 0 0 0 0 0 0

Longitudinal 

cracking

Thin overlay 4 2 4 4 2 5 1 0 0 0 0 0

Slurry seal 2 0 3 5 0 5 0 0 0 0 0 0

Crack seal 1 0 3 2 3 3 0 0 0 0 0 0

Aggregate seal coat 3 0 3 4 0 4 0 0 0 0 0 0

Transverse 

cracking

Thin overlay 4 2 4 4 2 5 1 0 0 0 0 0

Slurry seal 1 0 3 5 0 4 0 0 0 0 0 0

Crack seal 2 0 3 3 2 3 0 0 0 0 0 0

Aggregate seal coat 2 0 3 4 0 3 0 0 0 0 0 0

Note: For each pavement condition and distress type, the test section was analyzed if the database contained at least one data point before treatment and/or three or 

more data points after treatment that could be modeled.

L = low traffic (0 to 60,000 yearly ESAL).

M = medium traffic (61,000 to 120,000 yearly ESAL).

H = high traffic (> 120,000 yearly ESAL).
120

It can be seen from the table that in some cells, especially in the DF and DNF regions and for some 

pavement distress types, the number of available test sections for analyses was not significant 

(ranges from 0 to 2). Therefore, the analyses were conducted to assess the impact of each treatment 

type in each climatic region and for each pavement condition and distress type. That is, the data 

were not separated based on traffic level, type of base and subbase, or type of roadbed.

Nevertheless, the analyses of the impacts of each of the four treatment types on pavement 

performance were accomplished using the following steps:

• Step 1: For each treated pavement test section in the SPS-3 experiment, the available 

pavement condition (IRI) and distress data in the LTPP database were used to calculate 

RFP and RSP of that section after treatment.

• Step 2: For each SPS-3 test section, each pavement condition and distress type, and for 

each pavement treatment type, the minimum and maximum RFPs and RSPs were 

calculated and tabulated. Further, the averages of RFP and RSP of all test sections located 

in the same climatic region were also calculated and tabulated.

• Step 3: The time-dependent pavement condition and distress data of each control section 

and/or linked GPS section in the SPS-3 experiment were used to calculate RFP and RSP 

of that section after the assignment date.

Results of the analyses are discussed per pavement condition and distress type in the next 

five subsections.

IRI

Listed in table 37 are the calculated minimum, maximum, and average RFPs based on IRI data 

for the SPS-3 test sections that were subjected to the same treatment type and located in the same 

climatic region. The table also includes the same data for the associated control sections. To 

assist the reader in interpreting the data, the numbers listed in the first row of the table, for 

example, indicate the following:

• There were 19 SPS-3 test sections in the WF region that were subjected to thin overlay 

and accepted for analyses. The minimum, maximum, and average RFP of the 19 SPS-3 

test sections were 4, 20, and 16 years, respectively.

• There were 21 control sections in the WF region with a minimum RFP of 0 years, a 

maximum RFP of 19 years, and an average RFP of 11 years. 

• The difference in the average RFP of the test sections and the average RFP of the control 

sections was 5 years. That is, on average, RFP of the treated sections was 5 years longer 

than the control sections. 
121

Table 37. Impacts of various maintenance treatments and control section on pavement performance in terms of RFP based 

on IRI.

Climatic 

Region

Treatment 

Type

Remaining Functional Period (Years)

Difference 

in RFP 

(Years)

Test Sections Control Sections

Number of 

Sections Min Max Average

Number of 

Sections Min Max Average

WF Thin 

overlay

19 4 20 16 21 0 19 11 5

WNF 23 8 20 18 29 3 19 14 4

DF 13 5 20 17 13 2 19 12 5

DNF 3 3 13 9 4 3 18 11 –2

WF Slurry seal 15 0 20 12 21 0 19 11 1

WNF 22 4 20 19 29 3 19 15 4

DF 13 4 20 14 13 2 19 13 1

DNF 2 9 20 15 4 3 18 11 4

WF Crack seal 18 0 20 11 21 0 19 11 0

WNF 12 1 20 16 29 3 19 14 2

DF 13 3 20 15 13 2 18 12 3

DNF 4 6 20 14 4 3 18 11 3

WF Aggregate 

seal coat

16 0 20 13 21 0 19 11 2

WNF 21 14 20 19 29 3 19 15 4

DF 13 1 20 14 13 2 19 13 1

DNF 3 4 10 7 4 3 18 11 4

Max = Maximum.

Min = Minimum.
122

Examination of the results of the analyses listed in table 37 indicates the following:

• Test sections that were subjected to thin overlay treatment performed better than the 

control sections by 5, 4, and 5 years in the WF, WNF, and DF regions, respectively, while 

they performed worse in the DNF region by 2 years. The reason for the latter was that the 

construction of the overlay caused increases in the IRI of all test sections in the DNF 

region. For example, the IRI of test section 04B310 increased from 88.564 inches/mi

(1.3978 m/km) before the overlay to 95.901 inches/mi (1.5136 m/km) after the overlay.

• Test sections that were subjected to slurry seal performed better than the control sections 

by 1, 4, 1, and 4 years in the WF, WNF, DF, and DNF regions, respectively.

• Test sections that were subjected to crack seal performed better than the control sections 

by 2, 3, and 3 years in the WNF, DF, and DNF regions, respectively. Further, crack 

sealing had no impact on pavement performance in the WF region.

• Test sections that were subjected to aggregate seal coat performed better than the control 

sections by 2, 4, and 1 year in the WF, WNF, and DF regions, respectively, while they 

performed worse in the DNF regions by 4 years. Once again, the reason for the decreasing 

performance in the DNF regions was that construction of the aggregate seal coat increased 

the IRI of two of the three test sections.

For some of the SPS-3 test sections, the LTPP database contained one or more IRI data points 

before the sections were subjected to maintenance treatments. To assess the impact of the before 

treatment pavement conditions on the after treatment pavement performance, for each maintenance 

treatment type, RFPs after treatment were plotted against the last collected IRI data point before 

treatment. The results are shown in figure 52 through figure 55 for thin overlay, slurry seal, crack 

seal, and aggregate seal coat, respectively. Although the data in the figures are widely scattered, 

the general trend is that the higher the IRI is before treatment, the lower the RFP is after treatment. 

This finding was expected and supports the notion that maintaining pavement sections in good 

conditions pays higher dividends than treating deteriorated sections. Nevertheless, the scattering of 

the data in figure 52 through figure 55 was likely caused by differences in the original pavement 

cross sections, pavement materials, roadbed soil, climatic region, and traffic level. Unfortunately, 

the number of test sections subjected to the same traffic level bracket was so small that no decision 

regarding the impacts of traffic could be made with any level of certainty. Note the solid best fit 

curves in figure 52 through figure 55 are not intended to model the data. They show only the 

global trend, and therefore the inclusion of statistics such as standard error would be meaningless. 

As stated previously, the data in the figures are a function of many other variables that were not 

included in the analyses. Because separation of variables yielded data for few test sections (two or 

fewer), no decision could be made with any degree of certainty.
123

1 inch/mi = 0.0158 m/km.

Figure 52. Graph. After-treatment RFP versus before-treatment IRI of LTPP SPS-3 test 

sections subjected to thin overlay.

1 inch/mi = 0.0158 m/km.

Figure 53. Graph. After-treatment RFP versus before-treatment IRI of LTPP SPS-3 test 

sections subjected to slurry seal.
124

1 inch/mi = 0.0158 m/km.

Figure 54. Graph. After-treatment RFP versus before-treatment IRI of LTPP SPS-3 test 

sections subjected to crack seal.

1 inch/mi = 0.0158 m/km.

Figure 55. Graph. After-treatment RFP versus before-treatment IRI of LTPP SPS-3 test 

sections subjected to aggregate seal coat.
125

Rut Depth

Listed in table 38 are the calculated minimum, maximum, and average RFPs/RSPs based on rut 

depth data for the SPS-3 test sections that were subjected to the same treatment type and located 

in the same climatic region. The table also includes the same data for the associated control 

sections. The following summarizes the results:

• Test sections subjected to thin overlay performed better than the control sections by 7, 8, 

and 7 years in the WF, WNF, DF, respectively, and worse by 1 year in the DNF region.

• Test sections subjected to slurry seal performed better than the control sections by 2, 5, 4, 

and 3 years in the WF, WNF, DF, and DNF regions, respectively.

• Test sections subjected to crack seal performed better than the control sections by 1, 5, 7, 

and 2 years in the WF, WNF, DF, and DNF regions, respectively.

• Test sections subjected to aggregate seal coat performed better than the control sections 

by 3, 3, 5, and 9 years in the WF, WNF, DF, and DNF regions, respectively.

Similar to the IRI analyses, RSPs after treatment were plotted against the last measured rut depth 

data point before treatment. The results were submitted to FHWA and are available from the 

LTPP Customer Support Services.(79) The data indicate that deeper before treatment rut depths 

led to lower after treatment RFP/RSP or better performance in terms of rut depth after treatment. 

The scattering of data in the figures is mainly due to differences in the original pavement cross 

sections, pavement materials, roadbed soil, climatic region, and traffic level.
126

Table 38. Impacts of various maintenance treatments and control section on pavement performance in terms of RFP/RSP 

based on rut depth.

Climatic 

Region

Treatment 

Type

Remaining Functional/Structural Period (Years)

Difference 

in 

RFP/RSP 

(Years)

Test Sections Control Sections

Number of 

Sections Min Max Average

Number of 

Sections Min Max Average

WF Thin 

overlay

18 13 20 19 14 0 18 12 7

WNF 21 5 20 19 16 0 19 11 8

DF 8 9 20 19 8 0 19 12 7

DNF 4 0 20 10 3 0 16 11 –1

WF Slurry seal 12 1 20 14 14 0 18 12 2

WNF 19 0 20 16 16 0 19 11 5

DF 10 1 20 16 8 0 19 12 4

DNF 3 1 20 14 3 0 16 11 3

WF Crack seal 11 0 20 13 14 0 18 12 1

WNF 9 0 20 16 16 0 19 11 5

DF 9 2 20 18 8 0 18 11 7

DNF 3 0 20 13 3 0 16 11 2

WF Aggregate 

seal coat

11 0 20 15 14 0 18 12 3

WNF 22 0 20 14 16 0 19 11 3

DF 8 0 20 17 8 0 19 12 5

DNF 2 0 20 20 3 0 16 11 9

Max = Maximum.

Min = Minimum.
127

Alligator Cracking

Listed in table 39 are the calculated minimum, maximum, and average RSPs based on alligator 

cracking for the SPS-3 test sections and located in the same climatic region. The table also 

includes the same data for the associated control sections. The results listed in the table indicate 

the following:

• Test sections subjected to thin overlay performed better than the control sections by 

2 years in the WF and WNF regions, while they performed worse by 1 and 9 years in the 

DF and DNF regions, respectively.

• Test sections subjected to slurry seal performed worse than the control sections by 1, 3, 

and 7 years in the WF, DF, and DNF regions, respectively, while they performed the 

same in the WNF region.

• Test sections subjected to crack seal performed better than the control sections by 1 year 

in the DF region, while they performed worse by 2, 1, and 15 years in the WF, WNF, and 

DNF regions.

• Test sections subjected to aggregate seal coat performed better than the control sections 

by 2, 2, and 1 years in the WF, WNF, and DNF regions, respectively, while they 

performed worse by 1 year in the DF region.

Similar to the IRI and rut depths, for each treatment type, the RSPs of the test sections after 

treatment were plotted against the last collected alligator cracking data points before treatment. 

The results were submitted to FHWA and are available from the LTPP Customer Support 

Services.

(79)

In summary, the data indicate that as the alligator cracking increased, the after 

treatment RSPs decreased. That is, the data indicate that, on average, treating pavement sections 

at an early stage paid higher dividends than delayed treatment.
128

Table 39. Impacts of various maintenance treatments and control section on pavement performance in terms of RSP based on 

alligator cracking.

Climatic 

Region

Treatment 

Type

Remaining Structural Period (Years)

Difference 

in RSP 

(Years)

Test Sections Control Sections

Number of 

Sections Min Max Average

Number of 

Sections Min Max Average

WF Thin 

overlay

21 2 20 10 15 0 16 8 2

WNF 24 3 20 11 20 0 17 9 2

DF 11 4 20 11 8 6 18 12 –1

DNF 3 0 15 7 1 16 16 16 –9

WF Slurry seal 16 0 20 7 15 0 16 8 –1

WNF 30 2 20 10 20 0 17 10 0

DF 10 3 20 9 8 6 18 12 –3

DNF 2 0 18 9 1 16 16 16 –7

WF Crack seal 10 0 20 6 15 0 16 8 –2

WNF 22 0 20 8 20 0 17 9 –1

DF 11 0 20 13 8 5 18 12 1

DNF 2 0 2 1 1 16 16 16 –15

WF Aggregate 

seal coat

15 2 20 10 15 0 16 8 2

WNF 18 4 20 12 20 0 17 10 2

DF 9 6 20 11 8 6 18 12 –1

DNF 2 13 20 17 1 16 16 16 1

Max = Maximum.

Min = Minimum.
129

Longitudinal Cracking

Listed in table 40 are the calculated minimum, maximum, and average RSPs based on 

longitudinal cracking for the SPS-3 test sections that were subjected to the same treatment type 

and located in the same climatic region. The table also includes the same data for the associated 

control sections. The results listed in the table indicate the following: 

• Test sections subjected to thin overlay performed better than the control sections by 1 and 

2 years in the DF and DNF regions, respectively, while they performed worse than the 

control sections by 3 years in the WF region. They performed the same in the WNF region.

• Test sections subjected to slurry seal performed better than the control sections by 2 and 

3 years in the DF and DNF regions, while they performed worse than the control sections 

by 2 years in the WF region. They performed the same in the WNF region.

• Test sections subjected to crack seal performed better than the control sections by 1 year 

in the WNF region, while they performed worse by 5, 1, and 11 years in the WF, DF, and 

DNF regions, respectively.

• Test sections subjected to aggregate seal coat performed better than the control sections 

by 2, 4, and 7 years in the WNF, DF, and DNF regions, respectively, while they 

performed worse by 2 years in the WF region.

Once again, for each treatment type, the RSPs of the test sections after treatment were plotted 

against the last measured longitudinal cracking data point before treatment were submitted to 

FHWA and are available from the LTPP Customer Support Services.(79)It can be seen from the 

figures that, on average, the higher the longitudinal cracking length was before treatments, the 

lower the RSP was after treatments.
130

Table 40. Impacts of various maintenance treatments and control section on pavement performance in terms of RSP based on 

longitudinal cracking.

Climatic 

Region

Treatment 

Type

Remaining Structural Period (Years)

Difference 

in RSP 

(Year)

Test Sections Control Sections

Number of 

Sections Min Max Average

Number of 

Sections Min Max Average

WF Thin 

overlay

23 2 20 9 12 4 19 12 –3

WNF 26 5 20 12 20 0 18 12 0

DF 12 2 20 15 6 5 18 14 1

DNF 2 15 15 15 2 10 16 13 2

WF Slurry seal 13 3 20 10 12 4 19 12 –2

WNF 30 4 20 13 20 0 18 13 0

DF 10 8 20 16 6 5 18 14 2

DNF 1 16 16 16 2 10 16 13 3

WF Crack seal 12 0 20 7 12 4 19 12 –5

WNF 14 1 20 13 20 0 18 12 1

DF 7 1 20 13 6 5 18 14 –1

DNF 1 2 2 2 2 10 16 13 –11

WF Aggregate 

seal coat

21 3 20 10 12 4 19 12 –2

WNF 19 5 20 14 20 0 18 12 2

DF 9 8 20 18 6 5 18 14 4

DNF 1 20 20 20 2 10 16 13 7

Max = Maximum.

Min = Minimum.
131

Transverse Cracking

Listed in table 41 are the calculated minimum, maximum, and average RSPs based on transverse 

cracking for the SPS-3 test sections that were subjected to the same treatment type and located in 

the same climatic region. The table also includes the same data for the associated control 

sections. The data in the table indicate the following: 

• Test sections subjected to thin overlay performed better than the control sections by 1, 2, 

and 12 years in the WF, DF, and DNF regions, respectively, while they performed the 

same as the control sections in the WNF region.

• Test sections subjected to slurry seal performed better than the control sections by 2 and 

5 years in the DF and DNF regions, while they performed the same as the control 

sections in the WF and WNF regions.

• Test sections subjected to crack seal performed better than the control sections in the DF 

region by 1 year, while they performed the same in the WF and WNF regions, respectively. 

Note that insufficient data were available to make a comparison in the DNF region.

• Test sections subjected to aggregate seal coat performed better than the control sections 

by 2, 1, 1, and 5 years in the WF, WNF, DF, and DNF regions, respectively.

For each treatment type, the RSPs of the test sections after treatment were plotted against the last 

measured transverse cracking data point before treatment were submitted to FHWA and are 

available from the LTPP Customer Support Services.(79)It can be seen from the figures that the 

lower the cumulative transfer cracks value was, the higher the RSP was after treatment.
132

Table 41. Impacts of various maintenance treatments and control section on pavement performance in terms of RSP based on 

transverse cracking.

Climatic 

Regions

Treatment 

Type

Remaining Structural Period (Years)

Difference 

in RSP 

(Year)

Test Sections Control Sections

Number of 

Sections

Min Max Average

Number of 

Sections Min Max Average

WF Thin 

overlay

22 1 20 9 18 0 16 8 1

WNF 24 5 20 12 20 3 17 12 0

DF 12 2 20 13 8 0 18 11 2

DNF 2 12 20 16 1 4 4 4 12

WF Slurry seal 14 0 20 8 18 0 16 8 0

WNF 30 3 20 12 20 3 17 12 0

DF 7 2 20 13 8 0 18 11 2

DNF 1 9 9 9 1 4 4 4 5

WF Crack seal 14 0 20 8 18 0 16 8 0

WNF 18 0 20 12 20 3 16 12 0

DF 9 0 20 12 8 0 18 11 1

DNF 0 0 0 — 1 4 4 4 NC

WF Aggregate 

seal coat

17 0 20 10 18 0 16 8 2

WNF 16 3 20 13 20 3 16 12 1

DF 8 1 20 12 8 0 18 11 1

DNF 1 9 9 9 1 4 4 4 5

— Indicates no data.

NC = Could not be compared.
133

Summary, Conclusions, and Recommendations for LTPP SPS-3

Table 42 summarizes the impacts of the four SPS-3 maintenance treatments on the pavement 

performance. 

Table 42. Summary of the impact of treatment type on pavement performance compared 

with the control sections (years).

Treatment 

Type

Condition or Distress 

Type

Climatic Region

WF WNF DF DNF

Thin 

overlay

IRI 5 4 5 –2

Rut depth 7 8 7 –1

Alligator cracking 2 2 –1 –9

Longitudinal cracking –3 0 1 2

Transverse cracking 1 0 2 12

Slurry seal

IRI 1 4 1 4

Rut depth 2 5 4 3

Alligator cracking –1 0 –3 –7

Longitudinal cracking –2 0 2 3

Transverse cracking 0 0 2 5

Crack seal

IRI 0 2 3 3

Rut depth 1 5 7 2

Alligator cracking –2 –1 1 –15

Longitudinal cracking –5 1 –1 –11

Transverse cracking 0 0 1 NC

Aggregate 

seal coat

IRI 2 4 1 –4

Rut depth 3 3 5 9

Alligator cracking 2 2 –1 1

Longitudinal cracking –2 2 4 7

Transverse cracking 2 1 1 5

NC = Could not be compared.

The number in each cell of the table expresses the average increase in RFP or RSP of the test 

sections compared with the control sections. It should be noted that the sections in the DNF region 

were too few in number to draw any reliable conclusions. Also, in many instances, the control 

sections were not truly representative of the test sections that had undergone treatments in terms of 

pavement condition and distress. Nevertheless, the data in the table indicate the following:

• The thin overlay treatment improved the pavement performance in terms of IRI and rut 

depth in all climatic regions except the DNF region. This could be related to construction 

issues and, perhaps, the relatively high solar radiation (accelerated oxidation/aging) in the 

DNF region.

• In general, the thin overlay treatment did not improve the pavement performance in terms of 

alligator, longitudinal, and transverse cracking. This was mainly because of the high rate of 

reflective cracking. Immediately after treatment, all cracks were hidden by the thin overlay.
134

• One or few years later, most cracks were reflected through the overlay, which implies a 

relatively high rate of deterioration and hence a short RSP. The exception is the DNF 

region, where the 12-year increase in the average RSP of the two test sections compared 

with the one control section was mainly attributable to the limited number of sections. 

That is, the conclusion was not reliable because of the limited number of test sections and 

control sections.

• The slurry seal treatment improved the pavement performance in terms of IRI and rut 

depth but did not have much impact on alligator, longitudinal, and transverse cracking. 

The increase in RSP based on transverse cracking in the DNF region was likely 

attributable to the limited number of sections (one test section and one control section).

• Crack sealing appeared to improve the pavement performance in terms of rutting. This 

was expected because crack sealing decreases water infiltration, which increased the 

stiffness of the lower pavement layers. The pavement performance in terms of IRI was 

improved in the WNF, DF, and DNF regions. However, it did not improve the pavement 

performance in terms of cracking.

• The aggregate seal coat appeared to improve the pavement performance in all climatic 

regions in terms of IRI, rut depth, and cracking. This improvement varied from about 1 to 

5 years. In terms of IRI, the decrease in RFP of 4 years in the DNF region was highly 

likely owing to three reasons: construction quality, the good ride quality of one of the 

three control sections, and limited number of sections.

In general, the worse the pavement conditions were before treatment, the shorter the benefits of 

treatment were in terms of RFP or RSP.

IMPACT OF REHABILITATION TREATMENTS ON PAVEMENT PERFORMANCE 

USING LTPP SPS-5 TEST SECTIONS

Once again, one of the objectives of this study was to analyze the benefits of the various 

rehabilitation treatments applied to the SPS-5 test sections. Unfortunately, for some test sections, 

the LTPP database did not have enough time-series pavement condition and distress data to 

conduct the analyses. In one scenario, some of the test sections were subjected to a second 

treatment and only one or two data points were available. In another scenario, the measured IRI, 

rut depth, and/or cracking data showed improvement in the pavement condition and/or distresses 

over time without treatment. After an exhaustive search of the database, it was found that the 

database had an adequate number of time-series pavement condition and distress data for the 

evaluation of the benefits of the following rehabilitation treatments:

• Thin (2-inch (51-mm)) and thick (4-inch (102-mm)) AC overlay using recycled 

asphalt mixes.

• Thin (2-inch (51-mm)) and thick (4-inch (102-mm)) AC overlay using virgin 

asphalt mixes.
135

• Thin (2-inch (51-mm)) and thick (4-inch (102-mm)) mill and fill using recycled 

asphalt mixes.

• Thin (2-inch (51-mm)) and thick (4-inch (102-mm)) mill and fill using virgin 

asphalt mixes.

After identifying the types of treatments that could be analyzed, the time-dependent pavement 

condition and distress data were then organized per treatment type, climatic region, and per 

pavement condition and distress type. The data were then analyzed, RFPs and RSPs of each 

treated test section accepted for analyses, and the corresponding control and/or linked sections 

were calculated. For each pavement condition (IRI) and distress type (rut depth and alligator, 

longitudinal, and transverse cracking), RFP/RSP of the treatment and the treatment benefits are 

listed in table 43 through table 52. The benefits are listed per climatic region and pavement 

condition and distress type and are summarized in table 53. The summary of the treatment

benefits listed in table 53 is divided based on the pavement condition and distress type and on the 

treatment type. However, the discussion that follows that table is based on the benefits for the 

pavement condition and distress type.
136

Table 43. Impacts of various treatments and control section on pavement performance in terms of RFP based on IRI for virgin 

AC mixes (years).

Climatic Region

State

(Code)

Control

Section

RFP (Years)

Virgin AC Mix

Overlay Mill and Fill

Thin Thick Thin Thick

RFP B1 B2 RFP B1 B2 RFP B1 B2 RFP B1 B2

WF

Maine (23) 17 20 3 20 20 3 20 20 3 20 20 3 20

Minnesota (27) 5 ND — ND ND — ND ND — ND ND — ND

New Jersey (34) 10 20 10 20 20 10 20 20 10 20 20 10 20

Alberta (81) 16 ND — ND 20 4 20 20 4 20 20 4 20

Manitoba (83) ND 20 — 18 20 — 19 NS — NS 20 — 20

WNF

Alabama (1) 15 20 5 18 20 5 20 20 5 20 20 5 20

Florida (12) 12 20 8 20 20 8 20 20 8 20 20 8 20

Georgia (13) NCS 20 — 20 20 — 15 20 — 18 20 — 12

Maryland (24) 18 20 2 11 15 –3 12 20 2 20 20 2 20

Mississippi (28) 13 20 7 6 20 7 20 20 7 12 20 7 20

Missouri (29) ND NS — NS NS — NS NS — NS NS — NS

Oklahoma (40) ND ND — ND ND — ND ND — ND ND — ND

Texas (48) ND 20 — 10 NS — NS NS — NS NS — NS

DF Colorado (8) ND NS — NS 20 — 20 17 — ND 20 — ND

Montana (30) 4 16 12 6 20 16 20 20 16 20 20 16 N/A

DNF

Arizona (4) 17 20 3 20 20 3 20 20 3 20 20 3 20

California (6) 0 10 10 6 20 20 10 20 20 15 20 20 20

New Mexico (35) ND 20 — ND 20 — ND 20 — ND 20 — ND

— Indicates could not be estimated.

Thin = 2 inches (51 mm).

Thick = 4 inches (102 mm).

B1= Change in functional period (CFP).

B2 = Functional condition reoccurrence period.

ND = No data.

NCS = No control section.

NS = Model has a negative slope.

N/A = Not applicable.
137

Table 44. Impacts of various treatments and control section on pavement performance in terms of RFP based on IRI for 

recycled AC mixes (years).

Climatic Region

State

(Code)

Control

Section

RFP (Years)

Recycled AC Mix

Overlay Mill and Fill

Thin Thick Thin Thick

RFP B1 B2 RFP B1 B2 RFP B1 B2 RFP B1 B2

WF

Maine (23) 17 NS — NS NS — NS NS — NS NS — NS

Minnesota (27) 5 ND — ND ND — ND ND — ND ND — ND

New Jersey (34) 10 20 10 20 20 10 20 20 10 20 20 10 20

Alberta (81) 16 20 4 18 20 4 20 20 4 17 20 4 20

Manitoba (83) ND 20 — 20 NS — NS 20 — 20 20 — 20

WNF

Alabama (1) 15 20 5 17 20 5 20 20 5 20 20 5 10

Florida (12) 12 20 8 16 20 8 20 20 8 20 20 8 20

Georgia (13) NCS 20 — 15 20 — 15 20 — 14 20 — 11

Maryland (24) 18 ND — ND 20 2 15 20 2 12 20 2 20

Mississippi (28) 13 20 7 20 20 7 20 20 7 20 20 7 20

Missouri (29) ND NS — NS NS — NS NS — NS NS — NS

Oklahoma (40) ND ND — ND ND — ND ND — ND ND — ND

Texas (48) ND 20 — 15 20 — 20 20 — 20 20 — 16

DF Colorado (8) ND 20 — 20 NS — NS 20 — ND 20 — 20

Montana (30) 4 12 8 6 20 16 20 15 11 3 20 16 20

DNF

Arizona (4) 17 13 –4 8 20 3 20 16 –1 14 20 3 20

California (6) 0 11 11 10 20 20 10 9 9 8 20 20 18

New Mexico (35) ND 20 — ND 20 — ND 20 — ND 20 — ND

— Indicates could not be estimated.

Thin = 2 inches (51 mm).

Thick = 4 inches (102 mm).

B1= CFP.

B2 = Functional condition reoccurrence period.

ND = No data.

NCS = No control section.

NS = Model has a negative slope.
138

Table 45. Impacts of various treatments and control section on pavement performance in terms of RFP/RSP based on rut 

depth for virgin AC mixes (years).

Climatic Region

State

(Code)

Control 

Section 

RFP/RSP 

(Years)

Virgin AC Mix

Overlay Mill and Fill

Thin Thick Thin Thick

RFP/

RSP B1 B2

RFP/

RSP B1 B2

RFP/

RSP B1 B2

RFP/

RSP B1 B2

WF

Maine (23) 0 20 20 20 12 11 15 8 8 13 10 10 11

Minnesota (27) 16 NS — NS NS — NS NS — NS NS — NS

New Jersey (34) NS 20 — 20 20 — 20 NS — NS NS — NS

Alberta (81) ND ND — ND 20 — ND 20 — ND 20 — ND

Manitoba (83) ND 20 — ND 20 — ND 20 — ND 20 — ND

WNF

Alabama (1) NS 20 — ND 20 — ND 20 — ND 20 — ND

Florida (12) 12 20 8 20 20 8 20 20 8 20 20 8 20

Georgia (13) NCS 20 — 20 20 — 20 20 — 20 20 — 20

Maryland (24) ND NS — NS 20 — 3 NS — NS 20 — 20

Mississippi (28) 0 15 15 20 4 4 5 8 8 13 3 3 5

Missouri (29) NS 20 — 20 20 — 13 20 — 20 20 — 20

Oklahoma (40) ND ND — ND ND — ND ND — ND ND — ND

Texas (48) 14 20 6 20 20 6 20 20 6 18 20 6 18

DF Colorado (8) NS 20 — 13 20 — 13 20 — 20 18 — 20

Montana (30) NS 20 — 20 14 — 7 10 — 10 17 — 13

DNF

Arizona (4) NS 20 — ND NS — NS NS — NS NS — NS

California (6) ND 20 — 6 20 — 20 20 — 20 20 — 20

New Mexico (35) ND 20 — ND 20 — ND 20 — ND 20 — ND

— Indicates could not be estimated.

Thin = 2 inches (51 mm).

Thick = 4 inches (102 mm).

B1= Change in functional period.

B2 = Functional condition reoccurrence period.

ND = No data.

NCS = No control section.

NS = Model has a negative slope.
139

Table 46. Impacts of various treatments and control section on pavement performance in terms of RFP/RSP based on rut 

depth for recycled AC mixes (years).

Climatic Region

State

(Code)

Control 

Section 

RFP/RSP 

(Years)

Recycled AC Mix

Overlay Mill and Fill

Thin Thick Thin Thick

RFP/

RSP B1 B2

RFP/

RSP B1 B2

RFP/

RSP B1 B2

RFP/

RSP B1 B2

WF

Maine (23) 0 15 15 16 14 14 15 12 12 18 10 10 15

Minnesota (27) 16 20 4 20 NS — NS 20 4 20 NS — NS

New Jersey (34) NS 20 — 20 NS — NS NS — NS NS — NS

Alberta (81) ND NS — NS 20 — ND 20 — ND NS — NS

Manitoba (83) ND 20 — ND 20 — ND 20 — ND 20 — ND

WNF

Alabama (1) NS 20 — ND 20 — ND 20 — ND 20 — ND

Florida (12) 12 20 8 20 20 8 11 20 8 20 20 8 20

Georgia (13) NCS 20 — 20 20 — 20 20 — 20 20 — 20

Maryland (24) ND 4 — 1 2 — 0 9 — 2 3 — 1

Mississippi (28) 0 9 9 20 6 6 12 9 9 20 4 4 9

Missouri (29) NS 20 — 20 20 — 3 20 — 20 20 — 1

Oklahoma (40) ND ND — ND ND — ND ND — ND ND — ND

Texas (48) 14 20 6 20 20 6 20 20 6 20 20 6 20

DF Colorado (8) NS 19 — 6 20 — 8 18 — 20 13 — 20

Montana (30) NS 16 — 18 20 — 20 12 — 10 20 — 20

DNF

Arizona (4) NS 20 — ND 20 — ND 20 — ND NS — NS

California (6) ND 20 — 20 20 — 11 20 — 20 20 — 20

New Mexico (35) ND NS — NS 20 — ND 20 — ND 20 — ND

— Indicates could not be estimated.

Thin = 2 inches (51 mm).

Thick = 4 inches (102 mm).

B1= CFP.

B2 = Functional condition reoccurrence period.

ND = No data.

NCS = No control section.

NS = Model has a negative slope.
140

Table 47. Impacts of various treatments and control section on pavement performance in terms of RSP based on alligator 

cracking for virgin AC mixes (years).

Climatic Region

State

(Code)

Control

Section

RSP 

(Years)

Virgin AC Mix

Overlay Mill and Fill

Thin Thick Thin Thick

RSP B1 B2 RSP B1 B2 RSP B1 B2 RSP B1 B2

WF

Maine (23) 10 ND — ND ND — ND ND — ND ND — ND

Minnesota (27) 16 ND — ND ND — ND ND — ND ND — ND

New Jersey (34) ND 20 — 20 20 — 15 20 — 13 18 — 10

Alberta (81) ND ND — ND 13 — ND 11 — ND 14 — ND

Manitoba (83) ND 9 — 0 13 — 0 11 — 0 13 — 0

WNF

Alabama (1) 0 20 20 0 20 20 15 20 20 20 20 20 20

Florida (12) 12 20 8 11 20 8 20 20 8 20 19 8 20

Georgia (13) NCS 20 — 9 ND — ND ND — ND 20 — 10

Maryland (24) ND ND — ND ND — ND 20 — 20 20 — 20

Mississippi (28) 16 12 –4 7 10 –6 9 10 –6 5 9 –7 9

Missouri (29) NS ND — ND 8 — 6 9 — 8 10 — 7

Oklahoma (40) ND ND — ND ND — ND ND — ND ND — ND

Texas (48) NS NS — NS NS — NS ND — ND 20 — NS

DF Colorado (8) ND 6 — 4 7 — 3 6 — 4 8 — 1

Montana (30) ND NS — NS ND — ND 20 — 20 ND — ND

DNF

Arizona (4) ND 9 — ND 20 — ND 20 — ND ND — ND

California (6) ND 5 — ND 11 — ND 8 — ND 11 — ND

New Mexico (35) ND 12 — 5 ND — ND 20 — 20 16 — 11

— Indicates could not be estimated.

Thin = 2 inches (51 mm).

Thick = 4 inches (102 mm).

B1= CFP.

B2 = Functional condition reoccurrence period.

ND = No data.

NCS = No control section.

NS = Model has a negative slope. .
141

Table 48. Impacts of various treatments and control section on pavement performance in terms of RSP based on alligator 

cracking for recycled AC mixes (years).

Climatic Region

State

(Code)

Control

Section

RSP (Years)

Recycled AC Mix

Overlay Mill and Fill

Thin Thick Thin Thick

RSP B1 B2 RSP B1 B2 RSP B1 B2 RSP B1 B2

WF

Maine (23) 10 ND — ND ND — ND ND — ND ND — ND

Minnesota (27) 16 ND — ND ND — ND 20 4 — ND — ND

New Jersey (34) ND 11 — 19 15 — 0 12 — 0 17 — 1

Alberta (81) ND 7 — ND 6 — ND 7 — ND 10 — ND

Manitoba (83) ND 7 — 0 7 — 0 10 — 0 12 — 0

WNF

Alabama (1) 0 16 16 4 20 20 20 20 20 10 20 20 20

Florida (12) 12 20 8 16 20 8 20 20 8 20 20 8 20

Georgia (13) — 20 — 20 ND — ND 20 — 16 ND — ND

Maryland (24) ND ND — ND ND — ND ND — ND ND — ND

Mississippi (28) 16 7 –10 0 11 –5 5 6 –10 0 9 –8 4

Missouri (29) NS ND — ND ND — ND 20 — 17 ND — ND

Oklahoma (40) ND ND — ND ND — ND ND — ND ND — ND

Texas (48) NS 20 — NS 20 — NS 20 — NS 20 — NS

DF Colorado (8) ND 6 — 0 6 — 4 20 — 0 7 — 5

Montana (30) ND ND — ND 3 — 7 4 — 5 6 — 6

DNF

Arizona (4) ND 4 — ND 13 — ND 15 — ND 20 — ND

California (6) ND 4 — ND 8 — ND 3 — ND 20 — ND

New Mexico (35) ND 20 — 0 10 — 8 15 — 0 10 — 9

— Indicates could not be estimated.

Thin = 2 inches (51 mm).

Thick = 4 inches (102 mm).

B1= CFP.

B2 = Functional condition reoccurrence period.

ND = No data.

NS = Model has a negative slope.
142

Table 49. Impacts of various treatments and control section on pavement performance in terms of RSP based on longitudinal 

cracking for virgin AC mixes (years).

Climatic 

Region

State

(Code)

Control 

Section 

RSP 

(Years)

Virgin AC Mix

Overlay Mill and Fill

Thin Thick Thin Thick

RSP B1 B2 RSP B1 B2 RSP B1 B2 RSP B1 B2

WF

Maine (23) 8 9 0 8 9 1 8 9 1 8 9 1 9

Minnesota (27) 9 6 –3 2 11 2 6 8 –1 5 8 –1 6

New Jersey (34) ND 20 — NA 15 — NA 18 — NA 12 — NA

Alberta (81) ND ND — ND 20 — ND 20 — ND 17 — ND

Manitoba (83) ND 20 — 0 14 — 0 20 — 0 17 — 0

WNF

Alabama (1) ND 20 — 6 20 — 0 20 — 8 20 — 0

Florida (12) ND 20 — 0 20 — 0 17 — 14 20 — 14

Georgia (13) NCS 11 — 0 14 — 0 13 — 0 14 — 0

Maryland (24) ND ND — ND ND — ND 13 — 0 12 — 3

Mississippi (28) 9 20 11 0 12 2 1 14 5 5 ND — ND

Missouri (29) 4 5 1 5 10 6 10 10 5 10 18 13 19

Oklahoma (40) ND ND — ND ND — ND ND — ND ND — ND

Texas (48) NS 11 — ND 20 — ND 17 — ND 20 — ND

DF Colorado (8) ND 5 — 4 6 — 4 7 — 5 9 — 6

Montana (30) ND NS — NS ND — ND 10 — 9 ND — ND

DNF

Arizona (4) ND 20 — ND 18 — ND 20 — ND 20 — ND

California (6) ND 11 — ND 11 — ND 10 — ND 16 — ND

New Mexico (35) ND 12 — 7 10 — 8 11 — 5 10 — 8

— Indicates could not be estimated.

Thin = 2 inches (51 mm).

Thick = 4 inches (102 mm).

B1= CFP.

B2 = Functional condition reoccurrence period.

ND = No data.

NCS = No control section.

NS = Model has a negative slope.
143

Table 50. Impacts of various treatments and control section on pavement performance in terms of RSP based on longitudinal 

cracking for recycled AC mixes (years).

Climatic Region

State

(Code)

Control

Section

RSP (Years)

Recycled AC Mix

Overlay Mill and Fill

Thin Thick Thin Thick

RSP B1 B2 RSP B1 B2 RSP B1 B2 RSP B1 B2

WF

Maine (23) 8 10 2 10 9 0 8 10 2 10 9 1 9

Minnesota (27) 9 9 0 3 20 11 11 9 1 7 13 5 10

New Jersey (34) ND NS — NS 12 — N/A NS — NS 12 — N/A

Alberta (81) ND 13 — ND 14 — ND 12 — ND 13 — ND

Manitoba (83) ND NS — NS 20 — 0 20 — 0 16 — 0

WNF

Alabama (1) ND 20 — 20 20 — 0 20 — 0 20 — 0

Florida (12) ND 20 — 20 19 — 19 20 — 1 20 — 18

Georgia (13) NCS 11 — 1 14 — 0 13 — 0 14 — 0

Maryland (24) ND 7 — 5 ND — ND 16 — 7 20 — 6

Mississippi (28) 9 20 11 4 20 11 0 9 0 7 14 5 0

Missouri (29) 4 13 9 10 10 5 7 13 9 6 13 8 11

Oklahoma (40) ND ND — ND ND — ND ND — ND ND — ND

Texas (48) NS 10 — ND 12 — ND 12 — ND 12 — ND

DF Colorado (8) ND 7 — 4 6 — 4 7 — 6 6 — 5

Montana (30) ND ND — ND ND — ND ND — ND 19 — 8

DNF

Arizona (4) ND NS — NS 19 — ND 20 — ND 18 — ND

California (6) ND 10 — ND 10 — ND 8 — ND 9 — ND

New Mexico (35) ND 10 — 9 8 — 6 10 — 6 8 — 5

— Indicates could not be estimated.

Thin = 2 inches (51 mm).

Thick = 4 inches (102 mm).

B1= CFP.

B2 = Functional condition reoccurrence period.

ND = No data.

NCS = No control section.

NS = Model has a negative slope.

N/A = Not applicable.
144

Table 51. Impacts of various treatments and control section on pavement performance in terms of RSP based on transverse 

cracking for virgin AC mixes (years).

Climatic Region

State

(Code)

Control

Section

RSP (Years)

Virgin AC Mix

Overlay Mill and Fill

Thin Thick Thin Thick

RSP B1 B2 RSP B1 B2 RSP B1 B2 RSP B1 B2

WF

Maine (23) NS ND — ND 11 — 10 11 — 10 ND — ND

Minnesota (27) 16 2 –15 5 13 –4 15 6 –10 13 13 –3 14

New Jersey (34) ND 18 — 5 20 — 15 20 — 13 20 — 14

Alberta (81) ND ND — ND 20 — ND 17 — ND 11 — ND

Manitoba (83) ND 20 — 0 10 — 2 20 — 0 14 — 0

WNF

Alabama (1) 10 20 10 0 20 10 11 20 10 9 20 10 4

Florida (12) ND 19 — 7 20 — 7 20 — 0 20 — 2

Georgia (13) NCS ND — ND ND — ND ND — ND ND — ND

Maryland (24) ND ND — ND ND — ND 20 — 5 20 — 20

Mississippi (28) 8 12 4 6 11 3 9 14 6 5 13 5 11

Missouri (29) NS 9 — 8 ND — ND 10 — 9 14 — 11

Oklahoma (40) ND ND — ND ND — ND ND — ND ND — ND

Texas (48) NS 12 — ND 20 — ND 20 — ND 20 — ND

DF Colorado (8) ND 10 — 6 10 — 8 15 — 5 NS — NS

Montana (30) ND 20 — 20 20 — 20 20 — 20 20 — 20

DNF

Arizona (4) ND 14 — ND 19 — ND 19 — ND 19 — ND

California (6) ND 9 — ND 12 — ND 9 — ND 13 — ND

New Mexico (35) ND 15 — 10 ND — ND 12 — 11 17 — 16

— Indicates could not be estimated.

Thin = 2 inches (51 mm).

Thick = 4 inches (102 mm).

B1= CFP.

B2 = Functional condition reoccurrence period.

ND = No data.

NCS = No control section.

NS = Model has a negative slope.
145

Table 52. Impacts of various treatments and control section on pavement performance in terms of RSP based on transverse 

cracking for recycled AC mixes (years).

Climatic Region

State

(Code)

Control

Section

RSP (Years)

Recycled AC Mix

Overlay Mill and Fill

Thin Thin Thick

RSP B1 B2 RSP B1 B2 RSP B1 B2 RSP B1 B2

WF

Maine (23) NS ND — ND ND — ND 20 — 9 ND — ND

Minnesota (27) 17 8 –8 5 13 –4 9 10 –7 7 9 –8 8

New Jersey (34) ND 13 — 1 17 — 8 20 — 0 20 — 10

Alberta (81) ND 12 — ND 14 — ND 9 — ND 9 — ND

Manitoba (83) ND 17 — 0 17 — 0 20 — 0 8 — 6

WNF

Alabama (1) 10 19 9 0 20 10 7 20 10 2 20 10 7

Florida (12) ND 20 — 14 ND — ND 20 — 10 20 — 20

Georgia (13) NCS ND — ND ND — ND ND — ND ND — ND

Maryland (24) ND 14 — 10 8 — 7 20 — 20 ND — ND

Mississippi (28) 8 8 0 6 9 1 3 10 2 7 10 2 5

Missouri (29) NS ND — ND ND — ND 20 — 15 ND — ND

Oklahoma (40) ND ND — ND ND — ND ND — ND ND — ND

Texas (48) NS 11 — ND 15 — ND 13 — ND 17 — ND

DF Colorado (8) ND 6 — 4 ND — ND 6 — 4 ND — ND

Montana (30) ND ND — ND 20 — 20 ND — ND NS — NS

DNF

Arizona (4) ND NS — NS 8 — ND 9 — ND 11 — ND

California (6) ND 10 — ND 10 — ND 8 — ND 8 — ND

New Mexico (35) ND 17 — 3 15 — 8 17 — 2 11 — 9

— Indicates could not be estimated.

Thin = 2-inches (51 mm).

Thick = 4 inches (102 mm).

B1= CFP.

B2 = Functional condition reoccurrence period.

ND = No data.

NCS = No control section.

NS = Model has a negative slope.
146

Table 53. Summary of benefits of various rehabilitation treatments (years).

Treatment 

Type

Thickness 

(Inches) Statistic

Condition

Distress

Rut 

Depth

Cracking

IRI Alligator Longitudinal Transverse

B1 B2 B1 B2 B1 B2 B1 B2 B1 B2

Overlay, 

virgin AC 

mix

2

Min 2 6 6 6 –4 0 –3 0 –15 0

Max 12 20 20 20 20 20 11 8 10 20

Average 7 14 12 18 8 7 2 3 0 7

4

Min –3 10 4 3 –6 0 1 0 –4 2

Max 20 20 11 20 20 20 6 10 10 20

Average 7 18 7 14 7 10 3 4 3 11

Overlay, 

recycled 

AC mix

2

Min –4 6 4 1

–

10 0 0 1 –8 0

Max 11 20 15 20 16 20 11 20 9 14

Average 6 15 8 17 5 7 5 9 0 5

4

Min 2 10 6 0 –5 0 0 0 –4 0

Max 20 20 14 20 20 20 11 19 10 20

Average 8 18 9 12 8 8 7 6 2 8

Mill and 

fill, virgin 

AC mix

2

Min 2 12 6 10 –6 0 –1 0 –10 0

Max 20 20 8 20 20 20 5 14 10 20

Average 8 19 8 17 7 13 2 6 2 8

4

Min 2 12 3 5 –7 0 –1 0 –3 0

Max 20 20 10 20 20 20 13 19 10 20

Average 8 19 7 17 7 11 4 6 4 11

Mill and 

fill, 

recycled 

AC mix

2

Min –1 3 4 2

–

10 0 0 0 –7 0

Max 11 20 12 20 20 20 9 10 10 20

Average 6 16 8 17 6 7 3 5 2 7

4

Min 2 10 4 1 –8 0 1 0 –7 5

Max 20 20 10 20 20 20 8 18 10 20

Average 8 18 7 15 7 8 5 6 2 9

1 inch = 25.4 mm.

B1 = Changes in functional or structural period (CFP/CSP) in years.

B2 = Functional or structural condition reoccurrence period (FCROP/SCROP) in years.

Max = Maximum.

Min = Minimum.

IRI

The benefits data listed in table 53 under the heading “IRI” indicate that the averages of the 

CFPs (labeled “B1” in the table) of all eight treatments were similar and equaled about 7 years. 

This was expected because a proper construction of 2- and 4-inch (51-and 102-mm) overlays and 

2- and 4-inch (51- and 102-mm) mill-and-fill treatments result in smooth pavement surface and 

almost the same rate of deterioration. Further, the average functional condition reoccurrence 

period (FCROP) in years of any of the eight treatments was about 17 years (i.e., 17 years after 
147

applying any of the eight treatments, the IRI of the treated pavement would be the same as it was 

just before treatment).

Rut Depth

The benefits data listed in table 53 under the heading “Rut Depth” indicate that the benefits in 

terms of RSP (column labeled “B1”) and the structural condition reoccurrence period 

(SCROP) (column labeled “B2”) for AC overlays and mill-and-fill treatments using virgin 

and recycled asphalt mixes were statistically similar. Note that the SCROP is the same as TL 

as previously defined.(5)

Alligator Cracking

The benefits data listed in table 53 under the heading “Alligator Cracking” indicate that the B1s

of the eight treatments varied slightly depending on the thickness of the overlay and the type of 

the AC mix. On average, each treatment caused an increase in RSP of about 6 years. (This varied

from a high of 20 years to a low of 10 years.) The latter was mainly the result of the condition of 

the control sections (i.e., no alligator cracking). Thus, the minimum and maximum change in 

structural period (CSP) should not be taken seriously; they are for information only. The average 

CSP, on the other hand, was a good measure of the benefits of each treatment. Further, the 

average structural period of the 2-inch (51-mm)-thick virgin AC overlay was 1 to 3 years lower 

than the 4 inch (102-mm) virgin AC overlay. The type of AC mix (virgin and recycled) appeared

not to affect the SCROP.

Longitudinal Cracking

The benefits data listed in table 53 under the heading “Longitudinal Cracking” indicate that the 

average CFP/CSP of the test sections subjected to 2- and 4-inch (51- and 102-mm)-thick

overlays and mill and fill treatments using virgin and recycled AC mixes appeared to have had

the lowest CSPs (2 to 4 years), while the CSP for the recycled mixes was about 2 years longer. 

Further, the average SCROP of each of the four mill-and-fill treatments was about 6 years.

Transverse Cracking

The benefits data listed in table 53 under the heading “Transverse Cracking” varied and 

depended on the thickness of the AC overlay. The 2-inch (51-mm)-thick AC overlay yielded a

CSP of 0 years, whereas the 4-inch (102-mm) AC overlay yielded, on average, a CSP of 3 years. 

This was expected because the thin 2-inch (51-mm)-thick overlay has minor resistance to 

reflective cracking. The average SCROP of the 2-inch (51-mm)-thick overlay or mill and fill was 

about 7 years, whereas the average SCROP of the 4-inch (102-mm) overlay or mill and fill was 

about 10 years.

Summary, Conclusions and Recommendations for LTPP SPS-5

Based on data availability in the LTPP database, eight rehabilitation treatments were included in 

the analyses of the treatment benefits. The benefits were estimated by comparing RFP and RSP of 

the test sections and RFP and RSP (CFP/CSP) of the control or linked sections In addition, the 

FCROP/SCROP (the time in years from the treatment to the year during which the pavement 
148

condition or distresses are the same as those before treatment) were also used as calculated 

indicators of benefits. Based on the results of the analyses, the following conclusions were drawn:

• On average, the impact of 2- and 4-inch (51- and 102-mm)-thick virgin or recycled AC 

overlay on the pavement performance was almost the same.

• The 2-inch (51-mm)-thick AC overlay (virgin or recycled mix) did not provide long-term 

remediation of transverse cracking. The cracks in the lower pavement structure typically 

reflected through the overlay in a few years.

• On average, the benefits of the 2-inch (51-mm)-thick AC overlay in terms of alligator 

cracking were slightly less than those of the 4-inch (102-mm)-thick overlay.

• The minimum or maximum CFP or CSP should not be used as an indicator of benefits

because these values are also a function of the conditions and distresses of the control or 

linked sections.

Based on the results of the data, the research team strongly recommends the following:

• Establish a solid criterion for the selection of the control sections. This criterion should 

be based on the similarity of the pavement condition and distresses, traffic, and material 

types to the test sections. Perhaps each control or linked section should border the test 

section in question. Ideally, the roughness, rut depth, cracking, and any other condition 

measures of the control and test sections before treatment should be similar.

• Obtain the history of the selected control or linked section and the test sections and keep 

it in the database. This information should include construction and treatment history as 

well as pavement condition and distress data.

• Measure the pavement condition and distress data no more than 1 month before the 

application of a treatment and no more than 1 month after the completion of the treatment.

• Collect the pavement condition and distress data more frequently (once a year or less) 

and for a longer time period (6 years is recommended) before the treatment is applied.

IMPACTS OF PAVEMENT TREATMENTS ON PAVEMENT PERFORMANCE USING

THE LTPP GPS-6 TEST SECTIONS

The LTPP GPS-6 experiment contained flexible pavement test sections that were overlain prior 

to their assignment to the LTPP Program. The experiment also included test sections that were 

moved from other LTPP experiments after they were subjected to either AC overlay or mill-andfill treatments. The test sections in the GPS-6 experiment were classified as GPS-6A, -6B, -6C, 

-6D, and -6S. The following list explains each of the classifications:

• GPS-6A: The test sections under this classification were part of the original LTPP design. 

They were subjected to AC overlay prior to their assignment to the LTPP Program.
149

• GPS-6B: The test sections under this classification were also part of the original LTPP 

design. They were subjected to AC overlay following assignment to the LTPP Program.

• GPS-6C, -6D, and -6S: The test sections under these classifications did not have an 

experimental design associated with them. They were moved to one of these three

classifications from other LTPP experiments after they were subjected to rehabilitation 

actions according to the following scheme:

o If overlain with recycled AC mixes, they were moved to the GPS-6C classification.

o If overlain using virgin AC mixes, they are moved to the GPS-6D classification.

o If milled and filled using virgin or recycled AC mixes, they are moved to the GPS-6S 

classification.

After an extensive search of the database, all of the test sections in the GPS-6 experiment that 

had three or more before treatment and three or more after treatment time-series pavement 

condition and/or distress data points were grouped according to the following variables:

• Two treatment types (AC overlay and mill and fill).

• AC mix type (virgin and recycled).

• Thickness types (thin ≤ 2.5 inches (63.5 mm) and thick > 2.5 inches (63.5 mm)).

• Four climatic regions (WF, WNF, DF, and DNF).

• One pavement condition (IRI).

• Four pavement distress types.

Therefore, the analyses were conducted to assess the impacts of each treatment type and AC mix 

type and thickness on the pavement performance (IRI, rut depth, and cracking) in each climatic 

region using RFP and RSP of each treated test section before and after treatment.

For each test section, the treatment benefits were expressed in terms of the CFP or CSP, which 

were the difference between the after treatment RFP or RSP and the before treatment RFP or 

RSP. The minimum and maximum CFPs and CSPs and their averages for all test sections located 

in the same climatic region were calculated and listed in table 54 through table 58 depending on 

the pavement condition and distress type. The data in the five tables are discussed in the follow 

subsections per pavement condition and distress type.
150

Table 54. Impacts of various treatments on pavement performance in terms of CFP based on IRI (years).

Treatment 

Type

Mix 

Type

Thicknes

s

Climatic Regions

WF WNF DF DNF

No.

CFP (Year)

No.

CFP (Year)

No.

CFP (Year)

No.

CFP (Year)

Min Max Avg Min Max Avg Min Max Avg Min Max Avg

Overlay

Virgin Thin 6 4 20 11 16 6 17 10 3 6 17 12 0 0 0 —

Thick 6 5 20 13 5 10 14 12 4 3 20 13 1 14 14 14

Recycled Thin 0 0 0 — 4 2 20 10 0 0 0 — 0 0 0 —

Thick 1 13 13 13 3 4 14 8 1 –6 –6 –6 0 0 0 —

Mill and 

fill

Virgin Thin 4 –4 9 4 19 –7 12 7 3 6 14 11 1 13 13 13

Thick 1 19 19 19 4 4 14 9 0 0 0 — 7 5 20 12

Recycled Thin 2 10 13 12 2 11 16 13 0 0 0 — 0 0 0 —

Thick 3 10 20 15 7 3 19 12 0 0 0 — 1 13 13 13

— Indicates no data.

Avg = Average.

No. = Number of test sections.

Min = Minimum.

Max = Maximum.

Thin = ≤ 2.5 inches (63.5 mm).

Thick = > 2.5 inches (63.5 mm).
151

Table 55. Impacts of various treatments on pavement performance in terms of CFP/CSP based on rut depth (years).

Treatment 

Type

Mix 

Type Thickness

Climatic Regions

WF WNF DF DNF

No.

CFP/CSP (Year)

No.

CFP/CSP (Year)

No.

CFP/CSP (Year)

No.

CFP/CSP (Year)

Min Max Avg Min Max Avg Min Max Avg Min Max Avg

Overlay

Virgin Thin 6 9 20 13 12 –2 20 10 1 8 8 8 0 0 0 —

Thick 6 5 20 13 3 8 20 12 3 –2 9 5 2 10 13 9

Recycled Thin 0 0 0 — 2 4 10 7 0 0 0 — 0 0 0 —

Thick 0 0 0 — 1 14 14 14 0 0 0 — 0 0 0 —

Mill and 

fill

Virgin Thin 11 1 20 11 20 0 20 16 2 12 16 14 0 0 0 —

Thick 5 3 20 13 2 20 20 20 0 0 0 — 6 2 15 9

Recycled Thin 0 0 0 — 2 11 20 15 0 0 0 — 0 0 0 —

Thick 6 9 20 13 12 –2 20 10 1 8 8 8 0 0 0 —

— Indicates no data.

Avg = Average.

No. = Number of test sections.

Min = Minimum.

Max = Maximum.

Thin = ≤ 2.5 inches (63.5 mm).

Thick = > 2.5 inches (63.5 mm).
152

Table 56. Impacts of various treatments on pavement performance in terms of CSP based on alligator cracking (years).

Treatment 

Type

Mix 

Type Thickness

Climatic Regions

WF WNF DF DNF

No.

CSP (Year)

No.

CSP (Year)

No.

CSP (Year)

No.

CSP (Year)

Min Max Avg Min Max Avg Min Max Avg Min Max Avg

Overlay

Virgin Thin 3 11 20 15 4 5 20 13 0 0 0 — 0 0 0 —

Thick 0 0 0 — 2 8 13 10 0 0 0 — 1 6 6 6

Recycled Thin 0 0 0 — 1 4 4 4 0 0 0 — 0 0 0 —

Thick 0 0 0 — 1 10 10 10 0 0 0 — 0 0 0 —

Mill and 

fill

Virgin Thin 3 7 8 7 5 0 20 6 1 11 11 11 0 0 0 —

Thick 0 0 0 — 1 4 4 4 0 0 0 — 2 12 17 15

Recycled Thin 0 0 0 — 0 0 0 — 0 0 0 — 0 0 0 —

Thick 3 11 20 15 4 5 20 13 0 0 0 — 0 0 0 —

— Indicates no data.

Avg = Average.

No. = Number of test sections.

Min = Minimum.

Max = Maximum.

Thin = ≤ 2.5 inches (63.5 mm).

Thick = > 2.5 inches 63.5 mm).
153

Table 57. Impacts of various treatments on pavement performance in terms of CSP based on longitudinal cracking (years).

Treatment 

Type

Mix 

Type

Thicknes

s

Climatic Regions

WF WNF DF DNF

No.

CSP (Year)

No.

CSP (Year)

No.

CSP (Year)

No.

CSP (Year)

Min Max Avg Min Max Avg Min Max Avg Min Max Avg

Overlay

Virgin Thin 2 0 18 9 5 –3 20 7 0 0 0 — 0 0 0 —

Thick 0 0 0 — 2 5 8 7 0 0 0 — 0 0 0 —

Recycled Thin 0 0 0 — 1 3 3 3 0 0 0 — 0 0 0 —

Thick 0 0 0 — 1 5 5 5 0 0 0 — 0 0 0 —

Mill and 

fill

Virgin Thin 4 –3 5 0 16 –10 20 5 2 10 12 11 0 0 0 —

Thick 0 0 0 — 1 14 14 14 0 0 0 — 2 0 12 6

Recycled Thin 0 0 0 — 1 10 10 10 0 0 0 — 0 0 0 —

Thick 1 17 17 17 2 14 14 14 0 0 0 — 0 0 0 —

— Indicates no data.

Avg = Average.

No. = Number of test sections.

Min = Minimum.

Max = Maximum.

Thin = ≤ 2.5 inches (63.5 mm).

Thick = > 2.5 inches (63.5 mm).
154

Table 58. Impacts of various treatments on pavement performance in terms of CSP based on transverse cracking (years).

Treatment 

Type

Mix 

Type Thickness

Climatic Regions

WF WNF DF DNF

No.

CSP (Year)

No.

CSP (Year)

No.

CSP (Year)

No.

CSP (Year)

Min Max Avg Min Max Avg Min Max Avg Min Max Avg

Overlay

Virgin Thin 3 5 12 8 10 –2 17 6 0 0 0 — 0 0 0 —

Thick 2 8 20 14 3 7 14 11 0 0 0 — 1 4 4 4

Recycled Thin 0 0 0 — 1 3 3 3 0 0 0 — 0 0 0 —

Thick 0 0 0 — 1 3 3 3 0 0 0 — 0 0 0 —

Mill and 

fill

Virgin Thin 0 0 0 — 5 –6 16 5 1 3 3 3 0 0 0 —

Thick 0 0 0 — 1 5 5 5 0 0 0 — 3 0 12 7

Recycled Thin 0 0 0 — 2 10 11 11 0 0 0 — 0 0 0 —

Thick 2 8 11 9 3 8 16 13 0 0 0 — 0 0 0 —

— Indicates no data.

Avg = Average.

No. = Number of test sections.

Min = Minimum.

Max = Maximum.

Thin = ≤ 2.5 inches (63.5 mm).

Thick = > 2.5 inches (63.5 mm).
155

IRI

The data in table 54 indicate the following, on average:

• The thin and thick overlays using virgin AC mix extended the pavement functional 

period by about 11 and 13 years, respectively.

• The thick overlays using recycled AC mix extended the pavement functional period by 

13 years in the WF region and by 8 years in the WNF region. The construction of this 

overlay type on the single test section in the DF region caused 6 years loss in the 

pavement functional period. The reason was the rough pavement surface after 

construction.

• The CFPs of the thin mill and fill treatment using virgin AC mix were 4, 7, 11, and 

13 years in the WF, WNF, DF, and DNF regions, respectively, while the CFPs of the 

thick mill-and-fill treatment using virgin AC mixes are 19, 9, and 12 years in the WF, 

WNF, and DNF regions, respectively.

• The thin mill-and-fill treatment using recycled AC mix extended the pavement functional 

period by about 12 years in the WF and WNF regions, Whereas the thick mill-and-fill 

treatment using recycled AC mix extended the pavement functional period by about 

13 years in the WF, WNF, and DNF regions, respectively.

Rut Depth

The data in table 55 indicate the following, on average:

• The thin and thick overlays using virgin AC mix extended the pavement structural period 

by about 13, 11, 7, and 9 years in the WF, WNF, DF, and DNF regions, respectively.

• The thin and thick overlays using recycled AC mix in the WNF region extended the 

pavement structural period by 7 and 14 years, respectively.

• The thin mill-and-fill treatment using virgin AC mix extended the pavement structural 

period by 11, 16, and 14 years in the WF, WNF, and DF regions, respectively. On the 

other hand, the thick mill-and-fill treatment using virgin AC mix extended the pavement 

structural period by 13, 20, and 9 years in the WF, WNF, and DNF regions, respectively.

• The thick mill-and-fill treatment using recycled AC mix extended the pavement structural 

period by 15 years in the WNF regions, whereas the thick mill-and-fill treatment using 

recycled AC mix caused a 5-year loss in the pavement structural period in the WF region. 

This was most likely due to inadequate compaction during the construction of the AC fill.
156

Alligator Cracking

The data in table 56 indicate the following, on average:

• The thin virgin AC mix overlays extended the pavement structural period by about 

14 years in the WF and WNF regions while the thick overlay using virgin AC mix 

extended the pavement structural period by 10 and 6 years in WNF and DNF 

regions, respectively.

• In the WNF region, the thin and thick recycled AC mix overlays extended the pavement 

structural period by 4 and 10 years, respectively.

• The thin mill-and-fill treatment using virgin AC mix extended the pavement structural 

period by about 6 years in the WF and WNF regions and by 11 years in the DF region,

whereas the thick mill-and-fill treatment using virgin AC mix extended the pavement 

structural period by 4 and 14 years in the WNF and DNF regions, respectively.

• The thick mill-and-fill treatment using recycled AC mix extended the pavement structural 

period by about 13 years in the WF and WNF regions.

Longitudinal Cracking

The data in table 57 indicate the following, on average:

• The thin overlays using virgin AC mix extended the pavement structural period by about 

8 years in the WF and WNF regions. Likewise, the thick overlays using virgin AC mix 

extended the pavement structural period by 8 years in the WNF region.

• The thin and thick overlays using recycled AC mix extended the pavement structural 

period by about 4 years in the WNF region.

• The thin mill and fill treatment using virgin AC mix extended the pavement structural 

period by 5 years in the WF and WNF regions and by 1 years in the DF region, whereas 

the thick mill-and-fill treatment using virgin AC mix extended the pavement structural 

period by 14 and 6 years in the WNF and DNF regions, respectively.

• The thick mill-and-fill treatment using recycled AC mix extended the pavement structural 

period by about 15 years in the WF and WNF regions.

Transverse Cracking

The data in table 58 indicate the following, on average:

• The thin overlays using virgin AC mix extended the pavement structural period by about 

7 years in the WF and WNF regions, whereas the thick overlays using virgin AC mix 

extended the pavement structural period by 14, 11, and 4 years in the WF, WNF, and 

DNF regions, respectively.
157

• The thin and thick overlays using recycled AC mix extended the pavement structural 

period by 3 years in the WNF region.

• The thin mill-and-fill treatment using virgin AC mix extended the pavement structural 

period by 5 years in the WNF region. On the other hand, the thick mill-and-fill treatment 

using virgin AC mix extended the pavement structural period by about 6 years in the 

WNF and DNF regions.

• The thin mill-and-fill treatment using recycled AC mix extended the pavement structural 

period by 11 years in the WNF region. The thick mill-and-fill treatment using recycled 

AC mix extended the pavement structural period by nine and thirteen years in the WF 

and WNF regions, respectively.

Impact of the Before Treatment Condition and Distress on the Performance of the 

Pavement After Treatment for LTPP GPS-6

Several attempts were made to analyze the impacts of the before treatment pavement condition 

(IRI) and distresses (rut depths and cracking) on the pavement performance after treatment. 

Examples of the results for thin and thick virgin AC overlay and for IRI and transverse cracking 

are shown in the T2Ms in table 59 through table 62. Although only 27 and 16 test sections could 

be analyzed for thin and thick overlays using virgin AC mix, respectively, the results were 

logical and expected. The data in table 59 and table 60 indicate that the before treatment 

pavement condition (IRI) had minute to no effects on the RFP of the test sections. This was more 

pronounced for the thick AC overlay than for the thin AC overlay. That is if the AC overlay was 

constructed properly, it would produce a smooth pavement surface. Certainly thicker AC 

overlays will be constructed using two or more courses. The greater the number of the overlay 

courses, the smoother the final pavement surface is. The implication herein is that, if a pavement 

section is to be treated based on high IRIs (low ride quality), then the AC overlay should be 

constructed using at least two courses (two lifts). Otherwise, the original rough pavement surface 

could be milled to a smoother surface and then subjected to a single course (one lift) AC overlay.
158

Table 59. Functional T2M for thin overlay using virgin AC mix (IRI, number of LTPP test sections).

Row Designation

Column Designation

A B C D E F G H I J K L

RFP Before and After Thin Overlay Using Virgin AC Mix Based on IRI

Before Treatment After Treatment

RFP CS and Number and Percent of Pavement 

Sections in Each CS

RFP CS (Code and RFP Ranges (Years)) and 

Number of LTPP Test Sections Transferred 

From Each Before Treatment CS to the 

Indicated After Treatment CSs

Weighted Average 

FCROP, CFP, and 

After Treatment RFP 

of the Treatment 

(Years)

CS LTPP Test Sections

1 2 3 4 5 FCROP CFP RFP

RFP 

Condition 

Code

RFP 

Ranges 

(Years) Number Percent

A 1 < 2 4 15 < 2 2 to < 4 4 to < 8 8 to < 13 ≥ 13 14 11 12

B 2 2 to < 4 2 7 0 0 0 0 2 20 13 16

C 3 4 to < 8 2 7 0 0 0 0 2 18 10 16

D 4 8 to < 13 15 56 0 0 0 1 14 10 6 16

E 5 ≥ 13 4 15 0 0 0 0 4 9 0 16

F Total 27 100 0 0 1 2 24 12 6 15

Note: Bolding indicates no gain.
159

Table 60. Functional T2M for thick overlay using virgin AC mix (IRI, number of LTPP test sections).

Row Designation

Column Designation

A B C D E F G H I J K L

RFP Before and After Thick Overlay Using Virgin AC Mix Based on IRI

Before Treatment After Treatment

RFP CS and Number and Percent of Pavement 

Sections in Each CS

RFP CS (Code and RFP Ranges (Years)) and 

Number of LTPP Test Sections Transferred 

From Each Before Treatment CS to the 

Indicated After Treatment CSs

Weighted Average FCROP, CFP, and 

After Treatment RFP of the 

Treatment (Years)

CS

LTPP Test Sections 1 2 3 4 5

FCROP CFP

After

Treatment

RFP

RFP Condition 

Code

RFP 

Ranges 

(Years) Number Percent < 2 2 to < 4 4 to < 8 8 to < 13 ≥ 13

A 1 < 2 4 25 0 0 0 0 4 20 15 16

B 2 2 to < 4 0 0 0 0 0 0 0 — — —

C 3 4 to < 8 4 25 0 0 0 0 4 20 10 16

D 4 8 to < 13 5 31 0 0 0 0 5 10 6 16

E 5 ≥ 13 3 19 0 0 0 0 3 18 0 16

F Total 16 100 0 0 0 0 16 16 8 16

Note: Bolding indicates no gain.

— Indicates no data.
160

Table 61. Structural T2M for thin overlay using virgin AC mix (transverse cracking, number of LTPP test sections).

Row Designation

Column Designation

A B C D F G H I J K L M

RSP Before and After Thin Overlay Using Virgin AC Mix Based on Transverse Cracking

Before Treatment After Treatment

RSP CS and Number and Percent of Pavement Sections 

in Each CS

RSP CS (Code and RSP Ranges (Years)) 

and Number of LTPP Test Sections 

Transferred From Each Before Treatment 

RSP CS to the Indicated After Treatment 

RSP CSs

Weighted Average SCROP, CSP, and 

After Treatment RSP of the Treatment 

(Years)

CS

LTPP Test Sections 1 2 3 4 5

SCROP CSP

After 

Treatment 

RSP

RSP Condition 

Code

RSP 

Ranges 

(Years) Number Percent < 2 2 to < 4 4 to < 8 8 to < 13 ≥ 13

A 1 < 2 3 23 1 0 0 2 0 9 6 7

B 2 2 to < 4 2 15 0 0 0 0 2 12 13 16

C 3 4 to < 8 4 31 0 1 0 1 2 7 5 11

D 4 8 to < 13 3 23 0 0 1 0 2 6 3 13

E 5 ≥ 13 1 8 0 0 0 1 0 6 –6 10

F Total 13 100 1 1 1 4 6 8 5 11

Note: Bolding indicates no gain.
161

Table 62. Structural T2M for thick overlay using virgin AC mix (transverse cracking, number of LTPP test sections).

Row Designation

Column Designation

A B C D F G H I J K L M

RSP Before and After Thick Overlay Using Virgin AC Mix Based on Transverse Cracking

Before Treatment After Treatment

RSP CS and Number and Percent of Pavement Sections in 

Each CS

RSP CS (Code and RSP Ranges in Years) and 

Number of LTPP Test Sections Transferred 

From Each Before Treatment RSP CS to the 

Indicated AT RSP CSs

Weighted Average SCROP, 

CSP, and After Treatment 

RSP of the Treatment (Years)

CS LTPP Test Sections

1 2 3 4 5

SCROP CSP

After 

Treatment 

RSP

RSP Condition 

Code

RSP Ranges 

(Years) Number Percent

< 2 2 to < 4 4 to < 8 8 to < 13 ≥ 13

A 1 < 2 3 60 0 0 0 1 2 13 13 14

B 2 2 to < 4 0 0 0 0 0 0 0 — — —

C 3 4 to < 8 2 40 0 0 0 2 0 10 4 10

D 4 8 to < 13 0 0 0 0 0 0 0 — — —

E 5 ≥ 13 0 0 0 0 0 0 0 — — —

F Total 5 100 0 0 0 3 2 12 9 12

Note: Bolding indicates no gain.

— Indicates no data.
162

Unfortunately, there were fewer test sections available for analyses of the structural period. 

Table 61 and table 62 for thin and thick virgin AC overlay, respectively, list the results of the 

analyses of the impacts of transverse cracking before treatment on the pavement performance in 

terms of transverse cracking after treatment. There were only 13 test sections for thin AC overlay 

and only 5 for the thick AC overlay. When these limited sections were distributed among the 

five CSs before treatment, the number of test sections in each CS became statistically 

insignificant to support reliable conclusions. However, the limited data indicated that the longer 

the length of transverse cracks before treatment was, the worse the pavement performance after 

treatment was. Further, the thick AC overlay performed better than the thin overlay; it retarded

reflective cracking better.

Summary, Conclusions, and Recommendations for LTPP GPS-6

The performance of pavement rehabilitation is a function of many variables, including the type 

of rehabilitation, the material used, construction, traffic, and climate. Results of the analyses of 

the GPS-6 test sections confirmed that. Although the GPS-6 test sections did not represent 

enough data for detailed analyses of each variable, several cautious conclusions could be drawn

given the limited number of test sections. These conclusions include the following:

• In each climatic region, the impacts of the thin and thick overlay or mill-and-fill 

treatment on IRI were almost the same. This was expected because good quality 

construction could decrease the pavement roughness substantially regardless of the 

overlay thickness.

• Thin and thick overlays and mill-and-fill treatments had a similar impact on rut depths. 

Once again, this was expected because most pavement rutting occurs early in the 

pavement life and can be removed during the treatment. One word of caution is that poor 

compaction of the overlay could precipitate early rutting in the AC overlay.

• The impact of the AC overlay and mill-and-fill treatments on pavement performance in 

terms of alligator and longitudinal cracking could not be assessed with an acceptable

degree of certainty because of the limited number of test sections.

• The impact of thin and thick overlay on pavement performance in terms of transverse 

cracking was very much as expected; the thicker the overlay or the mill-and-fill treatment

was, the more the reflective transverse cracking was retarded and the longer the pavement 

structural period was.

• The effects of the conditions in the different climatic regions on the pavement condition 

and distress could not be fully assessed because of the limited number of test sections in 

each climatic region.

Based on the results of the analyses, the following is strongly recommended:

• The data collection frequency on newly designed and constructed or newly rehabilitated 

LTPP test sections should be increased to a minimum of once a year, twice a year for test 
163

sections subjected to light rehabilitation, and three times a year for test section subjected 

to maintenance treatments.

• The construction process should be documented, and the quality control data should be 

included in the database.

• Future analyses of pavement condition and distress data should be based on the new 

pavement rating and classification systems RFP and RSP.

• The benefits of pavement rehabilitation and/or maintenance treatments should be 

measured in terms of RFP or RSP, CFP or CSP, and FCROP or SCROP.

ORCSE METHOD

The procedures for the analyses for flexible pavement sections described earlier in this chapter 

provide a basis for evaluating pavement sections for which there is sufficient data to meet the 

data quality control requirements outlined in chapter 4. As discussed, not all State transportation 

departments maintain sufficient pavement management system (PMS) data to use these outlined 

methods or may not regularly monitor all pavement sections to the same rigorous standards. The 

estimation of a pavement section RFP or RSP from a single condition or distress record would be 

extremely beneficial for these State transportation departments to use on a network and 

individual section basis. The remainder of chapter 5 presents a novel method developed using the 

LTPP data for estimation of RFP or RSP for pavement sections that have experienced lower 

levels of monitoring or sections that may not yet have sufficient data records for modeling owing 

to age. Herein, the method is referred to as the ORCSE method. It is very important to note that 

the development of this method was not a part of the original study or study objective. Although 

it is included herein to show that such analyses are possible, it requires further study and 

calibration, and it must be emphasized that probabilistic issues are very important and should be 

considered in a more balanced and comprehensive PMS.

ORCSE Method and Development Procedures Using LTPP Data

The ORCSE method is a probabilistic model used to estimate the probability of a pavement section 

being part of a specific CS (RFP or RSP range) based on a single condition or distress record as 

shown in figure 56. This probability is developed using multiple probability distributions functions 

calibrated to local or regional pavement design groups, specific designs, construction practices, 

and other design or management factors. Similar to the local calibration recommended for 

MEPDG use in the FHWA’s Local Calibration of the MEPDG Using Pavement Management 

Systems, Final Report, Volume I, the ORCSE method provides the best estimates of a pavement 

section CS when calibrated specifically to one or multiple of these design or management 

constraints as long as those constraints are significant factors in pavement performance.(82)
164

1 inch/mi = 0.0158 m/km.

Figure 56. Graph. ORCSE model probability graph from LTPP SPS-1 before treatment 

evaluation.

The development of the ORCSE model for a group of pavement sections using an entire database 

of newly constructed pavements should follow eight steps. The flowchart in figure 57 illustrates 

the process. It is important to note that the ORCSE model should be tested using model 

validation techniques to ensure consistent results when using it to estimate the RFP/RSP of 

pavement sections. 
165

Figure 57. Illustration. Flowchart of ORCSE model steps with examples from LTPP SPS-1 

before treatment evaluation.

Note that although steps 1 through 3 were discussed in greater detail in previous sections of this 

chapter and are the basis for the RFP/RSP approach recommended in this report, they are 

summarized here with minor modifications to show the complete process:

• Step 1: For each pavement section and collection of condition or distress data, calculate 

the model parameters of the appropriate condition or distress model (see table 27).
166

• Step 2: Eliminate sections that fail to meet preestablished criteria, and, for the accepted 

sections, separate a portion of your accepted data for modeling and a portion for validation 

as outlined by the selected validation method. These criteria could include the following:

o Negative condition or distress growth.

o Construction-related failures observed early in pavement life or field observations of 

poor construction.

o Poor model fitness for the distress data.

It should be noted that a model could be constituted specifically of poorly constructed 

pavement sections to provide an estimate for other sections that might have similar 

construction deficiencies.

• Step 3: For sections that pass step 2, calculate the maximum functional or structural 

period using the previously fit model parameters (step 1) for each section.

• Step 4: For the maximum functional or structural period calculated in step 3, calculate 

the condition or distress back to the date of construction for each individual section on a 

yearly basis. If condition or distress values are not available for the period immediately 

after construction, consider using back propagating to the time of construction (figure 58

to figure 61).

1 inch/mi = 0.0158 m/km.

Figure 58. Graph. Backpropagation of IRI data for SHRP ID 26_0116 from an RFP of 

0 years.
167

1 inch/mi = 0.0158 m/km.

Figure 59. Graph. Backpropagation of IRI data for SHRP ID 19_0102 from an RFP of 

0 years.

1 inch/mi = 0.0158 m/km.

Figure 60. Graph. Backpropagation of IRI data for SHRP ID 19_0103 from an RFP of 

0 years.
168

1 inch/mi = 0.0158 m/km.

Figure 61. Graph. Backpropagation of IRI data for SHRP IDs 26_0116, 19_0102, and 

19_0103 displaying variation in IRI growth from an RFP of 0 years.

• Step 5: Divide the condition or distress values into their respective CSs based on ranges 

of RFP or RSP (see chapter 3). Recall that CS 1 corresponds to the RFP or RSP range 

from 0 to 2 years, and CS 5 corresponds to an RFP or RSP of 13 or more years.

• Step 6: For each of the grouped CS data collections (CS 1, CS 2, CS 3, CS 4, and CS 5), 

model the data distributions using a user-preferred probability density function (PDF), 

which has been tested for fitness, and calculate the 5th, 25th, 50th, 75th, and 

95th percentile for each PDF. These percentiles will be associated with the median, 

middle 50, and middle 90 percent of pavement section behaviors per CS.

• Step 7: Rotate each PDF vertically and orient each over its respective CS, connect each 

equivalent percentile range with line segments, and display the condition or distress 

threshold if it is applicable or assists in use.

• Step 8: Compare results between model and validation data groups using a selected cross 

validation technique to ensure similar results with any subset of data, ensuring accurate 

estimates of RFP and RSP for pavement sections to be assessed.

Specific details for individual steps, as applied for this report, include the following:

• An exponential model was applied to evaluated IRI data.

• The same elimination data as discussed early in this report for IRI data were applied as 

well as one additional criterion—pavements with functional and structural periods longer 
169

than 25 years were removed because of the typically low fitness of the models and earlylife records, which prevented construction of a complete lifecycle model. This is an 

important aspect of this method, and best results should be observed if model fitting is 

completed on sections that have substantially surpassed the established threshold before 

maintenance or rehabilitation occurred.

• For model validation, a repeated random subsampling validation was used with an 

80/20 model-validation split. Modeling and validation were completed five times.

• The PDF used was Epanechnikov Kernels PDF modeling, which produced the closest fit 

to the observed data while providing secondary benefits of allowing visual identification 

of possible sub-probability groups or divergent behaviors within the larger generalized 

sample. Divergent or bimodal behaviors might be indicative of construction issues or 

premature failure of multiple sections otherwise experiencing similar condition or distress 

growth. Although this modeling method was selected, any PDF or Kernel method may be 

appropriate after evaluation of the goodness of fit.

The ORCSE model graph may also be represented as a table considering the CS probability for a 

set of condition or distress values or range of values. An example with averaged weighted 

probabilities per 16 inches/mi (0.25 m/km) IRI range can be reviewed in table 63. Note that 

shading could be added to the table to emphasize the best match. 

Table 63. ORCSE model table example from LTPP SPS-1 before treatment evaluation.

IRI Range 

(inches/mi)

Probability of a CS or RFP Bracket for Selected IRI Ranges (percent)

CS 1 or RFP

< 2 years

CS 2 or RFP

2 to < 4 years

CS 3 or RFP

4 to < 8 years

CS 4 or RFP

8 to < 13 years

CS 5 or RFP

≥ 13 years

16–32 0 0 0 3 97

32–48 0 0 3 14 83

48–63 0 0 4 19 77

63–79 0 0 9 39 52

79–95 0 3 19 66 11

95–111 0 12 49 37 1

111–127 3 28 59 10 0

127–143 13 61 22 3 0

143–158 55 36 6 3 0

158–171 88 8 1 2 0

1 inch/mi = 0.0158 m/km.

The procedure for creating a table from the ORCSE model graph is outlined in the following 

four steps:

• For each analysis grouping (i.e., SPS-1 all climates), delineate the experienced condition 

or distress ranges desired into subdistress ranges (16 inches/mi (0.25 m/km) intervals for 

this report using IRI).

• For each of the subdistress values, record probabilities from the previously created 

ORCSE model for each of the five CSs.
170

• For each subdistress range (i.e., 16 to 32 inches/mi (0.25 to 0.50 m/km)), average and 

enter PDF probabilities into a preliminary ORCSE table.

• Modify the preliminary table by weighting the probabilities for each subdistress across all 

five CS columns. This results in a cumulative 100-percent probability for each 

subdistress row and helps to emphasize the differences in raw CS probabilities.

For example, the IRI range of 16 to 32 inches/mi (0.25 to 0.50 m/km) for CS 5 may have 24- and 

30-percent probability, respectively. The average probability for the range is thus the average of 

24 and 30 percent—27 percent. If it is assumed that for the remaining CS groups, whose percent 

probabilities are 20, 12, 8, and 1 percent for CS 4, CS 3, CS 2, and CS 1, respectively, the 

weighted probabilities can be found. Weighted probabilities are calculated for each CS as the 

averaged probability of the selected CS divided by the summation of all CSs for the subdistress 

range (16 to 32 inches/mi (0.25 to 0.50 m/km)). For this example, the weighted values are 40, 29, 

18, 12, and 1 percent probability for CS 5, CS 4, CS 3, CS 2, and CS 1, respectively. It can then 

be best estimated that for an IRI between 16 to 32 inches/mi (0.25 and 0.50 m/km), the pavement 

has the greatest chance of being in CS 5 with a greater than or equal to 13-year RFP.

Results of ORCSE Predictions and Analyses of LTPP SPS-1 Test Sections

Based on the procedures provided in chapter 5, analyses were completed for ORCSE models 

based on the same LTPP SPS-1 data collection. The results were generated in both ORCSE 

model graphs and tables for a total of five iterations, a randomly selected sample group from the 

complete dataset, to provide model validation. Figure 62 to figure 66 depict the resulting ORCSE 

graphs for the respective resampling.

1 inch/mi = 0.0158 m/km.

Figure 62. Graph. ORCSE model graph for LTPP SPS-1 virgin pavement analysis for

iteration 1.
171

1 inch/mi = 0.0158 m/km.

Figure 63. Graph. ORCSE model graph for LTPP SPS-1 virgin pavement analysis for

iteration 2.

1 inch/mi = 0.0158 m/km.

Figure 64. Graph. ORCSE model graph for LTPP SPS-1 virgin pavement analysis for

iteration 3.
172

1 inch/mi = 0.0158 m/km.

Figure 65. Graph. ORCSE model graph for LTPP SPS-1 virgin pavement analysis for

iteration 4.

1 inch/mi = 0.0158 m/km.

Figure 66. Graph. ORCSE model graph for LTPP SPS-1 virgin pavement analysis for

iteration 5. 
173

Results of Validation of ORCSE Predictions and Analyses of LTPP SPS-1 Test Sections

Table 64 through table 68 list each of the resulting ORCSE models for the respective sampling.

Table 64. ORCSE model table for LTPP SPS-1 virgin pavement analysis for iteration 1.

IRI Range 

(inches/mi

)

Probability of a CS or RFP Bracket for Selected IRI Ranges (percent)

CS 1 or 

RFP

< 2 Years

CS 2 or RFP

2 to < 4 

Years

CS 3 or RFP

4 to < 8 

Years

CS 4 or RFP

8 to < 13 

Years

CS 5 or 

RFP

≥ 13 Years

16–32 0 0 0 3 97

32–48 0 0 3 14 83

48–63 0 0 4 19 77

63–79 0 0 9 39 52

79–95 0 3 19 66 11

95–111 0 12 49 37 1

111–127 3 28 59 10 0

127–143 13 61 22 3 0

143–158 55 36 6 3 0

158–171 88 8 1 2 0

1 inch/mi = 0.0158 m/km.

Table 65. ORCSE model table for LTPP SPS-1 virgin pavement analysis for iteration 2.

IRI Range 

(inches/mi

)

Probability of a CS or RFP Bracket for Selected IRI Ranges (percent)

CS 1 or 

RFP

< 2 Years

CS 2 or RFP

2 to < 4 

Years

CS 3 or RFP

4 to < 8 

Years

CS 4 or RFP

8 to < 13 

Years

CS 5 or 

RFP

≥ 13 Years

16–32 0 0 0 19 81

32–48 0 0 2 21 77

48–63 0 0 3 25 71

63–79 0 0 10 51 38

79–95 0 3 28 59 10

95–111 0 13 59 27 1

111–127 3 35 53 9 0

127–143 15 65 17 3 0

143–158 62 31 5 3 0

158–171 90 6 1 2 0

1 inch/mi = 0.0158 m/km.
174

Table 66. ORCSE model table for LTPP SPS-1 virgin pavement analysis for iteration 3.

IRI Range 

(inches/mi

)

Probability of a CS or RFP Bracket for Selected IRI Ranges (percent)

CS 1 or 

RFP

< 2 Years

CS 2 or RFP

2 to < 4

Years

CS 3 or RFP

4 to < 8 

Years

CS or RFP 4

8 to < 13 

Years

CS 5 or 

RFP

≥ 13 Years

16–32 0 0 0 21 79

32–48 0 0 2 20 78

48–63 0 0 3 22 74

63–79 0 0 9 44 47

79–95 0 3 21 65 11

95–111 0 12 53 34 1

111–127 3 29 59 9 0

127–143 13 63 21 3 0

143–158 56 36 5 3 0

158–171 90 7 1 2 0

1 inch/mi = 0.0158 m/km.

Table 67. ORCSE model table for LTPP SPS-1 virgin pavement analysis for iteration 4.

IRI Range 

(inches/mi

)

Probability of a CS or RFP Bracket for Selected IRI Ranges (percent

CS 1 or 

RFP

< 2 Years

CS 2 or RFP

2 to < 4 

Years

CS 3 or RFP

4 to < 8 

Years

CS 4 or RFP

8 to < 13 

Years

CS 5 or 

RFP

≥ 13 Years

16–32 0 0 0 34 66

32–48 0 0 0 27 73

48–63 0 0 1 25 75

63–79 0 0 4 40 55

79–95 0 3 14 71 13

95–111 0 12 47 39 1

111–127 3 25 65 7 0

127–143 12 62 26 0 0

143–158 53 41 7 0 0

158–171 89 10 1 0 0

1 inch/mi = 0.0158 m/km.
175

Table 68. ORCSE model table for LTPP SPS-1 virgin pavement analysis for iteration 5.

IRI Range 

(inches/mi)

Probability of a CS or RFP Bracket for Selected IRI Ranges (percent)

CS 1 or RFP

< 2 years

CS 2 or RFP

2 to < 4 years

CS 3 or RFP

4 to < 8 years

CS 4 or RFP

8 to < 13 years

CS 5 or RFP

> 13 years

16–32 0 0 0 2 98

32–48 0 0 2 13 84

48–63 0 0 3 19 78

63–79 0 0 9 40 50

79–95 0 3 19 68 11

95–111 0 12 50 37 1

111–127 3 27 60 10 0

127–143 13 62 22 3 0

143–158 54 37 6 3 0

158–171 89 8 1 2 0

1 inch/mi = 0.0158 m/km.

The ORCSE model figures visually indicate that each model iteration produced similar results,

with the most significant variation in iteration 4 primarily adjusting the inner 90-percent 

pavement section behavior. These outer behaviors can be used to identify outliers existing in the 

accepted condition and distress model data as well as to visually approximate the variety of 

aging behavior in each modeled group. This is completed by assessing the width of the 

distribution at each CS—the wider the distribution is, the wider the rates of pavement condition 

or distress change are.

The ORCSE model tables list results similar to those shown in the figures. The tables present the 

same maximum probability trends for each IRI range to CS group and approximately match the 

change in distribution width for each CS. While not demonstrated here, the change in distribution 

width for each CS can also be used to approximate the variety of sections present for a modeled 

pavement section set. The greater the number of nonzero cells per row is, the lower the 

maximum relative probability per row is and the wider the range of section behavior.

The five iterations used to evaluate the consistency of the ORCSE model results were based on 

an 80/20 model-validation split using repeated random subsampling validation with five model 

iterations, as presented earlier. This numeric assessment of the models can be completed by 

comparing the difference between CS ranges estimated using ORCSE figures and tables with the 

modeled functional or structural period per section for each year the pavement section is 

modeled. This analysis provides the approximate number and percent of wrong predictions for 

similar pavements (i.e., those that have a similar design or management characteristics as those 

modeled) as well as details about which CS groups are most commonly incorrectly estimated by 

the ORCSE method.

Individual iteration analyses are presented in table 69 through table 73. These analyses can also 

be presented as a total number of differences for all five iterations, an average, and a median 

difference across each iteration. All three analyses are presented in table 74 through table 76.
176

Table 69. ORCSE validation of estimated versus modeled CS groups for iteration 1.

ORCSE Estimated CS

Modeled CS Difference in CS Incorrect 

Estimates

CS 1 CS 2 CS 3 CS 4 CS 5 Min Max Total (Percent)

CS 1 219 1 0 0 0 –1 –1 1 0

CS 2 0 217 3 0 0 –1 –1 3 1

CS 3 0 1 214 3 2 –2 1 6 3

CS 4 0 0 9 200 11 –1 1 20 9

CS 5 0 0 3 13 204 1 2 16 7

Min = Minimum.

Max = Maximum.

Table 70. ORCSE validation of estimated versus modeled CS groups for iteration 2.

ORCSE Estimated CS

Modeled CS Difference in CS Incorrect 

Estimates

CS 1 CS 2 CS 3 CS 4 CS 5 Min Max Total (Percent)

CS 1 240 0 0 0 0 0 0 0 0

CS 2 3 237 0 0 0 1 1 3 1

CS 3 0 4 236 0 0 1 1 4 2

CS 4 0 0 14 226 0 1 1 14 6

CS 5 0 0 0 95 145 1 1 95 40

Min = Minimum.

Max = Maximum.

Table 71. ORCSE validation of estimated versus modeled CS groups for iteration 3.

ORCSE Estimated CS

Modeled CS Difference in CS Incorrect 

Estimates 

CS 1 CS 2 CS 3 CS 4 CS 5 Min Max Total (Percent)

CS 1 160 0 0 0 0 0 0 0 0

CS 2 0 159 1 0 0 –1 –1 1 1

CS 3 0 0 158 2 0 –1 –1 2 1

CS 4 0 0 0 155 5 –1 –1 5 3

CS 5 0 0 0 18 142 1 1 18 11

Min = Minimum.

Max = Maximum.
177

Table 72. ORCSE validation of estimated versus modeled CS groups for iteration 4.

ORCSE Estimated CS

Modeled CS Difference in CS Incorrect 

Estimates 

CS 1 CS 2 CS 3 CS 4 CS 5 Min Max Total (Percent)

CS 1 220 0 0 0 0 0 0 0 0

CS 2 2 214 4 0 0 –1 1 6 3

CS 3 0 3 208 7 2 –2 1 12 5

CS 4 0 0 9 194 17 –1 1 26 12

CS 5 0 0 1 16 203 1 2 17 8

Min = Minimum.

Max = Maximum.

Table 73. ORCSE validation of estimated versus modeled CS groups for iteration 5.

ORCSE Estimated CS

Modeled CS Difference in CS Incorrect 

Estimates 

CS 1 CS 2 CS 3 CS 4 CS5 Min Max Total (Percent)

CS 1 199 1 0 0 0 –1 –1 1 1

CS 2 0 198 2 0 0 –1 –1 2 1

CS 3 0 1 195 2 2 –2 1 5 3

CS 4 0 0 7 185 8 –1 1 15 8

CS 5 0 0 8 27 165 1 2 35 18

Min = Minimum.

Max = Maximum.

Table 74. ORCSE summarized validation of estimated versus modeled CS groups for all 

iterations.

ORCSE 

Estimated CS

Modeled CS Difference in CS Incorrect 

Estimates

CS 1 CS 2 CS 3 CS 4 CS 5 Min Max Total (Percent)

CS 1 1,038 2 0 0 0 –1 0 2 0

CS 2 5 1,025 10 0 0 –1 1 15 1

CS 3 0 9 1,011 14 6 –2 1 29 3

CS 4 0 0 39 960 41 –1 1 80 8

CS 5 0 0 12 169 859 1 2 181 17

Min = Minimum.

Max = Maximum.
178

Table 75. ORCSE averaged validation of estimated versus modeled CS groups, all 

iterations.

ORCSE Estimated CS

Modeled CS Difference in CS Incorrect 

Estimates 

CS 1 CS 2 CS 3 CS 4 CS 5 Min Max Total (Percent)

CS 1 208 0 0 0 0 0 0 1 0

CS 2 1 205 2 0 0 –1 0 3 1

CS 3 0 2 202 3 1 –1 1 6 3

CS 4 0 0 8 192 8 –1 1 16 7

CS 5 0 0 2 34 172 1 2 36 17

Min = Minimum.

Max = Maximum.

Table 76. ORCSE median validation of estimated versus modeled CS groups, all iterations.

ORCSE Estimated CS

Modeled CS Difference in CS Incorrect 

Estimates 

CS 1 CS 2 CS 3 CS 4 CS 5 Min Max Total (Percent)

CS 1 219 0 0 0 0 0 0 0 0

CS 2 0 214 2 0 0 –1 –1 2 1

CS 3 0 1 208 2 2 –2 1 5 3

CS 4 0 0 9 194 8 –1 1 17 8

CS 5 0 0 1 18 165 1 2 19 11

Min = Minimum.

Max = Maximum.

The validation results indicate that for the most critical CS groups, CS 1 and CS 2, representing 

from the time the condition or distress threshold was reached to approximately 4 years 

beforehand, there were very few ORCSE estimation errors. The total errors during that period 

were only 17 across 2,080 total estimates, or less than 1 percent of all CS 1 and CS 2 estimates. 

For CS 1, the maximum difference was an overestimate of 1 CS group by the ORCSE 

estimation, representing a change from 0 to 2 years to 2 to 4 years. CS 2 also experienced the 

maximum estimate difference of 1 CS group with both occurrences of over and under-predicting 

CS groups compared with those sections modeled at CS 2. It is important to note that a change 

from one CS group to another in estimation may not be the entire width of the group. For an 

overestimation of CS 2 and CS 3, the modeled remaining years may be 5 while the estimated 

remaining years are 4, a difference of only 1 year.

The remaining CS groups experienced higher estimation errors, ranging from 1 to 3 percent for 

CS 3, 3 to 12 percent for CS 4, and 7 to 40 percent for CS 5, with average and median errors for 

each typically near the lower to middle of these ranges indicating that the majority of interactions 

experienced strong results. Further, the maximum CS difference in estimation between the 

ORCSE model method and the distress projection from the beginning of this chapter was only 

two CSs of the five total, again indicating a typically well-fit estimation of pavement section CS.
179

Summary, Conclusions, and Recommendations for the ORCSE Model

The ORCSE method, developed using LTPP data for estimation of RFP or RSP, was applied to 

pavement sections that had experienced lower levels of monitoring or sections that might not yet 

have had sufficient data records for modeling owing to age. Modeling and validation of this 

novel method indicate the following:

• The ORCSE method was successful in predicting pavement sections CSs throughout the 

pavement sections’ entire functional or structural period.

• The majority of incorrect estimates by the ORCSE method occurred soon after treatment 

when additional time was available to continue recording condition or distress data and 

perform comprehensive section modeling.

• The ORCSE method average estimation error for the most critical CSs—CS 1 and 

CS 2—was less than 1 percent.

• The ORCSE method was resilient when using different data selections for calibration 

based on repeated subsampling validation.

It is recommended that the ORCSE method be expanded to address additional conditions and 

distresses as well as applied to a wider range of LTPP and State transportation department data to 

further verify its successful prediction of CS groups. There is a significant potential benefit to 

local roadway owners as well as Federal roadway managers and State transportation departments 

in the use of the ORCSE method when planning pavement preservation, rehabilitation, and 

reconstruction.

181

CHAPTER 6. LTPP DATA ANALYSES OF RIGID PAVEMENTS

Chapter 5 presented the results of the analyses of the time-series condition and distress data of 

the LTPP flexible pavement test sections. This chapter presents the results of the analyses of the 

time-series condition and distress data of the LTPP rigid pavement test sections. Once again, the 

data used in this study were obtained from the LTPP database Standard Data Release 28.0.

IMPACTS OF MAINTENANCE TREATMENTS ON PAVEMENT CONDITION AND 

DISTRESS USING THE LTPP SPS-2 TEST SECTIONS 

The main objective of the SPS-2 experiment was to study the effects of the following:

(75)

• Climatic region.

• Drainage.

• Slab thickness (8 and 11 inches (203 and 279 mm)).

• The 14-day concrete flexural strength (551 and 899 lbf/inch2(3.8 and 6.2 MPa)).

• Slab width (12 and 14 ft (3.66 and 4.27 m)).

• Base type (dense-graded aggregate base, asphalt-treated base, permeable asphalt-treated 

base, and a combination thereof).

Analysis Steps

In this study, the analyses of the impacts of the various design variables were accomplished 

using the following steps:

• Step 1: For each pavement test section in the LTPP SPS-2 experiment, the time-dependent

pavement condition (IRI) and distress (transverse and longitudinal cracking) data were 

downloaded from the LTPP database, organized, and analyzed. Results of the analyses 

included the RFP and RSP of each test section calculated as the time period from the time 

of construction to the time when the pavement condition or distress reached the appropriate 

threshold values. The reason for calculating RFP and RSP from the construction data 

(surface age is 0 years) was that the dates of construction and the dates of the last data 

collection for the SPS-2 test sections were not the same. The implication of this is that the 

reference time for each SPS-2 test section was taken as the date of construction.

• Step 2: For each pavement condition and distress type, the resulting RFPs and RSPs and 

other inventory data (such as SHRP ID, State, slab thickness, drainage, slab width, 

concrete flexural strength, and so forth) were then organized in a Microsoft® Excel 

spreadsheet format.
182

• Step 3: For each SHRP ID and for each pavement condition and distress type, the 

minimum and maximum RFPs and RSPs and their averages were calculated and listed in 

the Microsoft® Excel spreadsheets.

• Step 4: The data were then organized into the following groups and subgroups and in 

table 77. The main objective of the division was to separate the design variables affecting 

pavement performance.

o Climatic region groups: The results of the analyses were organized into the 

four climatic regions—WF, WNF, DF, and DNF.

o Slab thickness subgroups: The results of the analyses in each climatic region were 

then organized into two subgroups based on the slab thicknesses of 8 and 11 inches

(203 and 279 mm).

o Slab width subgroups: The results of the analyses in each slab thickness subgroup 

were then organized into two subgroups based on the slab widths of 12 and 14 ft 

(3.66 and 4.27 m).

o Concrete flexural strength subgroups: The results in each slab width subgroup 

were then organized into two subgroups based on the concrete 14day flexural strength 

of 551 and 899 lbf/inch2(3.8 and 6.2 MPa).

o Drainage subgroups: The results in each concrete flexural strength subgroup were 

further divided into two drainage subgroups (presence and absence of drainage).
183

Table 77. Analysis subgroups and the number of test sections available for analyses in the LTPP SPS-2 experiment in each 

subgroup.

Condition/

Distress 

Type

Lane 

Width 

(ft)

Slab 

Strength 

(lbf/inches2)

Slab 

Thickness 

(inches)

Number of Available Test Sections Based on Climatic Region and 

Presence of Aggregate Base Drainage

WF WNF DF DNF

D ND D ND D ND D ND

IRI

12

500 8 1 4 1 4 2 2 1 2

11 3 8 1 2 3 2 1 2

900 8 3 7 1 2 3 2 1 1

11 1 3 2 3 1 2 1 2

14

500 8 3 6 1 2 3 2 1 2

11 1 4 1 4 1 2 1 2

900 8 2 4 0 4 2 2 1 2

11 2 7 0 2 2 2 1 2

Longitudinal 

cracking

12

500 8 2 3 1 4 2 3 1 2

11 4 8 1 2 1 2 1 2

900 8 4 8 1 2 0 2 1 2

11 2 3 2 4 1 2 1 2

14

500 8 4 7 1 2 1 2 1 2

11 2 4 2 4 2 2 1 2

900 8 2 4 0 4 2 2 1 2

11 4 7 0 2 1 2 1 2

Transverse 

tracking

12 500 8 2 3 1 4 2 2 1 2

11 4 8 1 2 1 2 1 2

900 8 4 8 1 2 0 2 1 2

11 2 3 2 4 1 2 1 2

14 500 8 4 7 1 2 1 2 1 2

11 2 4 2 4 2 2 1 2

900 8 2 4 0 4 2 2 1 2

11 4 7 0 2 1 2 1 1

1 ft = 0.305 m.

1 lbf/inch2 = .00690 MPa.

1 inch = 25.4 mm.

D = Drainable base.

ND = Undrainable base.
184

Analyses Results

The detailed analysis results were submitted to FHWA and are available from the LTPP Customer 

Support Services.(79) For convenience, the detailed results are summarized in table 78 through 

table 89. Because there are many design variables, four tables were populated to summarize the 

impacts of the design variables on RFP or RSP for each pavement condition or distress. Each 

table summarizes the impacts of the climatic region, slab thickness, drainable bases, and a 

combination of slab width and concrete flexural strength on the RFP or RSP of test sections based 

on one condition (IRI) or one distress (longitudinal or transverse cracks). For example, table 78

summarizes the results of the analyses of the impacts of design factors on RFP based on the IRI of 

SPS-2 test sections having slab width 12 ft (3.66 m) and concrete flexural strength 551 lbf/inch2

(3.8 MPa), while table 79 summarizes the results of analyses of the impacts of design factors on 

RFP based on the IRI of SPS-2 test sections having slab width 14 ft (4.27 m) and concrete 

flexural strength 551 lbf/inch2(3.8 MPa). The numbers in the tables indicate the differences in 

years in RFPs or RSPs of the SPS-2 test sections having the top heading parameters relative to 

RFPs and RSPs of the SPS-2 test sections having the side heading parameters. The explanation of 

the listed numbers in the tables is the same as that included in chapter 5 in table 29 through 

table 33. For convenience, the following paragraphs explain the listed numbers in table 78

through table 89 using the data from the first and second rows in table 78.
185

Table 78. Summary of the results of the analyses of the impacts of design factors on RFP based on IRI of LTPP SPS-2 test 

sections with slab width of 12 ft (3.66 m) and concrete flexural strength of 551 lbf/inch2(3.8 MPa).

Climatic 

Region

PCC Slab 

Thickness 

(inches)

Base 

Type

Differences Between RFP of the Top Heading and RFP of the Side Heading (Year) 

WF WNF DF DNF

8-inch

PCC

11-inch

PCC

8-inch

PCC

11-inch

PCC

8-inch

PCC

11-inch

PCC

8-inch

PCC

11-inch

PCC

D ND D ND D ND D ND D ND D ND D ND D ND

WF

8

D * 0 0 –2 0 0 0 0 0 0 0 0 0 –3 0 0

ND 0 * 0 –1 0 0 0 0 0 0 0 0 0 –3 0 0

11 D 0 0 * –2 0 0 0 0 0 0 0 0 0 –3 0 0

ND 2 1 2 * 2 2 2 2 2 2 2 2 2 –2 2 2

WNF

8

D 0 0 0 –2 * 0 0 0 0 0 0 0 0 –3 0 0

ND 0 0 0 –2 0 * 0 0 0 0 0 0 0 –3 0 0

11 D 0 0 0 –2 0 0 * 0 0 0 0 0 0 –3 0 0

ND 0 0 0 –2 0 0 0 * 0 0 0 0 0 –3 0 0

DF

8

D 0 0 0 –2 0 0 0 0 * 0 0 0 0 –3 0 0

ND 0 0 0 2 0 0 0 0 0 * 0 0 0 –3 0 0

11 D 0 0 0 –2 0 0 0 0 0 0 * 0 0 –3 0 0

ND 0 0 0 –2 0 0 0 0 0 0 0 * 0 –3 0 0

DNF

8

D 0 0 0 –2 0 0 0 0 0 0 0 0 * –3 0 0

ND 3 3 3 2 3 3 3 3 3 3 3 3 3 * 3 3

11 D 0 0 0 –2 0 0 0 0 0 0 0 0 0 –3 * 0

ND 0 0 0 –2 0 0 0 0 0 0 0 0 0 –3 0 *

* Indicates the diagonal of the matrix where the top and the side headings are the same.

1 inch = 25.4 mm.

D = Drainable base.

ND = Undrainable base.
186

Table 79. Summary of the results of analyses of the impacts of design factors on RFP based on IRI of LTPP SPS-2 test sections 

with slab width 14 ft (4.27 m) and concrete flexural strength of 551 lbf/inch2(3.8 MPa).

Climatic 

Region

PCC Slab 

Thickness 

(inches)

Base 

Type

Differences Between RFP of the Top Heading and RFP of the Side Heading (Year)

WF WNF DF DNF

8-inch

PCC

11-inch

PCC

8-inch

PCC

11-inch

PCC

8-inch

PCC

11-inch

PCC

8-inch

PCC

11-inch

PCC

D ND D ND D ND D ND D ND D ND D ND D ND

WF

8

D * –4 0 0 0 –1 0 0 0 0 0 0 0 0 0 –2

ND 4 * 4 4 4 3 4 4 4 4 4 4 4 4 4 2

11 D 0 –4 * 0 0 –1 0 0 0 0 0 0 0 0 0 –2

ND 0 –4 0 * 0 –1 0 0 0 0 0 0 0 0 0 –2

WNF

8

D 0 –4 0 0 * –1 0 0 0 0 0 0 0 0 0 –2

ND 1 –3 1 1 1 * 1 1 1 1 1 1 1 1 1 –1

11 D 0 –4 0 0 0 –1 * 0 0 0 0 0 0 0 0 –2

ND 0 –4 0 0 0 –1 0 * 0 0 0 0 0 0 0 –2

DF

8

D 0 –4 0 0 0 –1 0 0 * 0 0 0 0 0 0 –2

ND 0 –4 0 0 0 –1 0 0 0 * 0 0 0 0 0 –2

11 D 0 –4 0 0 0 –1 0 0 0 0 * 0 0 0 0 –2

ND 0 –4 0 2 0 –1 0 0 0 0 0 * 0 0 0 –2

DNF

8

D 0 –4 0 0 0 –1 0 0 0 0 0 0 * 0 0 –2

ND 0 –4 0 0 0 –1 0 0 0 0 0 0 0 * 0 –2

11 D 0 –4 0 0 0 –1 0 0 0 0 0 0 0 0 * –2

ND 2 –2 2 2 2 1 2 2 2 2 2 2 2 2 2 *

* Indicates the diagonal of the matrix where the top and the side headings are the same.

1 inch = 25.4 mm.

D = drainable base.

ND = undrainable base.
187

Table 80. Summary of the results of analyses of the impacts of design factors on RFP based on IRI of LTPP SPS-2 test sections 

with slab width 12 ft (3.66 m) and concrete flexural strength of 899 lbf/inch2(6.2 MPa).

Climatic 

Region

PCC Slab 

Thickness 

(inches)

Base 

Type

Differences Between RFP of the Top Heading and RFP of the Side Heading (Year)

WF WNF DF DNF

8-inch

PCC

11-inch

PCC

8-inch

PCC

11-inch

PCC

8-inch

PCC

11-inch

PCC

8-inch

PCC

11-inch

PCC

D ND D ND D ND D ND D ND D ND D ND D ND

WF

8

D * –4 0 0 0 –2 0 0 NC 0 NC 0 0 0 0 0

ND 4 * 4 4 4 2 4 4 NC 4 NC 4 4 4 4 4

11 D 0 –4 * 0 0 –2 0 0 NC 0 NC 0 0 0 0 0

ND 0 –4 0 * 0 –2 0 0 NC 0 NC 0 0 0 0 0

WNF

8

D 0 –4 0 0 * –2 0 0 NC 0 NC 0 0 0 0 0

ND 2 –2 2 2 2 * 2 2 NC 2 NC 2 2 2 2 2

11 D 0 –4 0 0 0 –2 * 0 NC 0 NC 0 0 0 0 0

ND 0 –4 0 0 0 –2 0 * NC 0 NC 0 0 0 0 0

DF

8

D NC NC NC 0 NC NC NC NC * NC NC NC NC NC NC NC

ND 0 –4 0 0 0 –2 0 0 NC * NC 0 0 0 0 0

11 D NC NC NC 0 NC NC NC NC NC NC * NC NC NC NC NC

ND 0 –4 0 0 0 –2 0 0 NC 0 NC * 0 0 0 0

DNF

8

D 0 –4 0 0 0 –2 0 0 NC 0 NC 0 * 0 0 0

ND 0 –4 0 0 0 –2 0 0 NC 0 NC 0 0 * 0 0

11 D 0 –4 0 0 0 –2 0 0 NC 0 NC 0 0 0 * 0

ND 0 –4 0 0 0 –2 0 0 NC 0 NC 0 0 0 0 *

* Indicates the diagonal of the matrix where the top and the side headings are the same.

1 inch = 25.4 mm.

D = Drainable base.

ND = Undrainable base.

NC = Could not be compared.
188

Table 81. Summary of the results of analyses of the impacts of design factors on RFP based on IRI of LTPP SPS-2 test sections 

with slab width 14 ft (4.27 m) and concrete flexural strength of 899 lbf/inch2(6.2 MPa).

Climatic 

Region

PCC Slab 

Thickness 

(inches)

Base 

Type

Differences Between RFP of the Top Heading and RFP of the Side Heading (Year)

WF WNF DF DNF

8-inch

PCC

11-inch

PCC

8-inch

PCC

11-inch

PCC

8-inch

PCC

11-inch

PCC

8-inch

PCC

11-inch

PCC

D ND D ND D ND D ND D ND D ND D ND D ND

WF

8

D * 0 0 0 NC 0 NC 0 0 0 NC 0 0 0 0 0

ND 0 * 0 0 NC 0 NC 0 0 0 NC 0 0 0 0 0

11 D 0 0 * 0 NC 0 NC 0 0 0 NC 0 0 0 0 0

ND 0 0 0 * NC 0 NC 0 0 0 NC 0 0 0 0 0

WNF

8

D NC NC NC NC * NC NC NC NC NC NC NC NC NC NC NC

ND 0 0 0 0 NC * NC 0 0 0 NC 0 0 0 0 0

11 D NC NC NC NC NC NC * NC NC NC NC NC NC NC NC NC

ND 0 0 0 0 NC 0 NC * 0 0 NC 0 0 0 0 0

DF

8

D 0 0 0 0 NC 0 NC 0 * 0 NC 0 0 0 0 0

ND 0 0 0 0 NC 0 NC 0 0 * NC 0 0 0 0 0

11 D NC NC NC 0 NC NC NC NC NC NC * NC NC NC NC NC

ND 0 0 0 0 NC 0 NC 0 0 0 NC * 0 0 0 0

DNF

8

D 0 0 0 0 NC 0 NC 0 0 0 NC 0 * 0 0 0

ND 0 0 0 0 NC 0 NC 0 0 0 NC 0 0 * 0 0

11 D 0 0 0 0 NC 0 NC 0 0 0 NC 0 0 0 * 0

ND 0 0 0 0 NC 0 NC 0 0 0 NC 0 0 0 0 *

* Indicates the diagonal of the matrix where the top and the side headings are the same.

1 inch = 25.4 mm.

D = Drainable base.

ND = Undrainable base.

NC = Could not be compared.
189

Table 82. Summary of the results of analyses of the impacts of design factors on RSP based on longitudinal cracking of LTPP 

SPS-2 test sections with slab width 12 ft (3.66 m) and concrete flexural strength of 551 lbf/inch2(3.8 MPa).

Climatic 

Region

PCC Slab 

Thickness 

(inches)

Base 

Type

Differences Between RFP of the Top Heading and RFP of the Side Heading (Year)

WF WNF DF DNF

8-inch

PCC

11-inch

PCC

8-inch

PCC

11-inch

PCC

8-inch

PCC

11-inch

PCC

8-inch

PCC

11-inch

PCC

D ND D ND D ND D ND D ND D ND D ND D ND

WF

8

D * –2 0 0 0 –1 0 0 0 –3 0 0 0 –7 0 0

ND 2 * 2 2 2 1 2 2 2 –1 2 2 2 –5 2 2

11 D 0 –2 * 0 0 –1 0 0 0 –3 0 0 0 –7 0 0

ND 0 –2 0 * 0 –1 0 0 0 –3 0 0 0 –7 0 0

WNF

8

D 0 –2 0 0 * –1 0 0 0 –3 0 0 0 –7 0 0

ND 1 –1 1 1 1 * 1 1 0 –2 1 1 1 –6 1 1

11 D 0 –2 0 0 0 –1 * 0 0 –3 0 0 0 –7 0 0

ND 0 –2 0 0 0 –1 0 * 0 –3 0 0 0 –7 0 0

DF

8

D 0 –2 0 0 0 0 0 0 * –3 0 0 0 –6 0 0

ND 3 1 3 7 3 2 3 3 3 * 3 3 3 –4 3 3

11 D 0 –2 0 0 0 –1 0 0 0 –3 * 0 0 –7 0 0

ND 0 –2 0 0 0 –1 0 0 0 –3 0 * 0 –7 0 0

DNF

8

D 0 –2 0 0 0 –1 0 0 0 –3 0 0 * –7 0 0

ND 7 5 7 7 7 6 7 7 6 4 7 7 7 * 7 7

11 D 0 –2 0 0 0 –1 0 0 0 –3 0 0 0 –7 * 0

ND 0 –2 0 0 0 –1 0 0 0 –3 0 0 0 –7 0 *

* Indicates the diagonal of the matrix where the top and the side headings are the same.

1 inch = 25.4 mm.

D = Drainable base.

ND = Undrainable base.
190

Table 83. Summary of the results of analyses of the impacts of design factors on RSP based on longitudinal cracking of LTPP 

SPS-2 test sections with slab width 14 ft (4.27 m) and concrete flexural strength of 551 lbf/inch2(3.8 MPa).

Climatic 

Region

PCC Slab 

Thickness 

(inches)

Base 

Type

Differences Between RFP of the Top Heading and RFP of the Side Heading (Year)

WF WNF DF DNF

8-inch

PCC

11-inch

PCC

8-inch

PCC

11-inch

PCC

8-inch

PCC

11-inch

PCC

8-inch

PCC

11-inch

PCC

D ND D ND D ND D ND D ND D ND D ND D ND

WF

8

D * –3 0 0 0 –7 0 0 0 0 0 –3 0 0 0 0

ND 3 * 3 3 3 –4 3 3 3 3 3 1 3 3 3 3

11 D 0 –3 * 0 0 –7 0 0 0 0 0 –3 0 0 0 0

ND 0 –3 0 * 0 –7 0 0 0 0 0 –3 0 0 0 0

WNF

8

D 0 –3 0 0 * –7 0 0 0 0 0 –3 0 0 0 0

ND 7 4 7 7 7 * 7 7 7 7 7 5 7 7 7 7

11 D 0 –3 0 0 0 –7 * 0 0 0 0 –3 0 0 0 0

ND 0 –3 0 0 0 –7 0 * 0 0 0 –3 0 0 0 0

DF

8

D 0 –3 0 0 0 –7 0 0 * 0 0 –3 0 0 0 0

ND 0 –3 0 0 0 –7 0 0 0 * 0 –3 0 0 0 0

11 D 0 –3 0 0 0 –7 0 0 0 0 * –3 0 0 0 0

ND 3 –1 3 0 3 –5 3 3 3 3 3 * 3 3 3 3

DNF

8

D 0 –3 0 0 0 –7 0 0 0 0 0 –3 * 0 0 0

ND 0 –3 0 0 0 –7 0 0 0 0 0 –3 0 * 0 0

11 D 0 –3 0 0 0 –7 0 0 0 0 0 –3 0 0 * 0

ND 0 –3 0 0 0 –7 0 0 0 0 0 –3 0 0 0 *

* Indicates the diagonal of the matrix where the top and the side headings are the same.

1 inch = 25.4 mm.

D = Drainable base.

ND = Undrainable base.
191

Table 84. Summary of the results of analyses of the impacts of design factors on RSP based on longitudinal cracking of LTPP 

SPS-2 test sections with slab width 12 ft (3.66 m) and concrete flexural strength of 899 lbf/inch2(6.2 MPa).

Climatic 

Region

PCC Slab 

Thickness

(inches)

Base 

Type

Differences Between RFP of the Top Heading and RFP of the Side Heading (Year)

WF WNF DF DNF

8-inch

PCC

11-inch

PCC

8-inch

PCC

11-inch

PCC

8-inch

PCC

11-inch

PCC

8-inch

PCC

11-inch

PCC

D ND D ND D ND D ND D ND D ND D ND D ND

WF

8

D * –2 0 –2 0 –3 0 0 NC 0 0 0 0 0 0 0

ND 2 * 2 –1 2 –1 2 2 NC 2 2 2 2 2 2 2

11 D 0 –2 * –2 0 –3 0 0 NC 0 0 0 0 0 0 0

ND 2 1 2 * 2 –1 2 2 NC 2 2 2 2 2 2 2

WNF

8

D 0 –2 0 –2 * –3 0 0 NC 0 0 0 0 0 0 0

ND 3 1 3 1 3 * 3 3 NC 3 3 3 3 3 3 3

11 D 0 –2 0 –2 0 –3 * 0 NC 0 0 0 0 0 0 0

ND 0 –2 0 –2 0 –3 0 * NC 0 0 0 0 0 0 0

DF

8

D NC NC NC –2 NC NC NC NC * NC NC NC NC NC NC NC

ND 0 –2 0 –2 0 –3 0 0 NC * 0 0 0 0 0 0

11 D 0 –2 0 –2 0 –3 0 0 NC 0 * 0 0 0 0 0

ND 0 –2 0 –2 0 –3 0 0 NC 0 0 * 0 0 0 0

DNF

8

D 0 –2 0 –2 0 –3 0 0 NC 0 0 0 * 0 0 0

ND 0 –2 0 –2 0 –3 0 0 NC 0 0 0 0 * 0 0

11 D 0 –2 0 –2 0 –3 0 0 NC 0 0 0 0 0 * 0

ND 0 –2 0 –2 0 –3 0 0 NC 0 0 0 0 0 0 *

* Indicates the diagonal of the matrix where the top and the side headings are the same.

1 inch = 25.4 mm.

D = Drainable base.

ND = Undrainable base.

NC = Could not be compared.
192

Table 85. Summary of the results of analyses of the impacts of design factors on RSP based on longitudinal cracking of LTPP 

SPS-2 test sections with slab width 14 ft (4.27 m) and concrete flexural strength of 899 lbf/inch2(6.2 MPa).

Climatic 

Region

PCC Slab 

Thickness

(inches)

Base 

Type

Differences Between RFP of the Top Heading and RFP of the Side Heading (Year)

WF WNF DF DNF

8-inch

PCC

11-inch

PCC

8-inch

PCC

11-inch

PCC

8-inch

PCC

11-inch

PCC

8-inch

PCC

11-inch

PCC

D ND D ND D ND D ND D ND D ND D ND D ND

WF

8

D * 0 0 0 NC 0 NC 0 –1 0 0 –3 0 0 0 0

ND 0 * 0 0 NC 0 NC 0 –1 0 0 –3 0 0 0 0

11 D 0 0 * 0 NC 0 NC 0 –1 0 0 –3 0 0 0 0

ND 0 0 0 * NC 0 NC 0 –1 0 0 –3 0 0 0 0

WNF

8

D NC NC NC NC * NC NC NC NC NC NC NC NC NC NC NC

ND 0 0 0 0 NC * NC 0 –1 0 0 –3 0 0 0 0

11 D NC NC NC NC NC NC * NC NC NC NC NC NC NC NC NC

ND 0 0 0 0 NC 0 NC * –1 0 0 –3 0 0 0 0

DF

8

D 1 1 1 0 NC 1 NC 1 * 1 1 –2 1 1 1 1

ND 0 0 0 0 NC 0 NC 0 –1 * 0 –3 0 0 0 0

11 D 0 0 0 0 NC 0 NC 0 –1 0 * –3 0 0 0 0

ND 3 3 3 0 NC 3 NC 3 2 3 3 * 3 3 3 3

DNF

8

D 0 0 0 0 NC 0 NC 0 –1 0 0 –3 * 0 0 0

ND 0 0 0 0 NC 0 NC 0 –1 0 0 –3 0 * 0 0

11 D 0 0 0 0 NC 0 NC 0 –1 0 0 –3 0 0 * 0

ND 0 0 0 0 NC 0 NC 0 –1 0 0 –3 0 0 0 *

* Indicates the diagonal of the matrix where the top and the side headings are the same.

1 inch = 25.4 mm.

D = Drainable base.

ND = Undrainable base.

NC = Could not be compared.
193

Table 86. Summary of the results of analyses of the impacts of design factors on RSP based on transverse cracking of LTPP 

SPS-2 test sections with slab width 12 ft (3.66 m) and concrete flexural strength of 551 lbf/inch2(3.8 MPa). 

Climatic 

Region

PCC Slab 

Thickness

(inches)

Base 

Type

Differences between RFP of the Top Heading and RFP of the Side Heading 

(Year)

WF WNF DF DNF

8-inch

PCC

11-inch

PCC

8-inch

PCC

11-inch

PCC

8-inch

PCC

11-inch

PCC

8-inch

PCC

11-inch

PCC

D ND D ND D ND D ND D ND D ND D ND D ND

WF

8

D * –6 1 –1 1 –4 1 1 1 1 1 1 1 –12 1 1

ND 6 * 6 5 6 2 6 6 6 6 6 6 6 –6 6 6

11 D –1 –6 * –2 0 –4 0 0 0 0 0 0 0 –12 0 0

ND 1 –5 2 * 2 –3 2 2 2 2 2 2 2 –11 2 2

WNF

8

D –1 –6 0 –2 * –4 0 0 0 0 0 0 0 –12 0 0

ND 4 –2 4 3 4 * 4 4 4 4 4 4 4 –8 4 4

11 D –1 –6 0 –2 0 –4 * 0 0 0 0 0 0 –12 0 0

ND –1 –6 0 –2 0 –4 0 * 0 0 0 0 0 –12 0 0

DF

8

D –1 –6 0 –2 0 –4 0 0 * 0 0 0 0 –12 0 0

ND –1 –6 0 11 0 –4 0 0 0 * 0 0 0 –12 0 0

11 D –1 –6 0 –2 0 –4 0 0 0 0 * 0 0 –12 0 0

ND –1 –6 0 –2 0 –4 0 0 0 0 0 * 0 –12 0 0

DNF

8

D –1 –6 0 –2 0 –4 0 0 0 0 0 0 * –12 0 0

ND 12 6 12 11 12 8 12 12 12 12 12 12 12 * 12 12

11 D –1 –6 0 –2 0 –4 0 0 0 0 0 0 0 –12 * 0

ND –1 –6 0 –2 0 –4 0 0 0 0 0 0 0 –12 0 *

* Indicates the diagonal of the matrix where the top and the side headings are the same.

1 inch = 25.4 mm.

D = Drainable base.

ND = Undrainable base.
194

Table 87. Summary of the results of analyses of the impacts of design factors on RSP based on transverse cracking of LTPP 

SPS-2 test sections with slab width 14 ft (4.27 m) and concrete flexural strength of 551 lbf/inch2(3.8 MPa). 

Climatic 

Region

PCC Slab 

Thickness

(inches)

Base 

Type

Differences between RFP of the Top Heading and RFP of the Side Heading (Year)

WF WNF DF DNF

8-inch

PCC

11-inch

PCC

8-inch

PCC

11-inch

PCC

8-inch

PCC

11-inch

PCC

8-inch

PCC

11-inch

PCC

D ND D ND D ND D ND D ND D ND D ND D ND

WF

8

D * –2 0 0 0 –5 0 0 0 0 0 0 0 –1 0 –1

ND 2 * 2 2 2 –3 2 2 2 2 2 2 2 0 2 0

11 D 0 –2 * 0 0 –5 0 0 0 0 0 0 0 –1 0 –1

ND 0 –2 0 * 0 –5 0 0 0 0 0 0 0 –1 0 –1

WNF

8

D 0 –2 0 0 * –5 0 0 0 0 0 0 0 –1 0 –1

ND 5 3 5 5 5 * 5 5 5 5 5 5 5 3 5 3

11 D 0 –2 0 0 0 –5 * 0 0 0 0 0 0 –1 0 –1

ND 0 –2 0 0 0 –5 0 * 0 0 0 0 0 –1 0 –1

DF

8

D 0 –2 0 0 0 –5 0 0 * 0 0 0 0 –1 0 –1

ND 0 –2 0 1 0 –5 0 0 0 * 0 0 0 –1 0 –1

11 D 0 –2 0 0 0 –5 0 0 0 0 * 0 0 –1 0 –1

ND 0 –2 0 1 0 –5 0 0 0 0 0 * 0 –1 0 –1

DNF

8

D 0 –2 0 0 0 –5 0 0 0 0 0 0 * –1 0 –1

ND 1 0 1 1 1 –3 1 1 1 1 1 1 1 * 1 0

11 D 0 –2 0 0 0 –5 0 0 0 0 0 0 0 –1 * –1

ND 1 0 1 1 1 –3 1 1 1 1 1 1 1 0 1 *

* Indicates the diagonal of the matrix where the top and the side headings are the same.

1 inch = 25.4 mm.

D = Drainable base.

ND = Undrainable base.
195

Table 88. Summary of the results of analyses of the impacts of design factors on RSP based on transverse cracking of LTPP 

SPS-2 test sections with slab width 12 ft (3.66 m) and concrete flexural strength of 899 lbf/inch2(6.2 MPa). 

Climatic 

Region

PCC Slab 

Thickness

(inches)

Base 

Type

Differences Between RFP of the Top Heading and RFP of the Side Heading (Year)

WF WNF DF DNF

8-inch

PCC

11-inch

PCC

8-inch

PCC

11-inch

PCC

8-inch

PCC

11-inch

PCC

8-inch

PCC

11-inch

PCC

D ND D ND D ND D ND D ND D ND D ND D ND

WF

8

D * –1 –2 –5 1 –5 1 1 NC 1 1 1 1 1 1 1

ND 1 * 0 –3 2 –4 2 2 NC 2 2 2 2 2 2 2

11 D 2 0 * –3 3 –4 3 3 NC 3 3 3 3 3 3 3

ND 5 3 3 * 6 –1 6 6 NC 6 6 6 6 6 6 6

WNF

8

D –1 –2 –3 –6 * –6 0 0 NC 0 0 0 0 0 0 0

ND 5 4 4 1 6 * 6 6 NC 6 6 6 6 6 6 6

11 D –1 –2 –3 –6 0 –6 * 0 NC 0 0 0 0 0 0 0

ND –1 –2 –3 –6 0 –6 0 * NC 0 0 0 0 0 0 0

DF

8

D NC NC NC –6 NC NC NC NC * NC NC NC NC NC NC NC

ND –1 –2 –3 –6 0 –6 0 0 NC * 0 0 0 0 0 0

11 D –1 –2 –3 –6 0 –6 0 0 NC 0 * 0 0 0 0 0

ND –1 –2 –3 –6 0 –6 0 0 NC 0 0 * 0 0 0 0

DNF

8

D –1 –2 –3 –6 0 –6 0 0 NC 0 0 0 * 0 0 0

ND –1 –2 –3 –6 0 –6 0 0 NC 0 0 0 0 * 0 0

11 D –1 –2 –3 –6 0 –6 0 0 NC 0 0 0 0 0 * 0

ND –1 –2 –3 –6 0 –6 0 0 NC 0 0 0 0 0 0 *

* Indicates the diagonal of the matrix where the top and the side headings are the same.

1 inch = 25.4 mm.

D = Drainable base.

ND = Undrainable base.

NC = Could not be compared.
196

Table 89. Summary of the results of analyses of the impacts of design factors on RSP based on transverse cracking of LTPP 

SPS-2 test sections with slab width 14 ft (4.27 m) and concrete flexural strength of 899 lbf/inch2(6.2 MPa). 

Climatic 

Region

PCC Slab 

Thickness

(inches)

Base 

Type

Differences Between RFP of the Top Heading and RFP of the Side Heading (Year)

WF WNF DF DNF

8-inch

PCC

11-inch

PCC

8-inch

PCC

11-inch

PCC

8-inch

PCC

11-inch

PCC

8-inch

PCC

11-inch

PCC

D ND D ND D ND D ND D ND D ND D ND D ND

WF

8

D * 2 2 2 NC 2 NC 2 5 2 2 0 2 3 2 2

ND 2 * 4 4 NC 4 NC 4 3 4 4 2 4 1 4 4

11 D –2 4 * 0 NC 0 NC 0 7 0 0 2 0 5 0 0

ND –2 4 0 * NC 0 NC 0 7 0 0 2 0 5 0 0

WNF

8

D

N

C

NC NC NC * NC NC NC NC NC NC NC NC NC NC NC

ND –2 4 0 0 NC * NC 0 7 0 0 2 0 5 0 0

11 D

N

C

NC NC NC NC NC * NC NC NC NC NC NC NC NC NC

ND –2 4 0 0 NC 0 NC * 7 0 0 2 0 5 0 0

DF

8

D 5 3 7 0 NC 7 NC 7 * 7 7 5 7 2 7 7

ND –2 4 0 5 NC 0 NC 0 7 * 0 2 0 5 0 0

11 D –2 4 0 0 NC 0 NC 0 7 0 * 2 0 5 0 0

ND 0 2 2 0 NC 2 NC 2 5 2 2 * 2 3 2 2

DNF

8

D –2 4 0 0 NC 0 NC 0 7 0 0 2 * 5 0 0

ND 3 1 5 5 NC 5 NC 5 2 5 5 3 5 * 5 5

11 D –2 4 0 0 NC 0 NC 0 7 0 0 2 0 5 * 0

ND –2 4 0 0 NC 0 NC 0 7 0 0 2 0 5 0 *

* Indicates the diagonal of the matrix where the top and the side headings are the same.

1 inch = 25.4 mm.

D = Drainable base.

ND = Undrainable base.

NC = Could not be compared.
197

The following list describes the RFP of the SPS-2 test sections having 11-inch (279-mm)-thick 

slab and undrainable bases in the WF region:

• Two years less than the RFP of test sections located in the WF region and having 8-inch 

(203-mm)-thick slab and drainable bases.

• An insignificant 1 year less than the RFP of test sections located in the WF region and 

having 8-inch-thick slab and undrainable bases. 

In the DNF region, the RFP of the SPS-2 test sections having 8-inch (203-mm)-thick slab and 

undrainable bases is 3 years less than the RFP of test sections located in the WF region and 

having 8-inch (203-mm)-thick slab and either drainable or undrainable bases.

It should be noted that the results listed in table 78 to table 89 are further summarized in table 90

based on the relative performance of comparable SPS-2 test sections. In this context, the term 

comparable implies SPS-2 test sections having the same slab thickness and slab width, the same 

concrete flexural strength, and similar bases. The summarized data in table 90 address the impact 

of the climatic regions on pavement performance in terms of functional condition (RFP based on 

IRI) and structural condition (RSP based on longitudinal and transverse cracking). The values in 

the table indicate the percent of the test sections, having the heading parameters that performed 

better, the same, or worse than the test sections having the side heading parameters. In the 

following sections, these values are presented and discussed for each pavement condition and 

distress type.
198

Table 90. Summary of the results of the analyses of the effects of climatic region on the performance of the LTPP SPS-2 test 

sections.

Condition/ 

Distress 

Type

Climatic 

Region

Percent of SPS-2 Test Sections Located in One Climatic Region That Performed Better, the Same, or 

Worse Than Compatible Test Sections Located in Other Regions

WF WNF DF DNF

Better Same Worse Better Same Worse Better Same Worse Better Same Worse

IRI

WF — — — 26 70 4 32 68 0 26 65 9

WNF 4 70 26 — — — 10 90 0 9 82 9

DF 0 68 32 0 90 10 — — — 0 90 10

DNF 9 65 26 9 82 9 10 90 0 — — —

Longitudinal 

Cracking

WF — — — 13 78 9 14 68 18 17 79 4

WNF 9 78 13 — — — 14 72 14 78 18 4

DF 18 68 14 14 72 14 — — — 18 77 5

DNF 4 79 17 4 18 78 5 77 18 — — —

Transverse 

Cracking

WF — — — 39 44 17 48 43 9 39 35 26

WNF 17 44 39 — — — 18 77 5 13 64 23

DF 9 43 48 5 77 18 — — — 9 64 27

DNF 26 35 39 23 64 13 27 64 9 — — —

— Indicates no data.
199

IRI

The data listed in the IRI rows in table 90 indicate that the pavement performance based on IRI, 

of the majority of the SPS-2 test sections, was not affected by the climatic regions. The various 

findings leading to this conclusion are detailed as follows:

• In the WF region, 70, 68, and 65 percent of the SPS-2 test sections performed the same as 

comparable test sections located in the WNF, DF, and DNF regions, respectively, while 

26, 32, and 26 percent performed worse.

• In the WNF region, 90 and 82 percent of the SPS-2 test sections performed the same as 

comparable test sections located in the DF and DNF regions, respectively, while only 

10 and 9 percent performed worse.

• In the DF region, 90 percent of the SPS-2 test section performed the same as comparable 

test sections located in the DNF region, and only 10 percent performed better.

Longitudinal Cracking

The data listed in the longitudinal cracking rows in table 90 indicate the following:

• In the WF region, 78, 68, and 79 percent of the SPS-2 test sections performed the same as 

comparable test sections located in the WNF, DF, and DNF regions, respectively, while 

13, 14, and 17 percent performed worse, and 9, 18, and 4 percent performed better. In the 

WNF region, 72 and 18 percent of the SPS-2 test sections performed the same as 

comparable test sections located in the DF and DNF regions, respectively, while 14 and 

78 percent performed worse, and 14 and 4 percent performed better.

• In the DF region, 77 percent of the SPS-2 test section performed the same as comparable 

test sections located in the DNF region, and 18 percent performed worse.

Transverse Cracking

The data listed in the transverse cracking block of table 90 indicate the following: 

• In the WF region, 44, 43, and 35 percent of the SPS-2 test sections performed the same as 

comparable test sections located in the WNF, DF, and DNF regions, respectively, while 

17, 9, and 26 percent performed better, and 39, 48, and 39 percent performed worse. 

• In the WNF region, the majority (77 and 64 percent) of the SPS-2 test sections performed 

the same as comparable test sections located in the DF and DNF regions, respectively,

while few percentages performed either better or worse.

• Likewise, in the DF region, the majority of the SPS-2 test sections performed the same as 

comparable test sections located in the DNF region, while 27 percent performed better,

and 9 percent performed worse. 
200

Summary, Conclusions, and Recommendations for LTPP SPS-2

The available data in the LTPP database Standard Data Release 28.0 regarding the LTPP SPS-2 

experiment were downloaded, organized, and analyzed. The intent was to study the impact of 

each design variable on pavement performance. When the data were divided into various groups 

based on separation of variables, the number of test sections under each variable was statistically 

insignificant. However, for each test section, the resulting RFPs and RSPs are listed in table 78

through table 89. Because of the limited number of SPS-2 test sections under each variable, the 

impact of the design variables on pavement performance was not analyzed or discussed any 

further. Rather, the data were summarized in table 90, and the impacts of the climatic region on 

pavement performance were presented after that table. Based on the analyses results, the 

following conclusions were drawn:

• On average, the pavement performance in terms of IRI was not affected by the climatic 

region, although the data indicated that SPS-2 test sections located in the WF region 

performed slightly worse than compatible test sections located in the other three 

climatic regions.

• On average, the majority of the SPS-2 test sections located in the WNF region performed 

worse in terms of longitudinal cracking than those in the DNF region. This was mainly 

due to the impact of excessive moisture on pavement performance.

• The WF region had a more damaging impact on pavement performance in terms of

transverse cracking than on those in the WNF, DF, and DNF regions. This was expected,

owing to the combined effects of subfreezing temperatures and moisture.

IMPACTS OF MAINTENANCE TREATMENTS ON PAVEMENT CONDITION AND 

DISTRESS USING THE LTPP SPS-4 TEST SECTIONS

The main objective of the LTPP SPS-4 experiment was to compare the performance of rigid 

pavement test sections subjected to selected maintenance treatments to the performance of 

untreated test sections or the control sections. The 34 SPS-4 test sites were initiated between 

1990 and 1995 and are distributed across the United States and Canada. Each of the SPS-4 test 

sites consisted of three test sections. One each of two sections at each site was subjected to one 

or the other of the following two treatments:

• Joint and crack sealing (410).

• Joint undersealing (420).

(Note that the numbers in parentheses are the LTPP designation of the treatment. For example, 

the designation of the joint and crack sealing is 410.) The third section was counted as a control 

section that was not treated in accordance with the original experimental design. However, only 

10 of the 34 test sites contained a test section that was joint undersealed, bringing the total 

number of test sections and control sections to 78.

Several variables affect the performance of the treated pavement sections. These include climatic 

region, traffic, subgrade type, etc. Similar to the SPS-3 experiment, unfortunately, in some 
201

scenarios, if these variables were separated, the number of test sections available for analyses 

became insignificant. To illustrate, table 91 lists the number of test sections available for 

analyses based on the separation of the following variables:

• Two treatment types.

• One pavement condition (IRI).

• Two pavement distress types (longitudinal and transverse cracking).

• Four climatic regions (WF, WNF, DF, and DNF).

• Three traffic levels.
202

Table 91. Number of test sections that have after treatment pavement condition and distress and traffic data.

Condition or Distress 

Type Treatment Type

Number of Test Sections Subjected to Each of Three Traffic 

Levels in the Various Climatic Regions

WF WNF DF DNF

L M H L M H L M H L M H

IRI

Joint and crack sealing 0 0 7 1 3 6 0 0 2 0 0 2

Joint undersealing 0 0 0 0 3 3 0 0 1 0 0 1

Control section 0 0 7 1 3 7 0 0 1 0 0 2

Longitudinal cracking

Joint and crack sealing 0 0 0 0 2 3 0 0 0 0 0 0

Joint undersealing 0 0 0 0 1 1 0 0 0 0 0 0

Control section 0 0 1 0 2 0 0 0 1 0 0 2

Transverse cracking

Joint and crack sealing 0 0 1 0 1 3 0 0 1 0 0 0

Joint undersealing 0 0 0 0 0 2 0 0 0 0 0 0

Control section 0 0 2 0 1 2 0 0 1 0 0 1

Note: For each pavement condition and distress type, a test section was analyzed only if it exhibited any condition or distress and had three or 

more data points after treatment that could be modeled.

L = Low traffic (0 to 60,000 ESAL/year).

M = Medium traffic (61,000 to 120,000 ESAL/year).

H = High traffic (> 120,000 ESAL/year).
203

It can be seen that for longitudinal and transverse cracking, the number of SPS-4 test sections 

that were available for analyses was statistically insignificant in all climatic regions. Therefore, 

the analyses were conducted to assess the impact of each treatment type in each climatic region 

and for each pavement condition and distress type. That is, the data were not separated based on 

traffic level or by the type of subbase or subgrade. Nevertheless, the analyses of the impacts of 

each of the two treatment types on pavement performance were accomplished using the 

following steps:

• Step 1: For each treated pavement test section in the SPS-4 experiment, each of the 

available pavement condition (IRI) and distress data were used to calculate the RFP and 

RSP of that section from the time of the treatment to the time when the pavement 

condition or distress reach the prespecified threshold values.

• Step 2: For each pavement condition and distress type and for each pavement treatment 

type, the minimum and maximum RFPs and RSPs and their averages for all test sections 

located in the same climatic region were calculated and are listed in table 92 through 

table 94 depending on the pavement condition and distress type.

Results of the analyses are discussed per pavement condition and distress type in the three subsections 

following table 92 through table 94.
204

Table 92. Impacts of various maintenance treatments and control section on pavement performance in terms of RFP based 

on IRI.

Climatic 

Region

Treatment 

Type

RFP (Year)

Difference in 

RFP (Year)

Test Sections Control Sections

Number of 

Test 

Sections Min Max Avg

Number of 

Test 

Sections Min Max Avg

WF

Joint crack 

sealing

8 10 20 17 8 8 20 17 0

WNF 10 6 20 18 11 0 20 15 3

DF 4 1 16 9 2 11 13 12 3

DNF 2 5 20 12 2 17 17 17 4

WF

Joint 

undersealing

0 — — — 8 8 20 17 NC

WNF 6 0 20 12 11 0 20 15 3

DF 1 3 3 3 2 11 13 12 10

DNF 1 2 2 2 2 17 17 17 14

—Indicates no data.

Min = Minimum.

Max = Maximum.

Avg = Average.

NC = Could not be compared.
205

Table 93. Impacts of various maintenance treatments and control section on pavement performance in terms of RSP based on 

longitudinal cracking.

Climatic 

Region

Treatment 

Type

RFP (Year)

Difference in 

RSP (Year)

Test Sections Control Sections

Number of 

Test 

Sections Min Max Avg

Number of 

Test 

Sections Min Max Avg

WF

Joint crack 

sealing

0 — — — 1 20 20 20 NC

WNF 5 13 20 19 2 20 20 20 1

DF 0 — — — 1 20 20 20 NC

DNF 0 — — — 2 17 20 19 NC

WF

Joint 

undersealing

0 — — — 1 20 20 20 NC

WNF 2 20 20 20 2 20 20 20 0

DF 0 — — — 1 20 20 20 NC

DNF 0 — — — 2 17 20 19 NC

—Indicates no data.

Min = Minimum.

Max = Maximum.

Avg = Average.

NC = Could not be compared.
206

Table 94. Impacts of various maintenance treatments and control section on pavement performance in terms of RSP based on 

transverse cracking.

Climatic 

Region

Treatment 

Type

RFP (Year)

Difference in 

RSP (Year)

Test Sections Control Sections

Number of 

Test 

Sections Min Max Avg

Number of 

Test 

Sections Min Max Avg

WF

Joint crack 

sealing

1 20 20 20 2 11 20 16 4

WNF 4 6 20 14 3 20 20 20 6

DF 1 19 19 19 1 20 20 20 1

DNF 0 — — — 1 14 14 14 NC

WF

Joint 

undersealing

0 — — — 2 11 20 16 NC

WNF 2 1 20 11 3 20 20 20 9

DF 0 — — — 1 20 20 20 NC

DNF 0 — — — 1 14 14 14 NC

—Indicates no data.

Min = Minimum.

Max = Maximum.

Avg = Average.

NC = Could not be compared.
207

IRI

The calculated minimum, maximum, and average RFPs based on IRI data for the SPS-4 test 

sections that were subjected to the same treatment type and for the associated control sections are 

listed in table 92. The data in the table indicate the following:

• Eight SPS-4 test sections in the WF region were subjected to joint and crack sealing and 

accepted for analyses. The minimum, maximum, and average RFPs of the eight SPS-4 

test sections were 10, 20, and 17 years, respectively. In addition, there were eight control 

sections with minimum, maximum, and average RFPs of 8, 20, and 17 years, 

respectively. Thus, the difference between the average RFPs of the treated test sections

and the control sections was 0 years. That is, joint crack sealing had no impact on 

pavement performance in the WF region.

• The average RFP of the 10 treated SPS-4 test sections located in the WNF region was 

3 years longer than the average RFP of the 11 control sections located in the same region.

• The average RFP of the four treated SPS-4 test sections located in the DF region was 

3 years shorter than the average RFP of the two control sections located in the same region.

• The average RFP of the two treated SPS-4 test sections located in the DNF region was 

5 years shorter than the average RFP of the two control sections located in the same region.

• The joint undersealing treatment of the SPS-4 test sections in the WNF, DF, and DNF 

regions caused greater pavement roughness, and consequently, the average RFPs of the 

control sections in the three regions were substantially shorter than the test sections.

The main reason for the differences between the average RFPs of the test sections and the control 

sections is that the conditions of the control sections were not representative of the conditions of 

the test sections when they were subjected to treatments. For example, the IRI obtained from the 

first survey performed on the treated test section 06B420 was 156 inches/mi (2.4 m/km) while 

the IRI of the control section 06B430 was 123.5 inches/mi (1.9 m/km). Because the magnitude 

and the rates of deterioration of the two test sections were different, they precipitated differences 

in their RFPs.

Longitudinal Cracking

The calculated minimum, maximum, and average RSPs based on longitudinal cracking data for 

the SPS-4 test sections that were subjected to the same treatment type and for the associated 

control sections are listed in table 93. The data in the table indicate that neither treatment had any 

impact on the average RFPs of the SPS-4 test sections located in the WNF region. No test or 

control sections are located in the other three climatic regions. 

Transverse Cracking

The calculated minimum, maximum, and average RSPs based on transverse cracking of the 

SPS-4 test sections that were subjected to the same treatment type and for the associated control 

sections are listed in table 94. The data in the table indicate the following:
208

• Joint and crack sealing had a positive impact on pavement performance in the WF region. 

The RFP of the one SPS-4 test section was 4 years longer than the average RFP of the 

two control sections, whereas, the same treatment caused losses in the average RFPs of 

the SPS-4 test sections located in the WNF and DF regions.

• The two SPS-4 test sections located in the WNF region and subjected to joint 

undersealing performed worse than the three control sections by 9 years.

Summary, Conclusions, and Recommendations for LTTP SPS-4

The available data in the LTPP database regarding the LTPP SPS-4 experiment were 

downloaded, organized, and analyzed. The intent was to study the impact of two maintenance 

treatments, joint and crack sealing (410) and joint undersealing (420), on pavement performance.

When the data were separated based on traffic levels, the number of test sections that were 

available for analyses in each traffic level was statistically insignificant. Therefore, the data were 

grouped based on the two maintenance treatment types and the four climatic regions. For each 

group, the minimum, maximum, and average RFPs and RSPs for the test and control sections 

were calculated and are listed in table 92 through table 94. The impacts of the two maintenance 

treatments in each climatic region were presented in the section following the tables. Based on 

the results of the analyses, the following conclusions were drawn:

• On average, in terms of IRI, joint and crack sealing treatment had no impact on pavement 

performance in the WF region, a positive impact in the WNF region, and a negative 

impact in the DF and DNF regions.

• On average, in terms of IRI, joint undersealing treatment had a negative impact on 

pavement performance in the WNF, DF, and DNF regions.

• On average, the two maintenance treatments had no impact on pavement performance in 

terms of longitudinal cracking in the WNF region.

• Joint and crack sealing treatment had a positive impact on pavement performance in the 

WF region and a negative impact in the WNF region.

• Joint undersealing treatment had no impact on the pavement performance in terms of

transverse cracking in the WNF region.

In summary, the research team concluded that joint and crack sealing was effective in the WF 

region and not effective in the other three climatic regions, and joint undersealing was not 

effective in any region.

IMPACTS OF REHABILITATION TREATMENTS ON PAVEMENT CONDITION AND 

DISTRESS USING THE LTPP SPS-6 TEST SECTIONS

The main objective of the SPS-6 experiment was to examine the effects of various rehabilitation 

treatments on the performance of rigid pavement test sections. The 14 SPS-6 test sites were initiated 

between 1989 and 1998 and are distributed across the United States and Canada. Each SPS-6 test 
209

site consisted of 1 control section and 7 treated test sections for a total of 112 test sections. Each of 

the seven treated sections was subjected to one of the following rehabilitation actions (note that the 

numbers in parenthesis are the LTPP designation of the rehabilitation actions):

• Minimum restoration (602).

• Minimum restoration with 4-inch (102-mm) AC overlay (603).

• Minimum restoration with 4-inch (102-mm) AC overlay and sawed and sealed joints in 

the AC (604).

• Maximum restoration (605).

• Maximum restoration with 4-inch (102-mm) AC overlay (606).

• Crack, break, and seat with 4-inch (102-mm) AC overlay (607).

• Crack, break, and seat with 8-inch (204-mm) AC overlay (608).

The minimum restoration action included limited patching, crack sealing, and joint stabilization. 

Further, diamond grinding was performed when faulting was considered too high. Maximum 

restoration included subsealing, subdrainage, joint repair and sealing, full-depth repairs and load 

transfer restoration, and diamond grinding. Cracking and seating was used for JPCP test sections 

while breaking and seating was performed for JRCP test sections.

For each SPS-6 test section subjected to one of the previously listed rehabilitation actions, the 

time-series pavement condition and distress data (collected after the rehabilitation action was 

taken and before the next treatment was applied) were used to calculate the RFPs and RSPs of 

that section. Thus, RFPs and RSPs expressed the pavement service period between rehabilitation 

and the time when the pavement condition or distress reached the prespecified threshold values. 

Similarly, the RFPs and RSPs of the control sections were also calculated. For each pavement 

condition (IRI) and distress type (rut depth and alligator, longitudinal, and transverse cracking), 

the treatment benefits were expressed in terms of the following:

• RFP or RSP of the treated pavement section.

• The difference in RFP or RSP of the treated pavement section and RFP or RSP of the 

associated control section. This difference was labeled CFP or CSP.

Results of the analyses of the treatment benefits are listed in table 95 through table 99 based on 

two climatic regions (no test sections were present in the DF and DNF regions), pavement type, 

and pavement condition and distress type. The data in the tables are discussed in the following 

subsections per pavement condition and distress type.
210

Table 95. RFP of control sections and the impact of treatment types on pavement performance in terms of RFP based on IRI.

Climatic 

Region

Pavement 

Type

State 

(Code)

Control 

Section 

RFP 

(Year)

Minimum 

Restoration 

and No AC 

Overlay

Minimum Restoration and 

AC Overlay

Maximum 

Restoration 

and No AC 

Overlay

Maximum 

Restoration 

and 4-inch

AC Overlay

Crack/Break and Seat and 

AC Overlay

4 inches

4 inches

With SS 4 inches 8 inches

RFP CFP RFP CFP RFP CFP RFP CFP RFP CFP RFP CFP RFP CFP

WF

JPCP

AZ (04) ND ND — 18 — 20 — ND — 20 — 20 — 20 —

IN (18) ND NS — 20 — 20 — NS — 20 — 14 — 20 —

MO (29) ND 20 — 20 — 10 — 16 — 15 — 20 — 20 —

SD (46) ND ND — ND — ND — ND — ND — ND — ND —

Average — 20 — 19 — 17 — 16 — 18 — 18 — 20 —

JRCP

IL (17) 7 20 13 20 13 20 13 13 6 ND — 20 13 20 13

IA (19) ND ND — ND — ND — 20 — 20 — 20 — 20 —

MI (26) 11 ND — 20 9 NS — 3 8 20 9 NS — 20 9

MO (29) ND ND — 20 — 20 — ND — 20 — ND — 20 —

PA (42) ND ND — 19 — 20 — ND — 19 — 20 — 20 —

Average 9 20 11 20 11 20 11 12 3 20 11 20 11 20 11

WNF JPCP

AL(01) 0 20 20 20 20 20 20 20 20 20 20 ND — 20 20

AR (05) ND 20 — 20 — 20 — ND — 20 — 20 — 20 —

CA (06) 0 ND — 15 15 ND — 7 7 ND — 16 16 20 20

TN (47) ND 15 — 20 — 13 — ND — 20 — ND 20 —

Average 0 18 18 19 19 18 18 14 14 20 20 18 18 20 20

JRCP OK (40) ND ND — ND — ND — ND — ND — ND — ND —

— Indicates could not be calculated.

1 inch = 25.4 mm.

SS = saw and seal of joints.

ND = no data.
211

Table 96. Impact of various treatments and control section on pavement performance in terms of RFP/RSP based on rut 

depth.

Climati

c 

Region

Paveme

nt Type

State 

(Code)

Contro

l 

Section 

RSP 

(Year)

Minimum 

Restoration 

and No AC 

Overlay

Minimum Restoration 

and AC Overlay

Maximum 

Restoration 

and No AC 

Overlay

Maximum 

Restoration 

and 4-inch

AC 

Overlay

Crack/Break and Seat 

and AC Overlay

4 inches

4 inches

With SS 4 inches 8 inches

RSP CSP RSP CSP RSP CSP RSP CSP RSP CSP RSP CSP RSP CSP

WF

JPCP

AZ (04) N/A N/A — 20 — 20 — N/A — NS — 20 — 20 —

IN (18) N/A N/A — 20 — 20 — N/A — 20 — 20 — 20 —

MO (29) N/A N/A — NS — NS — N/A — NS — NS — NS —

SD (46) N/A N/A — ND — ND — NA — ND — ND — ND —

Average — — — 20 — 20 — — — 20 — 20 — 20 —

JRCP

IL (17) N/A N/A — 20 — 20 — N/A — ND — NS — NS —

IA (19) N/A N/A — NS — 20 — N/A — NS — NS — 20 —

MI (26) N/A N/A — 20 — 20 — N/A — 20 — 20 — 20 —

MO (29) N/A N/A — 20 — 20 — N/A — 20 — ND — 20 —

PA (42) N/A N/A — 20 — 20 — N/A — 20 — 20 — 20 —

Average — — — 20 — 20 — — — 20 — 20 — 20 —

WNF JPCP

AL (01) N/A N/A — 20 — 20 — N/A — 20 — 20 — 20 —

AR (05) N/A N/A — 20 — 20 — N/A — 20 — 20 — 20 —

CA (06) N/A N/A — 20 — 20 — N/A — ND — 20 — 20 —

TN (47) N/A N/A — 20 — 20 — N/A — 20 — ND — 20 —

Average — — — 20 — 20 — — — 20 — 20 — 20 —

JRCP OK (40) N/A N/A — 20 — 20 — N/A — 20 — 20 — 20 —

— Indicates could not be calculated.

1 inch = 25.4 mm.

SS = Saw and seal of joints.

ND = No data.

N/A = Not applicable.

NS = Negative model slope (pavement condition and/or distress improving over time with no treatment).
212

Table 97. Impact of various treatments and control section on pavement performance in terms of RSP based on alligator 

cracking.

Climatic 

Region

Pavemen

t Type

State 

(Code)

Control 

Section 

RSP 

(Year)

Minimum 

Restoratio

n and No 

AC 

Overlay

Minimum 

Restoration and AC 

Overlay

Maximum 

Restoration 

and No AC 

Overlay

Maximum 

Restoration 

and 4-inch

AC Overlay

Crack/Break and 

Seat and AC 

Overlay

4 inches

4 inches

With SS 4 inches 8 inches

RSP CSP RSP CSP RSP CSP RSP CSP RSP CSP RSP CSP RSP CSP

WF

JPCP

AZ (04) N/A N/A — 13 — 20 — N/A — 19 — 19 — 20 —

IN (18) N/A N/A — 20 — 13 — N/A — 11 — 12 — 12 —

MO (29) N/A N/A —

N

D

— 15 — N/A — 19 — 19 — 20 —

SD (46) N/A

N/A

—

N

D

— ND — N/A— ND — ND — ND —

Average — — — 17 — 16 — — — 16 — 17 — 17 —

JRCP

IL (17) N/A N/A — 5 — 13 — N/A — ND — ND — 18 —

IA (19) N/A N/A —

N

D

— 8— N/A — 8— 8— 10 —

MI (26) N/A N/A — 20 — ND — N/A — 10 — ND — 20 —

MO (29) N/A N/A —

N

D

— 20 — N/A — ND — ND — 16 —

PA (42) N/A N/A — 20 — ND — N/A — ND — 20 — ND —

Average — — — 15 — 14 — — — 9 — 14 — 16 —

WNF JPCP

AL (01) N/A N/A — 9 — 15 — N/A — ND — 3 — 7 —

AR (05) N/A N/A — 18 — 16 — N/A — 18 — 20 — 20 —

CA (06) N/A N/A — 7 — ND — N/A — ND — 5 — 7 —

TN (47) N/A N/A —

N

D

— 12 — N/A — ND — ND — ND —

Average — — — 11 — 14 — — — 18 — 9 — 11 —

JRCP OK (40) N/A N/A — 20 — 20 — N/A — 20 — 5 — 9 —

— Indicates could not be calculated.

1 inch = 25.4 mm.

SS = Saw and seal of joints.
213

ND = No data.

N/A = Not applicable.
214

Table 98. Impact of various treatments and control section on pavement performance in terms of RSP based on longitudinal 

cracking.

Climatic 

Region

Pavement 

Type

State 

(Code)

Control 

Section 

RSP 

(Year)

Minimum 

Restoration 

and No AC 

Overlay

Minimum Restoration 

and AC Overlay

Maximum 

Restoration 

and No AC 

Overlay

Maximum 

Restoration 

and 4-inch

AC Overlay

Crack/Break and Seat 

and AC Overlay

4 inches

4 inches

With SS 4 inches 8 inches

RSP CSP RSP CSP RSP CSP RSP CSP RSP CSP RSP CSP RSP CSP

WF

JPCP

AZ (04) ND ND — 7 — 12 — ND — 13 — 10 — 13 —

IN (18) ND 19 — 6 — 12 — 20 — 14 — 15 — 10 —

MO (29) 20 ND — 10 10 6 14 13 7 9 11 10 10 10 10

SD (46) ND ND — ND — ND — ND — ND — ND — ND —

Average 20 19 1 8 12 10 10 17 3 12 8 12 8 11 9

JRCP

IL (17) 13 ND — 20 7 12 1 ND — ND — 20 7 17 4

IA (19) ND ND — ND — 7 — ND — 3 — 0 — 1 —

MI (26) 19 ND — 10 9 ND — NS — 10 9 9 10 9 10

MO (29) ND ND — ND — 19 — ND — 20 — ND — 20 —

PA (42) ND ND — 14 — 15 — ND — 14 — 14 — 17 —

Average 16 — — 15 1 13 3 — — 12 4 11 5 13 3

WNF JPCP

AL (01) ND 20 — 5 — 6 — ND — 5 — 7 — 20 —

AR (05) ND ND — 10 — 10 — ND — 12 — 9 — 9 —

CA (06) ND ND — 6 — 7 — ND — 7 — 5 — 5 —

TN (47) ND 20 — 6 — 5 — ND — 5 — ND — 7 —

Average — 20 — 7 — 7 — — — 7 — 7 — 10 —

JRCP OK (40) ND ND — 3 — 3 — ND — 3 — 3 — 3 —

— Indicates could not be calculated.

1 inch = 25.4 mm.

SS = Saw and seal of joints.

ND = No data.
215

Table 99. Impact of various treatments and control section on pavement performance in terms of RSP based on transverse 

cracking.

Climati

c 

Region

Paveme

nt Type

State 

(State 

Code)

Contro

l 

Section 

RSP 

(Year)

Minimum 

Restoratio

n and No 

AC 

Overlay

Minimum Restoration 

and AC Overlay

Maximum 

Restoratio

n and No 

AC 

Overlay

Maximum 

Restoratio

n and 4-

inch AC 

Overlay

Crack/Break and Seat 

and AC Overlay

4 inches

4 inches

With SS 4 inches 8 inches

RSP CSP RSP CSP RSP CSP RSP CSP RSP CSP RSP CSP RSP CSP

WF

JPCP

AZ (04) ND ND — 3 — 0 — ND — 7 — 8 — 12 —

IN (18) ND ND — 3 — 0 — ND — 3 — 3 — 16 —

MO (29) 20 20 0 12 8 0 20 20 0 9 11 11 9 10 10

SD (46) ND ND — ND — ND ND — ND — ND — ND —

Average 20 20 0 6 14 0 20 20 0 6 14 7 13 13 7

JRCP

IL (17) 5 0 5 20 15 20 15 0 5 ND — ND — 20 15

IA (19) ND ND — ND — NS ND — NS — 20 — 14 —

MI (26) 0 ND — 19 19 ND 0 0 15 15 14 14 12 12

MO (29) ND ND — ND — NS ND — 20 — ND — 20 —

PA (42) ND ND — 20 — NS ND — 20 — 20 — ND —

Average 3 0 3 20 17 20 17 0 3 18 15 18 15 17 14

WNF

JPCP

AL (01) 20 20 0 7 13 0 20 12 8 5 15 4 16 20 0

AK (05) ND ND — 8 — 0 — ND — 8 — 10 — 13 —

CA (06) ND ND — 6 — ND — ND — ND — 11 — 12 —

TN (47) ND 20 — 13 — 1 — ND — 11 — ND — ND —

Average 20 20 0 9 11 0 20 12 8 8 12 8 12 15 5

JRCP OK (40) ND ND — 12 — 17 — ND — 17 — 4 — 20 —

— Indicates could not be calculated.

1 inch = 25.4 mm.

SS = Saw and seal of joints.

ND = No data.

NS = Negative model slope (pavement condition and/or distress improving over time with no treatment).
216

IRI

The data listed in table 95 indicate the following:

• Although the RFP and CFP of some treated pavement sections are listed in the table, the 

results were based on only one test section and one control section. Hence, no substantial 

discussion and/or conclusion could be made.

• Based on the limited number of test sections in each treatment type, the following 

appeared to be true, on average:

o The maximum restoration and no AC overlay treatment type yielded the lowest RFP. 

This could be related to the treatment type or more likely the construction quality that 

yielded high pavement roughness.

o The impact of rigid pavement type and climatic region on the performance of the 

treated test sections was similar.

Rut Depth

The data listed in table 96 indicate the following:

• Although the test sections that received AC overlay has rut depth measurements available 

in the LTPP database, the PCC control sections were not subjected to AC overlay and 

hence did not have rut depth data. Therefore, no CFPs/CSPs could be calculated.

• Once again, although RFPs/RSPs of most treated pavement sections are listed in the 

table, the data in each category (each cell in the table) were based on only one test 

section. Nevertheless, the data in the table indicate that RFPs/RSPs of all treated 

pavement sections, where a minimum of three data points were collected, was 20 years,

regardless of the treatment type, pavement type, or climatic region.

Alligator Cracking

The data listed in table 97 indicate the following:

• The LTPP database had no alligator cracking data for any of the control sections. Once 

again, the reason was that the control sections were rigid pavement, while the test 

sections were composite pavements.

• The reported alligator cracking data on the test sections were highly likely top-down 

cracking. The reason was that in composite pavements, surface tensile stress and strain 

due to pavement-tire interaction was greater than the tensile stress and strain at the bottom 

of the AC overlay. Nevertheless, RSPs of most of the treated pavement sections listed in 

table 97 were based on only one test section per treatment type, pavement type, and 

climatic region. The data could not be compared with the control sections to extract 

treatment benefits because of the different pavement types. However, the differential 
217

benefits of the various treatments could be obtained by studying the minimum, maximum, 

and average values of RSP. The data in the table indicate the following:

o RSPs of the test sections subjected to minimum restoration and 4-inch (102-mm) AC 

overlay ranged from a low of 5 years to a high of 20 years, with an average of about 

14 years.

o RSPs of the test sections subjected to minimum restoration, sawing and sealing the 

joints, and 4-inch (102-mm) AC overlay ranged from a low of 5 years to a high of 

20 years, with an average of about 15 years.

o RSPs of the test sections subjected to maximum restoration and 4-inch (102-mm) AC 

overlay ranged from a low of 8 years to a high of 20 years, with an average of about 

15 years.

o RSPs of the test sections subjected to crack, break and seat, and 4-inch (102-mm) AC 

overlay ranged from a low of 5 years to a high of 20 years, with an average of about 

15 years.

o RSPs of the test sections subjected to crack, break, and seat and 8-inch (203-mm) AC 

overlay ranged from a low of 7 years to a high of 20 years, with an average of about 

15 years.

These observations indicate that the pavement performance of the five treatments was almost the 

same and independent of pavement type and climatic region.

Longitudinal Cracking

The data listed in table 98 indicate the following:

• The LTPP database contained three or more time-series data points for only three control 

sections. The other 11 sections either had fewer than three data points or the control 

section was treated before three data points were collected.

• It appeared that none of treatment types applied to JPCP and JRCP test sections located 

in the WF region were effective. The performance of the treated sections was less than 

the performance of the control sections.

• For JPCP test sections located in the WNF region, it appeared that the minimum 

restoration and no AC overlay treatment yielded the highest RSP (20 years), while the 

crack and break and seat and 8-inch AC overlay yielded an RSP of 10 years. RSPs of the 

test sections that received each of the other four treatments were 7 years.
218

Transverse Cracking

The data listed in table 99 indicate the following:

• For the JRCP test section located in Illinois (WF region), the minimum restoration and no 

AC overlay treatment appeared not to address the transverse cracking problem; the RSP 

of the treated test section is 0 years. Further, RSPs of two test sections located in Illinois 

and Michigan and subjected to maximum restoration and no AC overlay treatment were 

also 0 years. The data from this limited number of test sections suggested that neither the 

minimum nor the maximum restoration with no overlay treatments addressed transverse 

cracking problems in JRCP test sections, or the treatment construction quality was not 

adequate, or a combination thereof. On the other hand, the two treatments appeared to be 

the right treatment for the four JPCP test sections located in the WF (two sections in 

Missouri) and WNF regions (two sections in Alabama).

• The other three treatments—minimum and maximum restoration with 4-inch (102-mm)

AC overlay—appeared to be the right treatment for transverse cracking of JRCP test 

sections located in the WF region.

Summary, Conclusions, and Recommendations for LTPP SPS-6

The available data in the LTPP database Standard Data Release 28.0 regarding the LTPP SPS-6 

experiment were downloaded, organized, and analyzed. The intent was to study the impact of 

seven maintenance treatments on pavement performance. Each of the 7 test sections of each of 

the 14 test sites was subjected to certain treatments. The measured condition and distress data for 

each test site and control section were analyzed, and RFP, RSP, CFP, and CSP were calculated. 

The results were then grouped per pavement type and climatic region for further analyses. 

Unfortunately, the IRI and distress data for many control sections and for some test sections did 

not support the analyses because of either the lack of three data points or improvement in the 

pavement condition and/or distress over time without the application of treatments. 

Consequently, the results for only a few test sections could be compared. Based on the limited 

number of test and control sections, the following conclusions were drawn:

• RFP values of the treated pavement sections were independent of pavement type and 

climatic region.

• The pavement performance based on rut depth of treated test sections was independent of 

the treatment type, pavement type, and climatic region.

• The alligator cracking data in the database were highly likely an advanced form of topdown cracking (top-down cracks are fatigue cracks that initiate at the pavement surface 

and, over time, propagate downward) where the transverse and longitudinal cracks 

resembled alligator cracking.

• The performance of the test sections in terms of longitudinal cracking was worse after 

subjecting the section to any of the seven treatment types.
219

• Minimum and maximum pavement restoration with no AC overlay treatments did not 

improve the performance of the JRCP test sections.

It should be noted that each of these conclusions should be accepted with cautious because they 

were based on the results of a few and sometimes on only one test section.

IMPACTS OF BONDED CONCRETE OVERLAYS ON PAVEMENT PERFORMANCE 

USING THE LTPP SPS-7 TEST SECTIONS

The main objective of the SPS-7 experiment is to study the effects of bonded concrete overlay 

thickness, surface preparation before concrete overlay, and the use of cement grout on the 

performance of PCC pavements. Four SPS-7 test sites were initiated between 1990 and 1992. 

Three of the four sites consisted of CRCP test sections while the fourth site consisted of JPCP 

test sections. Each of the four test sites had eight test sections and one control section, except the 

test site in Louisiana, where no control section was included. The eight test sections were 

subjected to one of the following treatments (the numbers in parenthesis are the LTPP 

designation of the treatment):

• Three-inch (76-mm) concrete overlay with milling and grouting (702).

• Three-inch (76-mm) concrete overlay with milling (703).

• Three-inch (76-mm) concrete overlay with shot blasting (704).

• Three-inch (76-mm) concrete overlay with shot blasting and grouting (705).

• Five-inch (127-mm) concrete overlay with shot blasting and grouting (706).

• Five-inch (127-mm) concrete overlay with shot blasting (707).

• Five-inch (127-mm) concrete overlay with milling (708).

• Five-inch (127-mm) concrete overlay with milling and grouting (709).

For each test section that was subjected to one of these treatments, the available time-series 

pavement condition and distress data from the time of treatment to that of the next treatment 

were used to calculate the RFP and RSP of that section. Hence RFP and RSP describe the time 

period between the treatment construction and the time when the pavement condition or distress 

reached the prespecified threshold values. The RFPs and RSPs of the control sections were also 

calculated. For each pavement condition (IRI) and distress type (longitudinal and transverse 

cracking), the treatment benefits were calculated based on the following:

• The RFP and RSP of each treated test section.

• The difference in the RFP or RSP of the treated test section and the RFP or RSP of the 

associated control section. The difference was labeled CFP and CSP.

Results of the analyses are listed in table 100 through table 102 and discussed in the following 

subsections based on pavement condition and distress type. It should be noted that, for each of 

the CRCP test sections, the total transverse crack length was calculated as the sum of half of the 

cumulative length of low severity transverse cracks, the total length of medium severity cracks, 

and the total length of high severity transverse cracks. The reason was that the signature of 

CRCP is the tightly spaced transverse cracks (also called shrinkage cracks). Some of these 

transverse cracks may open up over time, connect, and produce punch-outs. After careful 
220

observations of the CRCP transverse crack data, it was observed that for most CRCP test 

sections, the total length of the low-severity transverse cracks reported in the database exceeded

the crack saturation point. Therefore, it was assumed that about half of the total length of the 

reported low-severity transverse cracks was open enough to be considered in the analyses. The 

other half were very tight shrinkage cracks.
221

Table 100. Impact of bonded concrete overlays on pavement performance in terms of RFP based on IRI.

Climatic 

Region

Existing 

Pavement 

Type

State 

(Code)

Control 

Section 

RFP 

(Year)

RFP and CFP (B1) of Treated Test Sections (Years)

Thin Bonded Overlay Thick Bonded Overlay 

Milling Shot Blasting Milling Shot Blasting

G NG G NG G NG G NG

RFP B1 RFP B1 RFP B1 RFP B1 RFP B1 RFP B1 RFP B1 RFP B1

WF CRCP Iowa (19) 20 20 0 20 0 NS — NS — 20 0 20 0 NS — 20 0Minnesota (27) ND 20 — 20 — 20 — 20 — 20 — 20 — 20 — 17 —

JPCP Missouri (29) 10 20 10 16 6 20 10 10 0 20 10 20 10 19 9 20 10

WNF CRCP Louisiana (22) NCS 20 — 20 — 20 — 20 — 20 — 20 — 20 — NS —

—Indicates could not be calculated.

G = Grouting.

NG = No grouting.

NCS = No control section.

ND = No data, no distress is observed, or fewer than three data points.

NS = Negative slope.

Thin = 3 inches (76 mm).

Thick = 5 inches (127 mm).

B1 = CFP.
222

Table 101. Impact of bonded concrete overlays on pavement performance in terms of RSP based on longitudinal cracking.

Climatic 

Region

Existing 

Pavement 

Type

State

(State Code)

Control 

Section 

RFP 

(Year)

RSP and CSP (B1) of Treated Test Sections (Years)

Thin Bonded Overlay Thin Bonded Overlay

Milling Milling Milling Milling 

G NG G NG G NG G NG

RSP B1 RSP B1 RSP B1 RSP B1 RSP B1 RSP B1 RSP B1 RSP B1

WF CRCP

Iowa (19) ND ND — 20 — ND — ND — ND — NS — ND — ND —

Minnesota

(27)

ND ND — ND — ND — 20 — 20 — ND — 20 — 11 —

JPCP Missouri (29) 20 ND — 18 2 20 0 20 0 20 0 20 0 13 7 20 0

WNF CRCP Louisiana

(22)

NCS NS — ND — ND — ND — ND — 20 — ND — 20 —

—Indicates could not be calculated.

G = Grouting.

NG = No grouting.

NCS = No control section.

ND = No data, no distress was observed, or fewer than three data points.

NS = Negative slope.

Thin = 3 inches (76 mm).

Thick = 5 inches (127 mm).

B1 = CFP.
223

Table 102. Impact of bonded concrete overlays on pavement performance in terms of RSP based on transverse cracking.

Climatic 

Region

Existing 

Pavemen

t Type

State

(State Code)

Control 

Section 

RFP 

(Year)

RSP and CSP (B1) of Treated Test Sections (Years)

Thin Bonded Overlay Thin Bonded Overlay

Milling Milling Milling Milling 

G NG G NG G NG G NG

RSP B1 RSP B1 RSP B1 RSP B1 RSP B1 RSP B1 RSP B1 RSP B1

WF CRCP Iowa (19) ND 9 — 7 — 6 — 6 — 0 — 0 — 3 — 7 —Minnesota (27) ND 0 — 0 — 0 — 0 — 0 — ND — 0 — 2 —

JPCP Missouri (29) 20 0 20 11 9 9 11 0 20 0 20 0 20 0 20 0 20

WNF CRCP Louisiana (22) NCS 0 — 0 — 2 — 0 ND — 0 — 0 — 0 —

—Indicates could not be calculated.

G = Grouting.

NG = No grouting.

NCS = No control section.

ND = No data, no distress is observed, or fewer than three data points.

NS = Negative slope.

Thin = 3 inches (76 mm).

Thick = 5 inches (127 mm).

B1 = CFP.
224

IRI

Table 100 lists RFPs and CFPs of all LTPP CRCP test sections located in Iowa, Minnesota, 

and Louisiana and the JPCP test sections located in Missouri. The data in the table indicate 

the following:

• The measured time-dependent IRI data of three of the eight CRCP test sections in Iowa

showed improvement in the IRI over time (negative slope) without the application of any 

treatment. Hence, RFP and CFP of those three test sections were not calculated.

• RFPs of the other 21 CRCP test sections in Iowa, Minnesota, and Louisiana were about 

20 years (20 years for 20 sections and 17 years for 1 section in Minnesota). That is, the 

data indicate that the performance of the treated CRCP test sections was independent of 

the eight treatment types and the two climatic regions.

• RFPs and the CFPs of the eight JPCP test sections located in Missouri appeared to be 

related to the treatment type. RFPs of the two test sections that were not grouted and 

subjected to a 3-inch (76-mm) concrete overlay with milling (703) or with shot blasting 

(704) are 16 and 10 years, respectively. These RFPs were 20 and 50 percent lower than 

the other two test sections that were grouted and subjected to 3-inch (76-mm) overlay and 

the four test sections that were subjected to 5-inch (127-mm) concrete overlays with and 

without grouting.

• The maximum CFP of the JPCP test sections (10 years) is mainly due to the low RFP of 

the control section (10 years).

Longitudinal Cracking

Table 101 lists RSPs and CSPs of all LTPP CRCP test and control sections located in Iowa, 

Minnesota, and Louisiana, and the JPCP test sections located in Missouri. The data in the table 

indicate the following:

• Only one of the eight test sections in Iowa had adequate time-series longitudinal data to 

be analyzed. The RSP of that test section was 20 years. Another test section showed 

improvement in the length of longitudinal cracking over time without the application of 

any treatment (negative slope). The LTPP database contained 0.1-ft (30-mm)-long

measured longitudinal cracking over time for the other six test sections and for the 

control section.

• The RSPs and CSPs for four test sections in Minnesota are listed in table 101. Once 

again, The LTPP database contained 0.1-ft (30-mm)-long measured longitudinal cracking 

over time for three test sections and for the control section and only two data points for 

one test section.

• The RSPs and CSPs for two test sections in Louisiana are listed in table 101. Once again, 

the LTPP database contained 0.1-ft (30-mm)-long measured longitudinal cracking over 

time for four test sections and only two data points for one test section. The data showed
225

improvement over time in the length of longitudinal cracking without the application of 

any treatment.

• The RSPs and CSPs of six JPCP test sections located in Missouri appear to be 

independent of the treatment type The RSP of one test section that was subjected to 

5-inch (127-mm) concrete overlay with shot blasting and grouting was 13 years, about 

7 years shorter than the RSPs of the other test sections. This could be the exception and 

not the rule. Stated differently, no decision could be or should be drawn based on only 

one section.

Transverse Cracking

Table 102 lists the RSP and the CSP values of most LTPP CRCP test sections located in Iowa, 

Minnesota, and Louisiana and the JPCP test sections located in the State of Missouri. The LTPP 

database did not contain adequate data except for two CRCP test sections, one located in 

Minnesota and the other in Louisiana. The data in table 102 indicated that none of the eight 

treatments in the two climatic regions were successful in treating transverse cracking problems in 

CRCP. The time-series transverse cracking data indicated that the RSP value was 0 years for 

two test sections in Iowa, six test sections in Minnesota, and six test sections in Louisiana. 

Further, the RSP of only one test section in each of the two States was 2 years, while the RSP of 

six test sections in Iowa ranged from 3 to 9 years.

Summary, Conclusions, and Recommendations for LTPP SPS-7

The LTPP SPS-7 experiment was designed to study the effects of bonded concrete overlay 

thickness, surface preparation before concrete overlay, and the use of cement grout on the 

performance of PCC pavements. Such study would be based on comparison between the 

performance of the test sections and the performance of compatible control sections. The 

pavement condition and distress data for each test and control section were downloaded from the 

LTPP database, organized, and analyzed to obtain the performance of the sections. Results of the 

analyses are listed in table 100 through table 102. Based on the results of the analyses, the 

following conclusions were drawn:

• The IRI-based performance of the treated CRCP test sections was independent of the 

eight treatment types and the two climatic regions.

• The performance of the JPCP test sections subjected to 3-inch (76-mm) concrete overlay 

with milling (703) or with shot blasting (704) treatments appeared to be lower than the 

performance of the other JPCP test sections subjected to the other six treatments.

• Because of lack of an adequate number of data points in the LTPP database, no specific 

conclusions could be drawn regarding longitudinal cracking performance.

• None of the eight treatments were effective to treat transverse cracking problems of the 

CRCP test sections.
226

IMPACTS OF PAVEMENT TREATMENTS ON PAVEMENT PERFORMANCE USING 

THE LTPP GPS-7 TEST SECTIONS

The GPS-7 pavement test sections are composites that were overlain prior to their assignment to 

the LTPP Program. The experiment also includes rigid pavement test sections that were moved 

from other LTPP experiments after they were subjected to AC overlay or existing composite 

pavement test sections that were subjected to mill and fill. The test sections in the GPS-7 

experiment are classified as GPS-7A, -7B, -7C, -7D, -7F, and -7S. The following explains each 

of the classifications:

• GPS-7A: The test sections under this classification were part of the original LTPP design. 

They were subjected to AC overlay prior to their assignment to the LTPP Program.

• GPS-7B: The test sections under this classification were also part of the original LTPP 

design. They were subjected to AC overlay following assignment to the LTPP Program.

• GPS-7C, -7D, -7F, and -7S: The test sections under these classifications did not have an 

experimental design associated with them. They were moved to GPS-7C, -7D, -7F, or -7S 

classification from other LTPP experiments after they were subjected to rehabilitation 

actions. The specific classification in the four GPS-7 experiments depended on the type 

of pavement rehabilitation detailed as follows:

o If the rigid pavement test sections from other LTPP experiments were overlain with 

virgin AC mixes, they were moved to the GPS-7B classification.

o If the rigid pavement test sections from other LTPP experiments were overlain or if 

the existing composite pavement test sections were overlain again using recycled AC 

mixes, they were moved to the GPS-7C classification.

o If the existing composite pavement test sections were overlain again using 

conventional AC mixes, they were moved to the GPS-7D classification.

o If the rigid pavement test sections from other LTPP experiments were subjected to 

crack and break and seat before being overlain using virgin or recycled AC mixes, 

they were moved to the GPS-7F classification.

o If the existing composite pavement test sections from other LTPP experiments were 

subjected to mill and fill using virgin or recycled AC mixes, they were moved to the 

GPS-7S classification.

Unfortunately, the number of rigid and composite pavement test sections that had more than 

three condition and/or distress data points before they were subjected to overlay or mill-and-fill 

treatments was extremely low. Given that the behavior of rigid pavement test sections would be 

much different than that of a composite pavement test sections, they could not be grouped to 

increase the number of test sections for analyses. However, the LTPP test sections in the GPS-7 

experiment that had three or more after treatment time-series pavement condition and/or distress 

data points were grouped according to the following variables:
227

• Two treatment types (AC overlay and mill and fill).

• AC mix type (virgin and recycled).

• Thickness types (thin ≤ 2.5 inches (63.5 mm) and thick > 2.5 inches (63.5 mm)).

• Four climatic regions (WF, WNF, DF, and DNF).

• One pavement condition (IRI).

• Four pavement distress types (rut depth, and alligator, longitudinal, and transverse cracking).

After grouping, the data were analyzed to assess, in each climatic region, the impacts of 

treatment type, AC mix type, and thickness on the calculated RFP and RSP based on IRI, rut 

depth, and cracking. It should be noted that the LTPP database contained no before treatment 

pavement condition and distress data for any test section. Therefore, only the RFP or RSP of the 

pavement sections were calculated. For each pavement condition and distress type, the average 

RFPs and/or RSPs of the test sections located in the same climatic region were calculated and are 

listed in table 103 through table 107. The data in the five tables are discussed in the following 

sections per pavement condition and distress type.
228

Table 103. Impacts of various treatment types on RFP of the test sections based on IRI.

Treatment 

Type Mix Type Thickness

Number of Test Sections and RFP Values in the Designated Climatic Region

WF WNF DF DNF

No. RFP (Year) No. RFP (Year) No. RFP (Year) No. RFP (Year)

Overlay

Virgin Thin 6 18 0 — 0 — 0 —

Thick 25 19 6 20 1 20 1 20

Recycled Thin 2 20 1 20 1 20 0 —

Thick 0 — 0 — 1 20 1 20

Mill and fill 

Virgin Thin 3 20 2 17 1 20 0 —

Thick 3 20 2 20 0 — 0 —

Recycled Thin 1 20 0 — 0 — 0 —

Thick 0 — 0 — 0 — 0 —

— Indicates could not be calculated.

No. = Number of test sections.

Thin = ≤ 2.5 inches (63.5 mm).

Thick = > 2.5 inches (63.5 mm).
229

Table 104. Impacts of various treatment types on RFP/RSP of the test sections based on rut depth.

Treatment 

Type

Mix 

Type Thickness

Number of Test Sections and RFP/RSP Values in the Designated Climatic 

Region

WF WNF DF DNF

No.

RFP/RSP

(Year) No.

RFP/RSP

(Year) No.

RFP/RSP

(Year) No.

RFP/RSP

(Year)

Overlay

Virgin Thin 4 20 0 — 0 — 0 —

Thick 20 20 6 20 0 — 0 —

Recycled Thin 0 — 0 1 6 0 —

Thick 0 — 1 20 0 — 0 —

Mill and fill 

Virgin Thin 3 20 1 20 1 20 0 —

Thick 3 20 1 20 0 — 0 —

Recycled Thin 1 20 0 — 0 — 0 —

Thick 0 — 0 — 0 — 0 —

— Indicates could not be calculated.

No. = Number of test sections.

Thin = ≤ 2.5 inches (63.5 mm).

Thick = > 2.5 inches (63.5 mm).
230

Table 105. Impacts of various treatment types on RSP of test sections based on alligator cracking.

Treatment Type Mix Type Thickness

Number of Test Sections and RSP Values in the Designated Climatic Region

WF WNF DF DNF

No. RSP (Year) No. RSP (Year) No. RSP (Year) No. RSP (Year)

Overlay

Virgin Thin 2 19 1 11 0 — 0 —

Thick 13 14 4 12 0 — 1 10

Recycled Thin 1 9 1 12 0 — 0 —

Thick 0 — 1 20 0 — 0 —

Mill and fill 

Virgin Thin 1 20 1 20 1 20 0 —

Thick 2 12 0 — 0 — 0 —

Recycled Thin 0 — 0 — 0 — 0 —

Thick 0 — 0 — 0 — 0 —

— Indicates could not be calculated.

No. = Number of test sections.

Thin = ≤ 2.5 inches (63.5 mm).

Thick = > 2.5 inches (63.5 mm).

Table 106. Impacts of various treatments on pavement performance in terms of RSP based on longitudinal cracking.

Treatment Type Mix Type Thickness

Number of Test Sections and RSP Values in the Designated Climatic Region

WF WNF DF DNF

No. RSP (Year) No. RSP (Year) No. RSP (Year) No. RSP (Year)

Overlay

Virgin Thin 4 6 1 4 0 — 0 —

Thick 17 8 6 8 0 — 2 10

Recycled Thin 0 — 0 — 1 8 0 —

Thick 0 — 1 10 1 8 0 —

Mill and fill 

Virgin Thin 1 9 1 15 1 8 0 —

Thick 3 6 2 13 0 — 0 —

Recycled Thin 2 5 0 — 0 — 0 —

Thick 0 — 0 — 0 — 0 —

— Indicates could not be calculated.

No. = Number of test sections.

Thin = ≤ 2.5 inches (63.5 mm),

Thick = > 2.5 inches (63.5 mm).
231

Table 107. Impacts of various treatments on pavement performance in terms of RSP based on transverse cracking.

Treatment Type Mix Type Thickness

Number of Test Sections and RSP Values in the Designated Climatic Region

WF WNF DF DNF

No. RSP (Year) No. RSP (Year) No. RSP (Year) No. RSP (Year)

Overlay

Virgin Thin 4 8 1 6 0 — 0 —

Thick 16 11 6 17 0 — 1 13

Recycled Thin 1 3 1 7 0 — 0 —

Thick 0 — 1 17 0 — 0 —

Mill and fill

Virgin Thin 1 11 1 17 1 9 0 —

Thick 4 16 2 16 0 — 0 —

Recycled Thin 2 12 0 — 0 — 0 —

Thick 0 — 0 — 0 — 0 —

— Indicates could not be calculated.

No. = Number of test sections.

Thin = ≤ 2.5 inches (63.5 mm).

Thick = > 2.5 inches (63.5 mm).
232

IRI

Table 103 lists the average RFPs of test sections located in the same climatic zone and subjected 

to one of the four treatments listed in the table. The data indicate that the average RFP of the test 

sections was between 17 and 20 years.

Rut Depth

Table 104 lists the average RFPs/RSPs of test sections located in the same climatic zone and 

subjected to one of the four treatments listed in the table. The data indicate that except for

one test section, the average RFP/RSP of all other sections was 20 years. Again, the exemption 

was one test section located in the DF region and subjected to thin overlay using recycled AC 

mixes. Its RFP/RSP was only 6 years. The reason for this RFP/RSP was highly likely problems 

associated with the AC mix or with construction of the overlay. The AC mix problems could be 

excessive binder content or unstable mix while the construction issue could be inadequate 

compaction of the overlay or the early opening of the road to traffic.

Alligator Cracking

Table 105 lists the average RSPs of test sections located in the same climatic zone and subjected 

to one of the four treatments listed in the table. It is important to note that the labeling as 

“Alligator Cracking” was highly likely not related to bottom-up fatigue cracks in composite 

pavements. The label was most likely related to advanced stages of top-down fatigue cracking. 

Nevertheless, the data in the table indicate that the average RSPs varied from 9 to 20 years 

detailed as follows:

• In the WF region, the average RSPs of the test sections that were subjected to thin 

overlay and mill-and-fill treatments using virgin AC mixes were 19 and 20 years, 

respectively, while the average RSPs of the test sections that were subjected to thick 

overlay and mill-and-fill treatments using virgin AC mixes were 14 and 12 years, 

respectively. Further, the RSP of thin overlay treatment using recycled AC mixes was 

only 9 years.

• The RSPs of the eight test sections located in the WNF region varied from 11 to 20 years.

• In the DF region, the RSP of the one test section subjected to thin mill-and-fill treatment 

using virgin AC mix was 20 years.

• In the DNF region, the RSP of the one test section subjected to thick overlay treatment 

using virgin AC mix was 10 years.

Because of the limited number of test sections subjected to a certain treatment and located in one 

climatic zone, and the lack of time-dependent pavement condition and distress data before 

treatment, no reliable conclusion could be drawn regarding the benefits of one treatment. For the 

same reasons the benefits of the various treatments could not be compared. 
233

Longitudinal Cracking

Table 106 lists the average RSPs of test sections located in the same climatic zone and subjected 

to one of the four treatments listed in the table. The data in the table indicate that the average 

RSPs varied from 4 to 15 years detailed as follows:

• In the WF region, the average RSPs of the test sections that were subjected to thin 

overlay and mill-and-fill treatment treatments using virgin AC mixes were 6 and 9 years, 

respectively, while the average RSPs of the test sections that were subjected to thick 

overlay and mill-and-fill treatments using virgin AC mixes were 8 and 6 years, 

respectively. Furthermore, the RSP of thin mill-and-fill treatment using recycled AC 

mixes was only 5 years.

• The RSPs of the 11 test sections located in the WNF region varied from 4 to 15 years.

• In the DF region, the three test sections had an RSP of 8 years.

• In the DNF region, the average RSP of the test sections subjected to thick overlay 

treatment using virgin AC mix was 10 years.

Once again, because of the limited number of test sections subjected to a certain treatment and 

located in one climatic zone, and the lack of time-dependent pavement condition and distress 

data before treatment, no reliable conclusion could be drawn regarding the benefits of one 

treatment. For the same reasons, the benefits of the various treatments could not be compared.

Transverse Cracking

Table 107 lists the average RSPs of test sections located in the same climatic region and 

subjected to one of the four treatments listed in the table. The data in the table indicate that the 

average RSPs varied from 4 to 17 years, detailed as follows:

• In the WF region, the average RSPs of the test sections that were subjected to thin 

overlay and mill-and-fill treatments using virgin AC mixes were 8 and 11 years, 

respectively, while the average RSPs of the test sections that were subjected to thick 

overlay and mill-and-fill treatments using virgin AC mixes were 11 and 16 years, 

respectively. Furthermore, the RSPs of thin overlay and mill-and-fill treatment using 

recycled AC mixes were 3 and 12 years, respectively.

• The RSPs of the 12 test sections located in the WNF region varied from 6 to 17 years. 

The thicker the AC overlay was, the longer the RSP was.

• In the DF region, the one test section had an RSP of 9 years.

• In the DNF region, the one test section had an RSP of 13 years.

Similar to alligator and longitudinal cracking, because of the limited number of test sections 

subjected to a certain treatment and located in one climatic zone, and the lack of time-dependent 

pavement condition and distress data before treatment, no reliable conclusion could be drawn 
234

regarding the benefits of one treatment. For the same reasons, the benefits of the various 

treatments could not be compared. 

Summary, Conclusions, and Recommendations for LTPP GPS-7

As noted earlier, the LTPP GPS-7 experiment consisted of the following types of sections:

• Composite pavement test sections that were subjected to AC overlay prior to their 

assignment to the LTPP Program.

• JPCP and JRCP test sections that were moved from other LTPP experiments after they 

were subjected to AC overlay.

• Composite pavement test sections that were parts of the other LTPP experiments that 

were milled and filled and moved to GPS-7.

All of the GPS-7 test sections that had more than three after-treatment data points were analyzed 

and then grouped based on the AC overlay thickness, surface preparation before the AC overlay, 

overlay type, and climatic regions. It should be noted that the before treatment data were those for 

rigid pavement, hence they were not included in the comparison of the pavement performance 

before and after treatment. Nevertheless, results of the analyses are listed in table 103 through

table 107. Based on the results of the analyses, the following conclusions were drawn:

• The average RFPs of the test sections in all the climatic regions subjected to any of the 

treatments previously described was between 17 and 20 years.

• The average RFP/RSP of all but one test section in all the climatic regions was 20 years. 

The RFP/RSP of that one test section was 6 years, most likely owing to construction 

problems.

• Given the limited number of sections available for analyses, no reliable conclusions could 

be drawn for alligator, transverse, and longitudinal cracking.
235

CHAPTER 7. LTPP DEFLECTION DATA ANALYSES

FWD DEFLECTION DATA AND RFP AND RSP

Pavement deflection data are typically collected by State transportation departments at the

project level and rarely at the network level. However, most LTPP test sections were subjected to 

deflection testing on a periodic basis. The deflection data were collected using the most common 

type of equipment, the FWD. FWD tests are often preferred over laboratory testing for several 

reasons, including the following:

(78,83)

• FWD tests are nondestructive in nature, whereas laboratory tests require cores.

• The operational cost per FWD test is much lower than a laboratory test.

• FWD tests have short duration and can be designed to provide more coverage of the 

pavement network compared with laboratory tests, which are time consuming and limited 

to the locations where pavement cores were extracted.

• FWD data reflect the in-situ boundary conditions.

The FWD operates on two basic assumptions: the force of impact of a falling weight is 

considered a static load, and the roadbed soils act as an elastic body.(83) The deflection data 

collected from FWD testing are typically used for the following purposes:

• Backcalculating the moduli values of the pavement layers, which can be used to assess the 

structural capacity of the roadway and to facilitate treatment type selection and design.

• Assessing the variability of the structural capacity along and across the pavement sections.

• Analyzing LTE in rigid pavements.

• Determining the presence of voids under the pavement slabs.

• Studying the magnitude of slab curling and its impact on ride quality.

At the time of this report, research was exploring methods to increase the efficiency of deflection 

data collection by using a rolling wheel deflectometer (RWD). The RWD collects pavement 

deflection data at highway speeds and makes network-level data collection more feasible. It was 

reported that the results were used to flag structurally deficient pavement sections for further 

analyses and to estimate the structural number.(83,84)In this study, the LTPP deflection data were

analyzed to determine whether the data could be used to estimate the RSP of pavement sections 

and to determine the critical time for pavement preservation.

The RSP algorithm is primarily based on the measured time-dependent pavement surface condition 

and distress data and their corresponding threshold values. Hence, the distress (such as cracking) 

must be visible from the pavement surface. During the development of the RFP and RSP concepts, 

it was envisioned that the pavement deflection data could be used to indicate impending distress 
236

and become a part of the RSP algorithm. Such algorithms would empower State transportation 

departments to take corrective actions prior to the manifestation of surface defects.

To incorporate deflection into the RSP algorithm, a deflection threshold value must be developed 

for each pavement section. To investigate the potential for the development of deflection 

threshold values, the measured FWD deflection data of various LTPP test sections were analyzed 

as described in the next few subsections.

At the outset, it was envisioned that the rate of change and the magnitude of the measured 

deflections were related to the measured pavement distresses such as alligator cracking. Because 

alligator cracks initiate at the bottom of the asphalt layer and propagate upward toward the 

pavement surface, the pavement system starts to weaken as the cracks initiate and before they 

reach the pavement surface. Therefore, it was assumed that flexible pavement deflections would 

start to increase before the appearance of alligator cracking on the pavement surface (i.e., the 

response of a pavement structure to load would increase as the pavement deteriorated, which 

could be used as an early warning of impending, surface alligator cracking). Similarly, for rigid 

pavements, increasing the magnitude of deflection could be a sign of deterioration of the 

concrete slab support. Such deterioration could lead to distresses such as transverse cracking, 

corner breaks, and so forth. Again, increasing deflection over time may provide a flag prior to 

the appearance of the pavement surface distress. Further, LTE across joints or cracks in rigid 

pavements was typically measured using the differential deflection across joints or cracks. 

Increasing relative differential deflection implied lower LTE. It was also envisioned that the rate 

of change of LTE across joints or cracks in rigid pavements could be related to the rate of change 

of faulting. Joints with a good load transfer mechanism, such as dowel bars, would have almost 

100-percent LTE and little or no faulting, whereas joints without dowel bars or with damaged or 

sheared dowel bars, or cracks with no aggregate interlock, would have minimal LTE and 

increased probability of faulting. Therefore, decreasing LTE over time could be used to warn of 

impending surface defects.

TIME-SERIES FWD DATA MODELING—FLEXIBLE PAVEMENT

Once again, it was envisioned that an analysis of the time-series deflection data could provide 

indication of relationships between pavement deflection and pavement condition or distress. To 

investigate such potential relationships, FWD deflection data from the LTPP SMP test sections 

were plotted as a function of time following the procedures discussed in the following 

subsections. A partial record of the inventory data for the SMP test sections are listed in 

table 108. The deflection data were analyzed to determine trends in the measured deflection over 

time. Such trends, if they existed, would provide a tool for pavement managers to estimate future 

pavement conditions and distress before surface defects such as cracking occurred. The SMP test 

sections were used in this analysis because deflection data were collected on a much more 

frequent basis than the other LTPP test sections. However, because the FWD tests were 

performed at various times of the year and at different temperatures, the measured deflection was 

adjusted to account for the material properties and their relationships to temperature. The details 

of these adjustments are presented in the following subsections.
237

Table 108. LTPP SMP partial inventory data.

SHRP 

ID

AC 

Thickness 

(inches)

Base 

Thickness 

(inches)

Subbase 

Thickness 

(inches) Roadbed Soil Type

Most Recent 

Traffic 

(ESAL/year

)

Climatic 

Region

010101 7.4 7.9 — — — WNF

010102 4.2 12.0 — — — WNF

040113 4.4 7.5 — — 337,000 DNF

040114 6.8 12.0 — — 308,000 DNF

041024 10.8 6.3 — Coarse-grained soil: clayey sand with gravel — DNF

081053 4.6 5.4 23.5 Fine-grained soils: lean inorganic clay 42,000 DF

091803 7.1 12.0 — Coarse-grained soils: well-graded sand with silt and 

gravel 36,000 WF

100102 4.1 11.8 39.0 Coarse-grained soils: poorly graded sand — WNF

131031 10.6 8.8 — Coarse-grained soil: silty sand — WNF

131005 7.6 8.8 — Coarse-grained soil: clayey sand — WNF

161010 10.7 5.4 — Coarse-grained soil: silty sand 235,000 DF

231026 7.2 17.6 — Coarse-grained soil: silty sand with gravel — WF

241634 3.6 4.8 13.0 Fine-grained soils: silt — WNF

251002 7.8 4.0 4.9 Coarse-grained soils: poorly graded sand with silt — WF

271018 4.4 5.2 — Coarse-grained soils: poorly graded sand with silt — WF

271028 9.6 0.0 — Coarse-grained soils: poorly graded sand with silt 112,000 WF

276251 7.4 10.2 — Coarse-grained soils: poorly graded sand with silt 70,000 WF

281802 3.1 4.9 1.6 Coarse-grained soils: poorly graded sand 98,000 WNF

281016 7.6 19.3 — — 52,000 WNF

308129 3.0 22.8 — Fine-grained soils: gravelly lean clay with sand 39,000 DF

310114 6.6 12.0 — — 95,000 WF

331001 8.4 19.3 14.4 Coarse-grained soils: poorly graded sand with silt 61,000 WF

351112 5.4 6.4 — — 51,000 DNF

360801 5.0 8.4 — — 2,000 WF

371028 1.6 8.2 — Coarse-grained soils: poorly graded sand with silt 93,000 WNF
238

SHRP 

ID

AC 

Thickness 

(inches)

Base 

Thickness 

(inches)

Subbase 

Thickness 

(inches) Roadbed Soil Type

Most Recent 

Traffic 

(ESAL/year

)

Climatic 

Region

404165 2.7 5.5 — — 151,000 WNF

460804 6.9 12.0 — — 3,000 DF

469187 5.5 6.0 3.0 Fine-grained soils: lean inorganic clay — DF

481077 4.9 10.4 — Fine-grained soils: sandy silt 132,000 WNF

481068 10.9 6.0 8.0 Fine-grained soils: sandy lean clay — WNF

481122 3.0 15.6 8.4 Coarse-grained soil: clayey sand 51,000 WNF

481060 7.5 12.3 6.0 — 323,000 WNF

483739 1.5 11.4 7.4 Coarse-grained soils: poorly graded sand 52,000 WNF

491001 5.1 5.8 — Coarse-grained soil: silty sand — DF

501002 8.5 25.8 — — 92,000 WF

510113 4.0 7.9 6.0 — 265,000 WNF

510114 7.3 11.9 6.0 Fine-grained soils: sandy silty clay with gravel 253,000 WNF

561007 2.8 6.2 — Coarse-grained soil: silty sand with gravel 5,000 DF

831801 4.4 5.6 13.2 Coarse-grained soil: silty sand 372,000 WF

871622 5.7 6.7 26.3 Fine-grained soils: sandy silt 271,000 WF

906405 2.8 9.0 2.5 Coarse-grained soil: silty sand 90,000 DF

— Indicates no data were available.

1 inch = 25.4 mm.
239

Temperature-Deflection Correlation

Some researchers have developed correlations between temperature and the measured flexible 

pavement deflections. At the time of this report, the most publicized temperature correlation 

model was that of the AI.(85)In this model, the measured pavement deflection basin was adjusted 

based on the measured pavement temperature and the untreated base thickness. Stated 

differently, the AI temperature adjustment factor (TAF) was used as a multiplier to adjust the 

entire measured deflection basin to a standard temperature of 70 oF (21 oC). This made the 

implementation of the AI method at the network level relatively easy. Other models, such as the 

BELLS, had also been developed and verified based on the LTPP SMP data.(86) These models 

were developed using the measured air temperatures during the FWD testing, the measured 

pavement surface temperatures, and the temperatures of the asphalt mat measured at various 

depths from the pavement surface. The use of these models facilitated the adjustment of the 

backcalculated layer moduli values to different temperatures. However, the data required to use 

the models is not readily available in the current or historic deflection records of most State 

transportation departments and would require additional effort to obtain.

In this study, the main purpose of the analyses of the deflection data was to identify 

relationships, if any, between the measured pavement deflection and the measured pavement 

condition data, to determine whether the deflection data could be used to estimate the optimum 

time for pavement preservation. To conduct the analyses, the measured pavement deflections 

must be adjusted to the standard temperature of 70 oF (21 oC). Therefore, the first step in the 

analyses was to validate the existing temperature adjustment models using the LTPP measured 

deflection data along the various SMP test sections. The analyses were based on the measured 

pavement deflections and surface temperature, the data most commonly collected by State 

transportation departments. Please note that at the time of this report, most State transportation 

departments did not collect network level deflection data, however, that may change in the near 

future as more efficient deflection measuring techniques are developed. Nevertheless, the 

temperature adjustment analyses were based on network level assessments, in conjunction with 

the RFP/RSP concepts developed in this study. After the analyses of the temperature adjustment 

models, backcalculation of layer moduli were performed at the project level.

The AI method for temperature adjustment of the measured deflection data could be relatively 

easily applied to network-level deflection data. As stated earlier, the AI method (see figure 67) 

provided TAFs to be multiplied by the measured deflection basin based on the mean pavement 

temperature and the thickness of the untreated base layer. 
240

©Asphalt Institute

1 inch = 25.4 mm.

oF = 1.8 × oC + 32.

Figure 67. Graph. AI TAF.

(85)

To assess the accuracy and applicability of the AI TAFs, the measured LTPP deflection data 

along several SMP test sections were analyzed to determine the TAFs at each site using the 

following steps:

• Step 1: For each of the SMP test sections listed in table 108, the FWD measured 

deflection data, treatment history, and the inventory data were downloaded from the 

LTPP Standard Data Release 28.0. The corresponding measured pavement surface 

temperature at the time of each test was paired with the deflection data for analyses.

• Step 2: The measured deflection data were first analyzed and their variability along the 

test sections and linearity with the load magnitude were scrutinized to produce the 

following findings:

o A linear relationship existed between the drop loads and the measured deflections.

o The measured deflections along a given test section were variable.

o The percent variability of the deflection data was more or less equal across the 

seven FWD sensors and in each climatic region.
241

• Step 3: The data were sorted based on the applied load and the various test locations 

along the test section. Drop height 2, which simulated the application of a 9,000-lbf 

(40-kN) half single axle load, was selected for analyses. The tests performed at the midlane (F1) and outer wheelpath (F3) locations were selected for individual analyses. All 

data from other test loads and locations were not included in the analyses.

• Step 4: On any particular test day, a series of 4 tests were conducted at drop height 2 and 

at about 10 locations along the test section. Note that tests conducted when the 

temperature was below freezing were not included in the analyses because the response 

to load is significantly different for frozen soils. On some days, the test sections were 

subjected to more than one series of tests. The various deflection tests conducted on a test 

section within each specific testing day were averaged along with the measured pavement 

surface temperatures to simulate the data that would be collected by a State transportation 

department (only one test per section). Note that the data averaging generally produced a 

less than 1-percent error and was dependent on the variability of the measured data.

• Step 5: The average peak deflection measured at sensors 1 through 7, located at 0, 8, 12, 

18, 24, 36, and 60 inches (0, 203, 305, 457, 610, 914, and 1,524 mm), respectively, from 

the load, were plotted against the measured pavement surface temperatures. The 

deflection measured beneath the load (d1) was composed of the response from all 

pavement layers; at 8 inches (203 mm) from the load (d2), it had slightly less influence 

from the asphalt layer; and at 60 inches (1,524 mm) from the load (d7), the pavement 

response was mainly from the roadbed soil. The asphalt layer was significantly more 

affected by temperature than the other layers, and as such, it was anticipated that the 

correlation between deflection and temperature would be significant at sensor 1 and 

would decrease with increasing distance from the load plate.

• Step 6: The measured deflection data for each sensor were then plotted and modeled as a 

function of temperature using the linear function shown in figure 68.

Figure 68. Equation. APDi.

Where:

APDi = Average peak pavement deflection at sensor i.

and Ci are regression constants for sensor i.

Figure 69 depicts the measured pavement deflections at sensors 1, 2, 4, and 7 as a linear 

function of the pavement surface temperature for SHRP test section 010101. The data in 

the figure indicate that, as it was expected, increases in the pavement temperatures caused

increases in the pavement deflections at sensors 1 and 2, while the deflection data at 

sensors 4 and 7 were minimally affected by temperature. The data also indicate that the rate 

of change of deflection with respect to temperature (the slope of the lines) decreased as the 

distance from the load to the sensor increased. Indeed, the slope at sensor 7 was zero. 

APDi = ωi + Ci

ωi
242

1 mil = 25.4 microns.

oF = 1.8 × oC + 32.

Figure 69. Graph. Peak measured pavement deflection at sensors 1, 2, 4, and 7 versus 

pavement surface temperature for SHRP test section 010101, F3.

In addition, the measured deflection data at sensor 1 along the outer wheelpath (F3) and mid 

lane (F1) along SHRP test sections 010101 and 010102 are depicted in figure 70 as a 

function of the measured pavement temperature. It can be seen from the figure that all the 

data could be modeled as a function of temperature using the same form as in figure 68.
243

oF = 1.8 × oC + 32.

1 mil = 25.4 micron.

Figure 70. Graph. Average measured peak deflection at sensor 1 versus the measured 

pavement surface temperature along the outer wheelpath and mid lane of SHRP test 

sections 010101 and 010102.

• Step 7: For each deflection sensor, figure 68 was then used to calculate the TAFs based 

on a standard temperature of 70 oF (21 oC). The calculated TAFs were then modeled as a 

function of temperature using figure 71. For each deflection sensor, the values of the 

regression constants of figure 71 were obtained and are listed in table 109.

Figure 71. Equation. TAFdi.

Where:

TAFdi = Temperature adjustment factor for sensor di (multiply the measured pavement 

deflection at temperature T and sensor di by TAFdito adjust the deflection to 70 oF 

(21 oC)).

di = Deflection sensor at the ith distance from the FWD load.

, , = regression constants (see table 109).

T = Measured pavement surface temperature at the time of FWD testing (oF).

• Step 8: The parameters of figure 71, listed in table 109, were then plotted as a function of 

the lateral distance of the deflection sensors from the center of the load. The plot was 

used to model the regression parameters of figure 71 as a function of the lateral distance. 

TAFdi= αiT

2 + βiT + γi

αi βi γi
244

This yielded figure 72, which is a global temperature adjustment equation that can be 

applied to the measured deflections at all deflection sensors.

Table 109. The regression parameters of figure 71 for each deflection sensor.

Deflection 

Sensor

Distance from FWD 

load (inches)

Regression Terms and Values

i (E−05) i (E−03) i

d1 0 7.09 −18.24 1.930

d2 8 3.72 −10.62 1.562

d3 12 2.45 −7.33 1.394

d4 18 1.59 −4.44 1.233

d5 24 1.37 −3.00 1.143

d6 36 1.65 −2.04 1.062

d7 60 1.83 −2.18 1.062

1 inch = 25.4 mm.

Figure 72. Equation. TAF.

Where:

TAF = Temperature adjustment factor (multiply the measured pavement deflection by 

this factor to obtain the temperature-adjusted deflection).

A, B, and C = Global regression parameters of the global temperature adjustment model.

T = Pavement surface temperature measured at the time of FWD testing (oF).

The equations in figure 73 through figure 75 define the global regression parameters in 

figure 72.

Figure 73. Equation. A.

Figure 74. Equation. B.

Figure 75. Equation. C.

Where:

d = Lateral distance from the center of the load to the sensor (inch).

1,2,3, 1,2,3, 1,2,3, 1,2 = Regression values (see table 110).

α β γ

TAF = AT

2 + BT + C

A = α1d

3 + β1d2 + γ1d + δ1

B = α2d

3 + β2d2 + γ2d + δ2

C = α3d

2 + β3d + γ3

α β γ δ
245

Table 110. Regression values for Calculation of Parameters A, B, and C in figure 73 

through figure 75.

Regression Parameter

Regression Value

A B C

1 −1.429E−09 — —

2 — 2.293E−07 —

3 — — 4.839E−04

1 1.631E−07 — —

2 — −2.964E−05 —

3 — — −4.232E−02

1 −5.516E−06 — —

2 — 1.221E−03 —

3 — — 1.881

1 7.086E−05 — —

2 — −1.831E−02 —

— Indicates not applicable.

• Step 9: The accuracy of the TAF equation (figure 72) and the AI temperature adjustment 

procedure were checked using the measured deflection data on April 25, 1995, along the 

mid-lane of the SHRP test section 351112. The measured deflection data at 52, 70, and 

86 oF (11, 21, and 30 oC) are shown in figure 76. The data at 52 and 86 oF (11 and 30 oC)

were adjusted to the standard temperature of 70 oF (21oC) using TAF and the AI 

procedures. Both results are depicted in figure 77. The square symbols in the figure 

represent the measured deflection data at 70 oF (21 oC) while the various dotted and 

dashed curves are the temperature adjusted deflection data measured at 52 and 86 oF

(11 and 30 oC) for the TAF and AI methods, respectively. The differences between the 

square symbols and the dotted and dashed curves in the figure are indicative of the 

accuracy of the TAF and the AI temperature adjustment procedures. It can be seen from 

the figure that the differences between the curves obtained using TAF and the square 

symbols were much smaller than the differences between the curves obtained using the 

AI procedure and the square symbols. These differences were then calculated as a percent 

of the measured deflection data at 70 oF (21 oC). These percentages (see figure 78) 

represent the errors between the temperature adjusted deflection data and the measured 

deflection data at 70 oF (21 oC). It can be seen from figure 78 that the TAF (figure 72) 

generally produced much smaller errors than the AI procedure.

α

α

α

β

β

β

γ

γ

γ

δ

δ
246

oF = 1.8 × oC + 32.

1 mil = 25.4 micron.

1 inch = 25.4 mm.

Figure 76. Graph. Measured deflection basins at three pavement surface temperatures 52, 

70, and 86 oF (11, 21, and 30 oC) along the mid-lane of the SHRP test section 351112 on

April 25, 1995.
247

oF = 1.8 × oC + 32.

1 mil = 25.4 micron.

1 inch = 25.4 mm.

Figure 77. Graph. Error from TAF and AI adjusted deflection basin at two pavement 

surface temperatures (52 and 86 oF (11 and 30 oC)), along the mid-lane of the SHRP test 

section 351112 on April 25, 1995.

oF = 1.8 × oC + 32.

Figure 78. Graph. Percent error of the temperature-adjusted deflection data using the 

equation in figure 72 and the AI procedure.
248

• Step 10: The previous step was repeated using the measured deflection data and 

pavement temperature of test sections located in the four climatic regions. The average 

regression parameters for figure 72 and the overall averages were then calculated and are 

listed in table 111. The differences in the TAF between climatic regions were generally 

minimal, especially above about 60 oF (16 oC).

Table 111. Averages and overall average of the regression parameters of TAF (figure 72) 

for each climatic region.

Regression 

Term

Average Regression Value for Each Climatic Region Overall 

WF WNF DF DNF Average

1 −1.94E−09 −1.27E−09 −8.70E−10 −1.63E−09 −1.43E−09

2 2.82E−07 2.18E−07 1.62E−07 2.49E−07 2.29E−07

3 6.73E−04 4.04E−04 3.23E−04 5.58E−04 4.84E−04

1 2.34E−07 1.39E−07 9.97E−08 1.75E−07 1.63E−07

2 −3.88E−05 −2.67E−05 −2.05E−05 −3.21E−05 −2.96E−05

3 −5.74E−02 −3.55E−02 −3.00E−02 −4.88E−02 −4.23E−02

1 −8.35E−06 −4.24E−06 −3.71E−06 −5.71E−06 −5.52E−06

2 1.67E−03 1.03E−03 8.69E−04 1.34E−03 1.22E−03

3 2.19 1.65 1.90 1.85 1.88

1

1.07E−04 4.61E−05 6.81E−05 6.73E−05 7.09E−05

2

−2.54E−02 −1.32E−02 −1.82E−02 −1.77E−02 −1.83E−02

Flexible Pavement Deflection Versus Time

The average peak deflections, after applying temperature adjustment, were used to study the 

changes in deflection over time. It was envisioned that structural distresses, such as fatigue 

cracking, might be correlated to the pavement deflection. For example, fatigue cracking could 

propagate toward the pavement surface at a rate proportional to the rate of increase in pavement 

deflection. The pavement deflection was envisioned to decrease following construction as the 

pavement was further compacted by traffic, and then eventually began to increase as the 

pavement began to deteriorate. Note that this relationship was only anticipated for SPS sections, 

because GPS sections were in service before the commencement of data collection. The 

relationships between pavement deflection and time were studied using the following steps:

• Step 1: For each deflection sensor, the averages of the peak deflections measured before

application of the first treatment were plotted as a function of time as shown in figure 79

(sensors 1, 2, 4, and 7 are shown). Note that for a few test sections, minor treatments such 

as skin patching were ignored when necessary to increase the available number of data 

points. The data in figure 79 indicates that the average measured peak deflections at each 

of sensors 1, 2, 4, and 7 were more or less consistent over time and fluctuated owing to 

pavement temperature and the moisture conditions at the time of testing. Note that 

fluctuation in the deflection generally mirrored fluctuation in pavement temperatures; 

lower temperature corresponded to lower deflection. The trend generally stayed 

consistent over time, and the deflection magnitude decreased with increasing distance 

from the load and decreasing temperatures.

α

α

α

β

β

β

γ

γ

γ

δ

δ
249

oF = 1.8 × oC + 32.

1 mil = 25.4 microns.

Figure 79. Graph. Average measured peak pavement deflection at sensors 1, 2, 4, and 

7 versus time for SHRP test section 010101, F1.

• Step 2: Each deflection versus time plot (d1) was visually inspected and assigned a trend 

description. The deflection was consistent over time, increasing over time, decreasing 

over time, or decreasing and then increasing over time. A summary of the results of this 

inspection is provided in table 112. Note that the majority of the SMP test sections 

exhibited more-or-less consistent deflection over time at sensor 7.

Table 112. Summary of deflection for sensor 1 versus time trend descriptions.

Trend Description

Measured Data

Temperature-Adjusted 

Data

No. of Test 

Sections Percentage

No. of Test 

Sections Percentage

Consistent 44 56 46 59

Increasing 24 31 25 32

Decreasing 3 4 3 4

Decreasing and then increasing 7 9 4 5

• Step 3: The measured deflection data were then temperature adjusted using the equation 

in figure 72 and plotted against time as shown in figure 80 for sensors 1, 2, 4, and 7, 

respectively. The data in figure 80 indicate that, during the 12-year period, the measured 

sensor 1 deflection decreased from about 9.84 to 7.87 mil (250 to about 200 microns)

with some minor localized fluctuations. These fluctuations are the result of the pavement 
250

temperature and moisture conditions at the time of testing. The trend over time generally 

stayed consistent with distance from the applied load, while the deflection magnitude and 

variability decreased.

1 mil = 25.4 micron.

Figure 80. Graph. Temperature-adjusted peak deflection at sensors 1, 2, 4, and 7 versus 

time for SHRP test section 010101, F1.

• Step 4: The temperature-adjusted deflection versus time plots were visually evaluated as 

in step 2, and the results summarized in table 112. The data in the table indicate that the 

majority of the time the deflection was more or less consistent over time, which indicates 

that no deterioration was occurring or that no relationship existed between deflection and 

distress. Only 37 percent of the data indicate increasing or decreasing and then increasing 

deflection over time.

• Step 5: Although the deflection data do not show a general trend over time, the data were 

further investigated by comparing deflection with the condition and distress data 

measured over the same period. Since deflection, IRI, rut, and cracking data were not 

collected at the same time, the data were grouped and taken as a calendar year average 

for comparison. Example of such comparison is shown in figure 81 through figure 85. 

The data in the figures indicate that no trend exists between deflection and pavement 

condition or distress.
251

1 inch/mi = 0.0158 m/km.

1 mil = 25.4 microns.

Figure 81. Graph. Temperature-adjusted peak deflection at sensor 1 versus IRI for

SHRP test section 010101, F1.

1 inch = 25.4 mm.

1 mil = 25.4 microns.

Figure 82. Graph. Temperature-adjusted peak deflection at sensor 1 versus rut depth for

SHRP test section 010101, F1.
252

1 ft2 = 0.0929 m2.

1 mil = 25.4 microns.

Figure 83. Graph. Temperature-adjusted peak deflection at sensor 1 versus alligator 

cracking for SHRP test section 010101, F1.

1 ft = 0.305 m.

1 mil = 25.4 microns.

Figure 84. Graph. Temperature-adjusted peak deflection at sensor 1 versus longitudinal

cracking for SHRP test section 010101, F1.
253

1 ft = 0.305 m.

1 mil = 25.4 microns.

Figure 85. Graph. Temperature-adjusted peak deflection at sensor 1 versus transverse

cracking for SHRP test section 010101, F1.

Backcalculation of Pavement Layer Resilient Moduli Values

The measured and/or temperature-adjusted pavement deflection data, along with layer thickness 

and Poisson’s ratio, can be used to backcalculate the pavement layer moduli (MR) values. Such 

values are useful in the design of pavement rehabilitation treatments. To further develop and 

verify the temperature adjustment procedures described previously, the deflection data from a 

few test sections were used to backcalculate the layer MR values. The data used were from SHRP 

test sections 010101, 081053, 271028, and 351112. A partial record of the inventory data for the 

test sections used in this analysis are listed in table 108.

The MICHBACK software package, developed at Michigan State University, was used in the 

analyses to backcalculate pavement layer MR values. MICHBACK uses the Chevronx computer 

program (a five-layer elastic program) as the forward engine to calculate the pavement 

deflections for a given set of layer moduli, Poisson ratios, layer thicknesses, and load magnitude. 

The MICHBACK program uses a modified Newtonian algorithm to calculate a gradient matrix 

by incrementing the estimated layer modulus values and calculating the differences between the 

measured and the calculated pavement deflection in three consecutive cycles. When the 

convergence criteria (specified by the program user) are satisfied, the iteration process stops, and 

the final set of backcalculated layer moduli are recorded.(83)In this study, the following 

convergence criteria were used:
254

• Modulus tolerance: Maximum modulus tolerance (the difference between two 

successive backcalculated modulus values) of 1 percent.

• Root mean square (RMS) error: Maximum RMS error tolerance (the square root of the 

sum of squared errors between measured and calculated deflections) of 5 percent.

The pavement layers were assigned the following Poisson’s ratio values:

• AC—0.35.

• Granular base—0.40.

• Roadbed soil—0.45.

Note that the aggregate base and sand subbase layers were combined into a singular base layer. 

This was done to improve the accuracy of the backcalculation. 

The results of the backcalculation are listed in table 113. Note that for each location, three or 

four tests were performed during a 1-day period. Also note that the first set of four tests was

conducted at point location 114.3, the next four at point location 91.4, and the third four was the 

average for the entire test section. The first set of three tests was conducted at point location 

30.5, the next three at point location 7.6, and the final three at point location 7.6. The pavement 

surface temperature varied throughout each day of testing by as much as 82 oF (28 oC). Hence, 

the AC layer MR value varied throughout the day. Higher temperature corresponded to softer 

asphalt binder and lower MR. There were also some changes in the base layer MR values and 

minimal changes in the roadbed soil MR values. The changes in AC layer MR are shown as a 

function of pavement surface temperature for the analysis using both the measured and 

temperature-adjusted deflection data in figure 86 through figure 89. The data in the figures 

indicate that in general the AC layer MR values decreased with increasing temperature based on 

the measured deflection, while the values based on the temperature-adjusted deflection were 

opposite. Note that the two curves generally cross near the reference temperature of 70 °F 

(21 oC). This finding supports the use of the equation in figure 72 and indicates that the AC layer 

modulus values could be determined from the model of the backcalculated moduli values based 

on the measured or temperature-adjusted data by selecting the value at 70 °F (21 oC).
255

Table 113. Backcalculated resilient modulus values.

State 

Code

SHRP 

ID

FWD 

Test 

Date

FWD 

Test 

Time

Pavement 

Surface 

Temperature 

(°F)

Backcalculated Layer Resilient 

Modulus—Measured Deflection 

(psi)

Backcalculated Layer Resilient 

Modulus—Adjusted Deflection 

(psi)

Asphalt Base Roadbed Soil Asphalt Base Roadbed Soil

1 0101 4/16/96 0832 52 1,162,303 9,176 19,771 674,661 16,317 18,823

1 0101 4/16/96 1121 77 718,996 9,819 18,931 844,193 8,525 19,248

1 0101 4/16/96 1402 102 487,634 11,556 18,635 941,488 7,592 19,340

1 0101 4/16/96 1639 97 466,884 12,809 19,059 812,734 9,834 19,532

1 0101 4/16/96 0821 50 1,191,564 53,493 22,261 656,820 56,902 22,102

1 0101 4/16/96 1112 79 641,012 65,940 20,930 906,343 54,878 21,040

1 0101 4/16/96 1350 104 455,836 58,919 21,264 1,132,931 49,501 21,107

1 0101 4/16/96 1630 99 379,298 82,785 21,602 895,810 78,689 21,423

1 0101 4/16/96 0836 53 1,098,468 22,168 19,160 639,126 30,920 18,815

1 0101 4/16/96 1126 78 698,266 21,903 18,821 889,003 17,587 19,068

1 0101 4/16/96 1407 102 439,646 27,870 18,610 954,010 19,785 18,789

1 0101 4/16/96 1643 96 407,830 32,042 18,916 811,745 24,301 19,119

8 1053 3/7/97 0954 43 809,038 25,551 20,518 457,612 21,733 20,496

8 1053 3/7/97 1121 64 656,069 25,579 21,976 619,150 24,196 22,198

8 1053 3/7/97 1246 84 506,896 26,722 22,196 709,939 28,053 22,219

27 1028 10/8/96 0822 50 2,092,102 — 25,441 1,343,882 — 25,956

27 1028 10/8/96 1031 64 1,908,807 — 25,231 1,689,933 — 25,415

27 1028 10/8/96 1219 73 1,862,257 — 25,621 1,984,829 — 25,539

35 1112 4/25/95 0754 52 2,626,520 102,303 42,397 1,668,134 84,612 42,021

35 1112 4/25/95 1017 70 2,045,288 81,978 40,578 2,266,073 63,815 40,914

35 1112 4/25/95 1219 86 1,647,345 47,891 40,152 2,571,180 38,301 40,846

— Indicates not applicable.

oF = 1.8 × oC + 32.

1 psi = 6.895 kPa.
256

oF = 1.8 × oC + 32.

1 psi = 6.895 kPa.

Figure 86. Graph. AC layer MR values versus pavement surface temperature for SHRP test 

section 010101.

oF = 1.8 × oC + 32.

1 psi = 6.895 kPa

Figure 87. Graph. AC layer MR values versus pavement surface temperature for SHRP test 

section 081053.
257

oF = 1.8 × oC + 32.

1 psi = 6.895 kPa.

Figure 88. Graph. AC layer MR values versus pavement surface temperature for SHRP test 

section 271028.

oF = 1.8 × oC + 32.

1 psi = 6.895 kPa.

Figure 89. Graph. AC layer MR values versus pavement surface temperature for 

SHRP test section 351112.
258

The backcalculated MR values were further evaluated by plotting the results based on measured 

data versus those based on temperature-adjusted data, as shown in figure 90 and figure 91. The 

data in the figures indicate that minimal bias existed in the procedure for temperature adjustment 

across the various testing temperatures. Some data were reduced, and some data were increased 

following adjustment based on temperature (i.e., temperature adjustment did not consistently 

lead to increases or decreases in backcalculated MR values).

oF = 1.8 × oC + 32.

1 psi = 6.895 kPa.

Figure 90. Graph. AC layer MR values from measured and temperature-adjusted 

deflection data.
259

1 psi = 6.895 kPa.

Figure 91. Graph. Base and roadbed soil layer MR values from measured and temperatureadjusted deflection data.

Although the backcalculation of pavement layer MR values using temperature-adjusted pavement 

deflection was shown to work, many pavement engineers recommend performing backcalculation 

on measured data and then adjusting the results. This is because temperature adjustment in general 

is not exact, and it can introduce error. Such error can be magnified when the backcalculation 

routine is performed. Hence, making the adjustment at the end is preferred by many.

Rigid Pavement Deflection Versus Time

The measured rigid pavement deflections were evaluated over time using the following steps:

• Step 1: The deflection, treatment, and inventory data were downloaded from the LTPP 

Standard Data Release 28.0 for each of the SPS-2 test sections listed in table 114.

The data were sorted based on the applied load and the various test locations within the 

lane. Drop height 2 simulated the application of a 9,000-lbf (40-kN) half single axle load 

and was selected for analyses. The tests performed at the middle lane at mid-panel 

location (J1) were selected for individual analyses. All data from other test loads and 

locations were not included in the analyses.
260

Table 114. LTPP SPS-2 partial inventory data.

SHRP 

ID

PCC 

Thickness 

(Inches)

Base 

Thickness 

(Inches)

Subbase 

Thickness 

(Inches) Roadbed Soil Type

Most Recent 

Traffic 

(ESAL/Year)

Climatic 

Region

040213 7.9 5.8 — Coarse grained soil: silty sand with gravel 1,444,000 DNF

040214 8.3 6.1 — Coarse grained soil: silty sand with gravel 1,444,000 DNF

040215 11.0 6.3 — Coarse grained soil: silty sand with gravel 1,454,000 DNF

040216 11.2 6.3 — Coarse grained soil: silty sand with gravel 1,454,000 DNF

040217 8.1 6.1† — Coarse grained soil: silty sand with gravel 1,444,000 DNF

040218 8.3 6.2† — Coarse grained soil: clayey sand with gravel 1,444,000 DNF

040219 10.8 6.2† — Coarse grained soil: silty sand with gravel 1,453,000 DNF

040220 11.2 6.2† — Coarse grained soil: clayey sand with gravel 1,454,000 DNF

040221 8.1 8.4* — Coarse grained soil: silty sand with gravel 1,444,000 DNF

040222 8.6 8.2* — Coarse grained soil: clayey sand with gravel 1,445,000 DNF

040223 11.1 7.6* — Coarse grained soil: silty sand with gravel 1,453,000 DNF

040224 10.6 8.2* — Coarse grained soil: clayey sand with gravel 1,452,000 DNF

040262 8.1 6.1 — Coarse grained soil: silty sand with gravel 1,444,000 DNF

040263 8.2 8.3* — Coarse grained soil: clayey sand with gravel 1,444,000 DNF

040264 11.5 8.2* — Coarse grained soil: clayey sand with gravel 1,454,000 DNF

040265 10.8 6.8 — Coarse grained soil: clayey sand with gravel 1,453,000 DNF

040266 12.3 3.9† — Coarse grained soil: clayey sand with gravel 1,455,000 DNF

040267 11.3 3.9† — Coarse grained soil: clayey sand with gravel 1,454,000 DNF

040268 8.5 3.8† — Coarse grained soil: clayey sand with gravel 1,445,000 DNF

080213 8.6 5.9 — Fine-grained soil: clay with sand 463,000 DF

080214 8.4 5.9 — Coarse-grained soil: clayey sand with gravel 462,000 DF

080215 11.5 6.0 — Fine-grained soil: sandy clay 470,000 DF

080216 11.9 5.9 — Coarse-grained soil: poorly graded sand with silt Not reported DF

080217 8.6 6.7 — Fine-grained soil: sandy lean clay 463,000 DF

080218 7.6 6.2† — Fine-grained soil: clay 459,000 DF
261

SHRP 

ID

PCC 

Thickness 

(Inches)

Base 

Thickness 

(Inches)

Subbase 

Thickness 

(Inches) Roadbed Soil Type

Most Recent 

Traffic 

(ESAL/Year)

Climatic 

Region

080219 9.9 6.1† — Coarse-grained soil: clayey sand with gravel 467,000 DF

080220 11.2 6.2† — Fine-grained soil: clay 469,000 DF

080221 8.3 8.0* — Fine-grained soil: sandy lean clay 461,000 DF

080222 8.5 8.5* — Fine-grained soil: sandy clay 462,000 DF

080223 11.7 8.9* — Coarse-grained soil: well graded sand with silt 470,000 DF

080224 11.6 7.7* — Coarse-grained soil: clayey sand 470,000 DF

080259 11.9 — — Coarse-grained soil: clayey sand 470,000 DF

100201 8.3 6.2 34.0 Coarse-grained soil: silty sand 368,000 WNF

100202 8.8 6.2 22.0 Coarse-grained soil: well graded sand with silt 374,000 WNF

100203 11.7 6.1 14.0 Coarse-grained soil: silty sand 396,000 WNF

100204 11.0 6.3 12.0 Coarse-grained soil: well graded sand with silt 392,000 WNF

100205 9.2 5.5† 30.0 Coarse-grained soil: silty sand 378,000 WNF

100206 8.9 6.1† 42.0 Coarse-grained soil: well graded sand with silt 375,000 WNF

100207 11.3 6.9† 14.1 Coarse-grained soil: silty sand 394,000 WNF

100208 12.1 6.0† 30.0 Coarse-grained soil: sand 397,000 WNF

100209 8.2 8.0* 24.0 Coarse grained soil: clayey sand 367,000 WNF

100210 8.3 8.2* 36.0 Coarse-grained soil: silty sand 368,000 WNF

100211 11.8 7.5* 30.0 Coarse-grained soil: clayey sand 396,000 WNF

100212 12.4 7.4 30.0 Coarse-grained soil: silty sand 398,000 WNF

100259 10.2 7.9 42.0 Coarse-grained soil: well graded sand with silt 387,000 WNF

100260 10.2 7.8 42.0 Coarse-grained soil: silty sand 387,000 WNF

260213 8.3 6.1 18.5 Fine-grained soil: silty clay 2,042,000 WF

260214 8.8 5.8 18.5 Fine-grained soil: silty clay 1,481,000 WF

260215 11.1 6.2 15.5 Fine-grained soil: silty clay 2,129,000 WF

260216 11.3 5.9 15.5 Fine-grained soil: silty clay 1,550,000 WF

260217 8.4 6.1† 18.5 Fine-grained soil: silty clay 2,047,000 WF

260218 7.3 6.3† 18.5 Fine-grained soil: silty clay 1,895,000 WF
262

SHRP 

ID

PCC 

Thickness 

(Inches)

Base 

Thickness 

(Inches)

Subbase 

Thickness 

(Inches) Roadbed Soil Type

Most Recent 

Traffic 

(ESAL/Year)

Climatic 

Region

260219 11.3 5.9† 15.5 Fine-grained soil: silty clay 1,550,000 WF

260220 11.2 5.8† 15.5 Fine-grained soil: silty clay 1,549,000 WF

260221 8.1 8.6* 16.5 Fine-grained soil: silty clay 1,513,000 WF

260222 8.3 8.4* 16.5 Fine-grained soil: silty clay 1,615,000 WF

260223 11.0 8.4* 13.5 Fine-grained soil: sandy clay 1,548,000 WF

260224 11.1 8.3* 13.5 Fine-grained soil: sandy clay 1,549,000 WF

260259 11.3 8.0* 18.0 Fine-grained soil: silty clay 1,550,000 WF

— Indicates no base layer was present.

*Upper portion of base was treated.

†Treated base.

1 inch = 25.4 mm.
263

• Step 2: On any particular test day, a series of four tests were conducted at drop height 2 

for each of about 10 locations along the test section. The deflection tests conducted on a 

test section in each specific testing day were averaged to simulate the data that would be 

collected by a State transportation department (one test per section). Deflection data 

collected after the application of a treatment were not included in the analyses.

• Step 3: The measured deflection data were plotted against time as shown in figure 92 for 

sensor 1. The data in the figure indicate that the deflection increased and then decreased, 

and overall decreased over the 4-year period of data collection, with values of about 4.33 to 

5.91 to 3.94 inches (110 to 150 to 100 microns). Localized fluctuations occurred due to 

moisture conditions at the time of testing and instrument and measurement variability.

1 mil = 25.4 microns.

Figure 92. Graph. Measured peak deflection at sensor 1 versus time for SHRP test section 

100201, J1.

• Step 4: The measured deflection versus time plots were visually evaluated, and the 

results are summarized in table 115. The data in the table indicate that 45 percent of the 

test sections showed increasing deflection over time, 27 percent showed more or less 

consistent deflection, and 27 percent showed decreasing deflection over time. 

Consequently, the time-dependent deflection measured over a 4-year period cannot be 

used as an indication of pavement deterioration or pavement distress.
264

Table 115. Summary of deflection for sensor 1 versus time trend descriptions for

LTPP SPS-2.

State

Trend Description

Increasing Consistent Decreasing

No. of 

Test 

Section

s

Percentag

e

No. of 

Test 

Section

s

Percentag

e

No. of 

Test 

Section

s Percentage

Arizona 10 53 5 26 4 21

Colorado 2 15 6 46 5 38

Delaware 5 42 5 42 2 17

Michigan 8 57 2 14 4 29

Total sections and 

average percent

25 45 15 27 15 27

• Step 5: Although the deflection data did not show a consistent trend over time, the data 

were further investigated by comparing the deflection with the pavement condition and 

distress data measured over the same time period. Because deflection, IRI, faulting, and 

cracking data were not collected at the same time, the data were grouped and taken as a 

calendar year average for comparison. Examples of such comparisons are shown in 

figure 93 through figure 96. The data in the figures indicate there was no trend in 

relationship between deflection and pavement condition or distress. Similar results were 

found for the other test sections.

1 inch/mi = 0.0158 m/km.

1 mil = 25.4 microns.

Figure 93. Graph. Measured peak deflection at sensor 1 versus IRI for SHRP test section 

100201, J1.
265

1 inch = 25.4 mm.

1 mil = 25.4 microns.

Figure 94. Graph. Measured peak deflection at sensor 1 versus faulting for

SHRP test section 100201, J1.

1 ft = 0.305 m.

1 mil = 25.4 microns.

Figure 95. Graph. Measured peak deflection at sensor 1 versus longitudinal cracking for

SHRP test section 100201, J1.
266

1 ft = 0.305 m.

1 mil = 25.4 microns.

Figure 96. Graph. Measured peak deflection at sensor 1 versus transverse cracking for

SHRP test section 100201, J1.

The pavement deflection data could be used to estimate the pavement response to load and for 

the design of pavement treatments, etc. The LTPP rigid pavement deflection data did not support 

the use of deflection as an indicator of impending surface distress. Therefore, deflection could 

not be used in this study as a component of RFP or RSP.

Rigid Pavement LTE Versus Time

The LTEs calculated based on FWD testing were evaluated over time using the following steps:

• Step 1: The LTE, treatment, and inventory data were downloaded from the LTPP 

Standard Data Release 28.0 for each of the SPS-2 test sections listed in table 114.

• Step 2: The data were sorted based on the applied load and the various test locations 

within the lane. Drop height 2 simulated the application of a 9,000-lbf (40-kN) half single 

axle load and was selected for analyses. The tests performed in the outer wheelpath at 

joint approach locations (J4) were selected for individual analyses. All data from other 

test loads and locations were not included in the analyses.

• Step 3: On any particular test day, a series of four tests were conducted at drop height 2 

for each of about 10 locations along the test section. The various LTE tests conducted on 

a test section in each specific testing day were averaged to simulate the data that would 

be collected by a State transportation department (only one test per section). LTE data 

collected after the application of a treatment were not included in the analyses.
267

• Step 4: The LTE data were then plotted as a function of time. An example of such a plot 

is provided in figure 97. The data in the figure indicate that the LTE generally increased 

over the 4-year period of data collection, ranging from about 88 to 98 percent. Some 

localized fluctuations occurred as a result of the moisture conditions at the time of testing 

along with instrument and measurement variability.

Figure 97. Graph. LTE versus time for SHRP test section 100201, J4.

• Step 5: The LTE versus time plots were visually evaluated and the results are summarized 

in table 116. The data in the table indicate that 21 percent of the test sections showed 

increases in the LTE over time, 30 percent showed consistent LTE, and 48 percent showed 

decreases in the LTE over time. Once again, statistically speaking, the LTPP LTE data 

could not be used as an indicator of pavement distress or deterioration.

Table 116. Summary of LTE versus time trend descriptions.

State

Trend Description

Increasing Consistent Decreasing

No. of 

Test 

Section

s Percentage

No. of 

Test 

Section

s

Percentag

e

No. of 

Test 

Section

s

Percentag

e

Arizona 2 11 3 17 13 72

Colorado 3 23 7 54 3 23

Delaware 3 25 4 33 5 42

Michigan 4 31 3 23 6 46

Total sections and

average percent

12 21 17 30 27 48

• Step 6: Although the LTE data did not show a consistent trend over time, the data were 

further investigated by comparing LTE with the condition and distress data measured 
268

over the same time period. Because the LTE, IRI, faulting, and cracking data were not 

collected at the same time, the data were grouped and taken as a calendar year average 

for comparison purposes. Examples of such comparisons are shown in figure 98 through 

figure 101. The data in the figures indicate that there was no trend in the relationship 

between LTE and pavement condition or distress. Similar results were found for the other 

test sections.

1 inch/mi = 0.0158 m/km.

Figure 98. Graph. LTE versus IRI for SHRP test section 100201, J4.

1 inch = 25.4 mm.
269

Figure 99. Graph. LTE versus faulting for SHRP test section 100201, J4.

1 ft = 0.305 m.

Figure 100. Graph. LTE versus longitudinal cracking for SHRP test section 100201, J4.

1 ft = 0.305 m.

Figure 101. Graph. LTE versus transverse cracking for SHRP test section 100201, J4.
270

SUMMARY, CONCLUSIONS, AND RECOMMENDATIONS

This chapter presented the results of the analyses of the measured flexible and rigid pavement 

deflections and the LTE data. The main objective of the analyses was to determine whether 

pavement deflections and LTE data could be incorporated into the RFP and RSP algorithms.

It was envisioned that measured pavement deflections and/or LTE data would be correlated with 

pavement condition or distress, and hence, they could be used as an early warning of pending 

deterioration before it became visible on the pavement surface.

For flexible pavements, the measured pavement deflections were a function of the pavement 

temperatures. Therefore, the accuracy of the existing temperature adjustment methods were 

reviewed and scrutinized using the measured LTPP deflection data. Based on the results, a new 

global temperature adjustment methodology was developed that could be applied to all deflection 

sensors and all climatic regions.

Based on the results of the analyses, the following conclusions and recommendations were drawn:

• The LTPP deflection data collected for the SMP test sections did not support the use of the 

AI deflection temperature adjustment procedure. The equation in figure 72 was developed 

to more accurately adjust the measured deflection based on the asphalt surface temperature. 

It is recommended that the equation in figure 72 be used to adjust the measured flexible 

pavement deflections based on the measured pavement surface temperature.

• The LTPP deflection data collected for the SMP test sections did not support the 

inclusion of the deflection data in the RFP and RSP algorithms. It is recommended that 

the deflection data for flexible pavement be excluded from the RFP and RSP algorithms 

for the LTPP data.

• The LTPP deflection data collected for the SMP test sections did not correlate with the 

measured pavement condition and distress data. It is recommended that the flexible 

pavement deflection data not be used as an indication of impending pavement distress.

• The backcalculated AC layer modulus values of the selected LTPP test sections were 

dependent on the pavement surface temperature. To estimate the AC modulus at the 

standard temperature of 21 oC, two procedures are recommended:

o Adjust the measured deflection data to 21 oC using the equation in figure 72 and then 

backcalculate the layer moduli.

o During 1 day, conduct a minimum of three FWD tests and measure the pavement 

deflection at various times. Two FWD tests should be conducted at pavement 

temperatures below or above 70 °F (21 oC) and the third test on the other side of 

70 °F (21 oC). Backcalculate the AC modulus value for each of the three FWD tests. 

Plot the modulus as a function of the measured pavement temperature. From the 

graph, estimate the AC modulus at 70 °F (21 oC).
271

• The LTPP deflection and LTE data collected for the SPS-2 test sections in Arizona, 

Colorado, Delaware, and Michigan did not support modeling of the data as a function of 

time. It is recommended that the deflection and LTE data for rigid pavement be excluded 

from the RFP and RSP algorithms.

• The LTPP deflection and LTE data collected for the SPS-2 test sections in Arizona, 

Colorado, Delaware, and Michigan did not support the correlation of deflection or LTE 

data with the measured pavement condition and distress data. It is recommended that the 

deflection and LTE data for rigid pavement not be used as indication of impending distress.

• It is further recommended that, for new LTPP experiments, the deflection data should be 

collected over a longer period of time (such as 10 to 20 years), and deflection data should 

be measured before and after treatments are applied.

• It is recommended that further research should be conducted to allow the incorporation of 

deflection data into the RFP and RSP algorithms. An early indicator of impending 

distress could prove to be very helpful to State transportation departments to allow them 

to treat the pavement before surface distress manifestation. In this study, although no 

relationships could be established based on the LTPP deflection data, future research

could uncover such relationships.

273

CHAPTER 8. STATE DATA ANALYSES 

BACKGROUND

Results of the analyses of the LTPP time-series pavement condition and distress data measured 

along flexible and rigid pavement test sections were presented and discussed in previous 

chapters. The results are presented based on treatment type, climatic regions, and various other 

factors. This chapter addresses the similarities and differences between the LTPP data and the 

pavement condition and distress data measured by Colorado, Louisiana, and Washington along 

various pavement segments of their respective pavement networks. The following differences 

between the LTPP and the State data were observed:

• The LTPP data were measured along a 500-ft (152.4-m)-long test and control sections 

located throughout the United States and Canada, whereas the State data were measured 

along 0.5-mi (0.8-km)-long to 8-mi (13-km)-long pavement projects and stored in the 

databases for each 0.1-mi (0.16-km)-long pavement segment along the project. Thus, the 

data for an 8-mi (13-km)-long pavement project were stored in 80 different fields, 1 field 

per 0.1 mi (0.16 km).

• The units of measurement used by the LTPP Program were not the same as those used by 

the State transportation departments. For example, the LTPP unit of measurement for IRI 

was m/km, while it was inches/mi for the States. Therefore, before the analyses of the 

State data, the data were converted to the same units as the LTPP data.

• The pavement condition and distress along one single LTPP test section represented one 

single data point in time, whereas for each pavement project, the State data contained as 

many data points at one time as the number of 0.1-mi (0.16-km)-long pavement segments 

along the project. For example, the data from one survey of an 8-mi (13-km)-long

pavement project was equivalent to 80 LTPP test sections.

• The State transportation departments’ databases were based on their distress 

identification definitions and procedures, which may or may not have been compatible 

with the Distress Identification Manual for the Long-Term Pavement Performance 

Program.

(80)

• The State transportation department’s databases lacked details of the types, 

classifications, and properties of the pavement layers and roadbed soils.

The LTPP data contained the pavement conditions and distresses of flexible, rigid and composite 

pavements test and control sections. Although the data from CDOT, LADOTD, and WSDOT

contained the same data, the number of rigid and composite pavement projects that received 

treatments for which the database contained three or more data points was very limited. Hence, it 

was decided to limit the comparison to flexible pavement sections only.

The research team developed a step-by-step procedure to mine, handle, organize, and analyze the 

data based on one of the objectives of this study. This objective was to compare the results of the 
274

analyses of the LTPP and State data and to determine whether the methodologies used in the 

analyses of the LTPP data applied equally to the State data. Therefore, the pavement condition 

and distress data for the pavement networks of CDOT, LADOTD, and WSDOT were requested, 

received, organized, and subjected to the same types of analyses as the LTPP data using the stepby-step procedure detailed in the next section.

ANALYSIS STEPS

This section presents the steps of the procedure used in the analyses of the State. These steps are 

similar to those used in the analyses of the LTPP data. The difference is that the LTPP test 

sections were analyzed individually. On the other hand, a State pavement project consisted of 

many 0.1-mi (0.16-km)-long pavement segments where the pavement condition and distress 

varied substantially along the project. Although each 0.1-mi (0.16-km)-long pavement segment 

along a given pavement project was analyzed individually, results of the analyses of each project 

in a State transportation department that received the same treatment were grouped into the fiveCS system based on the RFPs and RSPs of each 0.1-mi (0.16-km) segment. The system was 

developed in this study and presented in chapter 3 (Pavement Condition Classification) of this 

report. Finally, the benefits of a given treatment type were calculated as the weighted average 

benefits of each 0.1-mi (0.16-km)-long pavement segment in each State using RFP or RSP, CFP 

or CSP, and FCROP or SCROP. For the LTPP data, the benefits were calculated using the same 

parameters based on the weighted average benefits of each test section that received the same 

treatment type and located in any of the four climatic regions. The analyses procedure used the 

following steps:

• Step 1: The pavement condition and distress data were converted into Microsoft® Excel 

spreadsheet format and separated per pavement type.

• Step 2: For each pavement network, the treatment data were searched, and each 0.1-mi

(0.16-km)-long segment of several pavement projects that received one of the following 

treatments were identified, copied, and stored in a separate Microsoft® Excel datasheet:

o Thin overlay (≤ 2.5 inches (63.6 mm)).

o Thick overlay (> 2.5 inches (63.6 mm)).

o Thin mill and fill (≤ 2.5 inches (63.6 mm)).

o Thick mill and fill (> 2.5 inches (63.6 mm)).

o Single chip seal.

• Step 3: The pavement condition and distress data for each identified 0.1-mi (0.16-km)-

long pavement segment were examined to determine whether the segment had a 

minimum of three time-series data that could be modeled using the proper mathematical 

function. Those segments that did not pass the test were not included in the analyses. 

Table 117 summarizes the number of 0.1-mi (0.16-km)-long pavement segments that 

were accepted for analyses for each treatment type in each State. The table also lists the 

number of LTPP test sections that received similar treatment type and were analyzed. The 
275

results of these LTPP test sections were compared with those of the 0.1-mi (0.16-km)-

long) pavement segments.

Table 117. Number of 0.1-mi (0.16-km)-long pavement segments and LTPP test sections for 

each treatment type available for analyses.

Treatment 

Type Data Source

Number of 0.1-Mi (0.16-Km)-long Pavement Segments and 

LTPP Test Sections Available for Analyses

IRI

Rut 

Depth

Alligator 

Cracking

Longitudinal 

Cracking

Transverse 

Cracking

Thin 

overlay

Washington 349 709 1,746 1,000 1,538

Colorado 94 126 128 129 70

Louisiana 219 224 202 71 134

SPS-3 and -5 36 35 34 40 37

GPS-5 25 19 7 7 13

Thick 

overlay

Washington 10 122 403 310 220

Colorado ND ND ND ND ND

Louisiana 1,416 1,242 1,199 595 984

GPS-5 14 15 10 15 13

GPS-6 15 13 5 2 6

Thin mill 

and fill

Washington 123 701 886 357 633

Colorado 28 74 49 38 24

Louisiana 163 191 146 80 135

GPS-5 13 13 13 17 16

GPS-6 27 33 9 22 6

Thick mill 

and fill

Washington ND ND ND ND ND

Colorado ND ND ND ND ND

Louisiana 735 957 605 286 396

GPS-5 14 14 13 15 14

GPS-6 12 13 3 3 4

Chip seal

Washington 52 38 156 111 194

Colorado 50 12 43 35 52

Louisiana 1,089 574 1,605 772 819

SPS-3 21 22 18 21 17

ND = No data.

• Step 4: The data for each 0.1-mi (0.16-km)-long pavement segment of each project were 

analyzed, and RFPs and RSPs before treatment were calculated.

• Step 5: For each 0.1-mi (0.16-km)-long pavement segment, the treatment benefits were 

calculated in terms of RFP or RSP after treatment, CFPs or CSPs, and FCROPs or 

SCROPs. The treatment benefits were then compared with the treatment benefits 

obtained from the LTPP test sections. One issue that should be noted is that the history of 

the 0.1-mi (0.16-km)-long pavement segments and the treatment dates were different 

from one pavement project to another and from one LTPP test section to another. To 

compare the benefits using an equivalent reference, RFP and RSP were calculated from 

the treatment time to the time when the pavement reached the prespecified threshold 
276

value. Stated differently, the calculated RFP and RSP of each 0.1-mi (0.16-km)-long

pavement segment and of the LTPP test sections represented the time in years from the 

treatment date to the time when the prespecified threshold value was reached. Further, the 

same threshold values were used in the analyses of the State and the LTPP data.

• Step 6: The results of the analyses of the 0.1-mi (0.16-km)-long pavement segments were 

grouped into six T

2Ms, which were submitted to FHWA and are available from the LTPP 

Customer Support Services.(79) The T2Ms list the before and after treatment CSs for all 

0.1-mi (0.16-km)-long pavement segments that were analyzed. The T2Ms also list the 

benefits of the treatments in term of RFP/RSP, CFP/CSP, and FCROP/SCROP and their 

averages.

• Step 7: Results of the analyses of all LTPP test sections (the numbers are also listed in 

table 117) that were subjected to one of the previously listed treatments and located in 

any climatic region were grouped for each pavement condition and distress type. The 

weighted average benefits in terms of RFP/RSP, CFP/CSP, and FCROP/SCROP were 

then calculated.

• Step 8: The two sets of benefits were then compared per pavement condition and distress 

type, as detailed in the following sections.

IRI

Table 118 summarizes the calculated benefits (in terms of IRI) for all 0.1-mi (0.16-km)-long 

pavement segments in each State transportation department that received the indicated treatment 

type and the comparable LTPP test sections. The table also lists the number of 0.1-mi 

(0.16-km)-long segments and the number of LTPP test sections involved in the analyses. For 

ease of visual comparison of the benefits, they were plotted in bar graph format as shown in 

figure 102 through figure 104. Examination of the three figures indicated that the benefits, for

IRI in terms of the RFP, CFP, and FCROP of each treatment type of the LTPP SPS and GPS test 

sections and of the 0.1-mi (0.16-km)-long pavement segments in each of the cited State 

transportation departments, were very similar. The conclusions and recommendations based on 

these observations are included in a separate section at the end of this chapter.
277

Table 118. Comparison of the weighted average treatment benefits based on IRI of 

five treatment types performed on LTPP test sections and on pavement projects of CDOT, 

LADOTD, and WSDOT. 

Treatment Type Data Source

Number of 

0.1-Mi (0.16-Km)-Long

Pavement Segments

Treatment Benefits (Year)

RFP CFP FCROP

Thin overlay

Washington 349 19 9 13

Colorado 94 11 1 3

Louisiana 219 18 14 17

SPS-3 and -5 36 18 11 11

GPS-6 25 19 10 13

Thick overlay

Washington 10 20 4 10

Louisiana 1,416 19 14 18

SPS-5 14 20 7 18

GPS-6 15 20 14 16

Thin mill and fill

Washington 123 19 7 14

Colorado 28 14 8 10

Louisiana 163 18 11 15

SPS-5 13 20 8 19

GPS-6 27 18 7 6

Thick mill and fill

Louisiana 735 18 12 16

SPS-5 14 20 8 19

GPS-6 12 20 12 15

Chip seal

Washington 52 12 4 2

Colorado 50 16 4 0

Louisiana 1,089 12 2 1

SPS-3 21 15 2 4
278

Figure 102. Graph. Comparison of the weighted average RFP based on IRI of five 

treatment types performed on LTPP test sections and on various pavement projects of 

CDOT, LADOTD, and WSDOT.

Figure 103. Graph. Comparison of the weighted average CFP based on IRI of five 

treatment types performed on LTPP test sections and on various pavement projects of

CDOT, LADOTD, and WSDOT.
279

Figure 104. Graph. Comparison of the weighted average FCROP based on IRI of five 

treatment types performed on LTPP test sections and on various pavement projects of

CDOT, LADOTD, and WSDOT.

RUT DEPTH

Table 119 summarizes the calculated benefits (in terms of rut depth) for all 0.1-mi (0.16-km)-long 

pavement segments in each cited State transportation department that received the indicated 

treatment type and the comparable LTPP test sections. The table also lists the number of 0.1-mi 

(0.16-km)-long segments and the number of LTPP test sections involved in the analyses. For ease 

of visual comparison of the benefits, they were plotted in bar graph format as shown in figure 105

through figure 107. Examination of the three figures indicated that the benefits, in terms of the 

RFP/RSP, CFP/CSP, and FCROP/SCROP of each treatment type of the LTPP test sections and of 

the 0.1-mi (0.16-km)-long pavement segments in each of the cited State transportation 

departments were very similar. Once again, the conclusions and recommendations based on these 

observations are included in a separate section at the end of this chapter.
280

Table 119. Comparison of the weighted average treatment benefits based on rut depth of 

five treatment types performed on LTPP test sections and on pavement projects of CDOT, 

LADOTD, and WSDOT.

Treatment 

Type Data Source

Rut Depth

Number of 0.1-Mi 

(0.16-Km)-Long 

Pavement 

Segments

RFP/RS

P (Year)

CFP/CS

P (Year)

FCROP/SCROP 

(Year)

Thin 

overlay

Washington 709 20 6 15

Colorado 126 14 4 7

Louisiana 224 20 4 18

SPS-3 and -5 35 19 10 16

GPS-6 19 20 11 18

Thick 

overlay

Washington 122 20 8 15

Louisiana 1,242 20 6 10

SPS-5 15 18 7 14

GPS-6 13 19 11 18

Thin mill 

and fill

Washington 701 19 8 16

Colorado 74 20 6 14

Louisiana 191 20 14 19

SPS-5 13 17 8 17

GPS-6 33 19 14 17

Thick mill 

and fill

Louisiana 957 18 9 14

SPS-5 14 18 7 17

GPS-6 13 20 12 18

Chip seal

Washington 38 20 1 9

Colorado 12 19 3 0

Louisiana 574 19 6 8

SPS-3 22 15 4 11
281

Figure 105. Graph. Comparison of the weighted average RFP/RSP based on rut depth of 

five treatment types performed on LTPP test sections and on pavement projects of CDOT, 

LADOTD, and WSDOT.

Figure 106. Graph. Comparison of the weighted average CFP/CSP based on rut depth of 

five treatment types performed on LTPP test sections and on pavement projects of CDOT, 

LADOTD, and WSDOT.
282

Figure 107. Graph. Comparison of the weighted average FCROP/SCROP based on rut 

depth of five treatment types performed on LTPP test sections and on pavement projects of 

CDOT, LADOTD, and WSDOT.

ALLIGATOR CRACKING

Table 120 summarizes the average calculated benefits (relative to alligator cracking) for all 

0.1-mi (0.16-km)-long pavement segments in each cited State transportation department that 

received the indicated treatment type and the comparable LTPP test sections. The table also lists 

the number of 0.1-mi (0.16-km)-long segments and the number of LTPP test sections involved in 

the analyses. For ease of visual comparison of the benefits, they were plotted in bar graph format 

as shown in figure 108 through figure 110. Examination of the three figures indicated that the 

benefits of each treatment type of the LTPP test sections and of the 0.1-mi (0.16-km)-long

pavement segments in each of the cited State transportation departments were very similar. The 

conclusions and recommendations based on these observations are included in a separate section 

at the end of this chapter.
283

Table 120. Comparison of the weighted average treatment benefits based on alligator 

cracking of five treatment types performed on LTPP test sections and on pavement 

projects of CDOT, LADOTD, and WSDOT.

Treatment Type Data Source

Alligator Cracking

Number of 0.1-Mi 

(0.16-km)-Long

Pavement 

Segments

RSP 

(Years)

CSP 

(Years)

SCROP 

(Years)

Thin overlay

Washington 1,746 18 6 13

Colorado 128 9 6 0

Louisiana 202 11 9 10

SPS-3 and -5 34 12 5 6

GPS-6 7 16 14 15

Thick overlay

Washington 403 18 5 14

Louisiana 1,199 15 13 15

SPS-5 10 14 7 10

GPS-6 5 14 9 12

Thin mill and fill

Washington 886 18 2 10

Colorado 49 11 3 7

Louisiana 146 18 9 13

SPS-5 13 15 7 14

GPS-6 9 9 7 11

Thick mill and fill

Louisiana 605 17 15 17

SPS-5 13 15 7 11

GPS-6 3 12 11 13

Chip seal

Washington 156 19 2 7

Colorado 43 10 7 5

Louisiana 1,605 10 8 9

SPS-3 18 11 1 6
284

Figure 108. Graph. Comparison of the weighted average RSP based on alligator cracking 

of five treatment types performed on LTPP test sections and on pavement projects of 

CDOT, LADOTD, and WSDOT.

Figure 109. Graph. Comparison of the weighted average CSP based on alligator cracking 

of five treatment types performed on LTPP test sections and on pavement projects of 

CDOT, LADOTD, and WSDOT.
285

Figure 110. Graph. Comparison of the weighted average SCROP based on alligator 

cracking of five treatment types performed on LTPP test sections and on pavement 

projects of CDOT, LADOTD, and WSDOT.

LONGITUDINAL CRACKING

Table 121 summarizes the average calculated benefits (in terms of longitudinal cracking) for all 

0.1-mi (0.16-km)-long pavement segments in each cited State transportation department that 

received the indicated treatment. For ease of visual comparison of the benefits, they were plotted 

in bar graph format as shown in figure 111 through figure 113. Examination of the three figures 

indicated that the benefits of each treatment type of the LTPP test sections and of the 0.1-mi 

(0.16-km)-long pavement segments in each of the cited State transportation departments were 

very similar. The conclusions and recommendations based on these observations are included in 

a separate section at the end of this chapter.
286

Table 121. Comparison of the weighted average treatment benefits based on longitudinal 

cracking of five treatment types performed on LTPP test sections and on pavement 

projects of CDOT, LADOTD, and WSDOT.

Treatment Type Data Source

Longitudinal Cracking

Number of 0.1-Mi 

(0.16-Km)-Long 

Pavement 

Segments

RSP 

(Years)

CSP 

(Years)

SCROP 

(Years)

Thin overlay

Washington 1,000 18 4 13

Colorado 129 11 2 1

Louisiana 71 17 8 11

SPS-3 and -5 40 13 1 5

GPS-6 7 10 8 10

Thick overlay

Washington 310 19 0 14

Louisiana 595 17 7 11

SPS-5 15 14 3 4

GPS-6 2 8 7 4

Thin mill and fill

Washington 357 18 4 9

Colorado 38 9 2 4

Louisiana 80 18 11 12

SPS-5 17 14 2 6

GPS-6 22 7 5 4

Thick mill and fill

Louisiana 286 16 7 13

SPS-5 15 15 4 6

GPS-6 3 15 9 10

Chip seal

Washington 111 19 4 10

Colorado 35 13 10 5

Louisiana 772 17 9 11

SPS-3 21 18 1 8
287

Figure 111. Graph. Comparison of the weighted average RSP based on longitudinal 

cracking of five treatment types performed on LTPP test sections and on pavement 

projects of CDOT, LADOTD, and WSDOT.

Figure 112. Graph. Comparison of the weighted average CSP based on longitudinal 

cracking of five treatment types performed on LTPP test sections and on pavement 

projects of CDOT, LADOTD, and WSDOT.
288

Figure 113. Graph. Comparison of the weighted average SCROP based on longitudinal 

cracking of five treatment types performed on LTPP test sections and on pavement 

projects of CDOT, LADOTD, and WSDOT.

TRANSVERSE CRACKING

Table 122 summarizes the average calculated benefits (relative to transverse cracking) for all 

0.1-mi (0.16-km)-long pavement segments in each cited State transportation department that 

received the indicated treatment type and the comparable LTPP test sections. The table also lists 

the number of 0.1-mi (0.16-km)-long segments and the number of LTPP test sections involved in 

the analyses. For ease of visual comparison of the benefits, they were plotted in bar graph format 

as shown in figure 114 through figure 116. Examination of the three figures indicated that the 

benefits, in terms of RSP, CSP, and SCROP of each treatment type of the LTPP test sections and 

of the 0.1-mi (0.16-km)-long pavement segments in each of the cited State transportation 

departments were very similar. The conclusions and recommendations based on these observations 

are included in a separate section at the end of this chapter.
289

Table 122. Comparison of the weighted average treatment benefits based on transverse 

cracking of five treatment types performed on LTPP test sections and on pavement 

projects of CDOT, LADOTD, and WSDOT.

Treatment Type Data Source

Transverse Cracking

Number of 

0.1-Mi

(0.16-Km)-Long 

Pavement 

Segments

RSP 

(Year)

CSP 

(Year)

SCROP 

(Year)

Thin overlay

Washington 1,538 19 2 12

Colorado 70 13 4 4

Louisiana 134 11 3 7

SPS-3 and -5 37 13 0 7

GPS-6 13 12 6 8

Thick overlay

Washington 220 20 2 17

Louisiana 984 14 8 10

SPS-5 13 16 3 11

GPS-6 6 14 11 12

Thin mill and fill

Washington 633 19 2 11

Colorado 24 8 4 1

Louisiana 135 15 9 12

SPS-5 16 16 2 9

GPS-6 6 11 5 5

Thick mill and fill

Louisiana 396 16 12 15

SPS-5 14 17 4 11

GPS-6 4 16 7 14

Chip seal

Washington 194 20 0 5

Colorado 52 7 3 3

Louisiana 819 13 9 9

SPS-3 17 12 2 5
290

Figure 114. Graph. Comparison of the weighted average RSP based on transverse cracking 

of five treatment types performed on LTPP test sections and on pavement projects of 

CDOT, LADOTD, and WSDOT.

Figure 115. Graph. Comparison of the weighted average CSP based on transverse cracking 

of five treatment types performed on LTPP test sections and on pavement projects of 

CDOT, LADOTD, and WSDOT.
291

Figure 116. Graph. Comparison of the weighted average SCROP based on transverse 

cracking of five treatment types performed on LTPP test sections and on pavement 

projects of CDOT, LADOTD, and WSDOT.

SUMMARY, CONCLUSIONS, AND RECOMMENDATIONS

Pavement condition and distress databases of three pavement networks were requested and 

received from three State transportation departments—CDOT, LADOTD, and WSDOT. Each 

database was searched and pavement projects that received one of the following five treatment 

types were identified.

• Thin overlay (≤ 2.5 inches (63.5 mm)).

• Thick overlay (> 2.5 inches (63.5 mm)).

• Thin mill and fill (≤ 2.5 inches (63.5 mm)).

• Thick mill and fill (> 2.5 inches (63.5 mm)).

• Single chip seal.

The pavement condition and distress data for each of the 0.1-mi (0.16-km)-long pavement 

segments along each selected pavement project that was treated using one of these five 

treatments was analyzed. Results of the analyses included RFPs and RSPs before and after 

treatment, CFPs and CSPs after treatment, and FCROPs and SCROPs after treatment. The 

pavement segments of all pavement projects in one State transportation department that received 

the same treatment type were grouped based on their RFPs or RSPs into the proper CSs before 

treatment. Each of the 0.1-mi (1.6-km)-long pavement segments in each CS group before 

treatment was listed in the after treatment CS based on its after treatment RFP or RSP. For each 

treatment type, the weighted average treatment benefits, in terms of each pavement condition and 

distress type, were then calculated. The results were submitted to FHWA and are available from 

the LTPP Customer Support Services.(79) These weighted average treatment benefits were then 
292

compared with the weighted average treatment benefits of the LTPP test sections. The results are 

listed in table 118 through table 122 and shown in figure 102 through figure 116. The data in the 

15 figures indicated the following:

• The weighted average benefits of each of the five treatment types, in terms of each 

pavement condition and distress type, obtained from the analyses of the LTPP data were 

similar to the benefits obtained from the State data. The implication of this is that the 

treatment benefits described in this report that were found using the LTPP data, could be 

used as benchmark values for the national practice. State transportation departments 

could use such data to do the following:

o Gauge the effectiveness of their current practices using similar analyses.

o Conduct lifecycle cost analyses of various treatment alternatives to optimize the 

pavement network rehabilitation and treatment strategy.

• The methodologies described in this report for the analyses of the LTPP pavement 

condition and distress data applied to the State data.

• The three-level (poor, fair, and good) and five-level (very poor, poor, fair, good, and very 

good) pavement rating systems developed in this study, and presented in chapter 4 based 

on the time-series pavement condition and distress, were equally applicable to the LTPP 

and State data.

• The average variability in the measured pavement condition and distress data over time 

for the LTPP test sections was very similar to the variability of the State-measured data 

along most pavement projects.

• The percent of the LTPP test sections that were excluded from the analyses because of an 

inadequate number of data points or because of improving pavement condition and/or 

distress over time without the application of treatments was equivalent to the percent of 

the 0.1-mi (0.16-km)-long segments of a pavement project that was excluded from the 

analyses for the same reasons.

Based on the results of the analyses, the following is strongly recommended:

• The dual pavement condition rating systems should be submitted for approval and 

adoption by FHWA, AASHTO, and the State transportation departments.

• The algorithms developed in this study should be standardized and used on future 

research studies.

• The benchmark values regarding the benefits of the five treatment types included in this 

study should be expanded to include additional pavement treatments. 
293

CHAPTER 9. SUMMARY, CONCLUSIONS, AND RECOMMENDATIONS

This chapter presents a summary of the studies described in chapters 2 through 8, followed by 

significant conclusions reached organized by topic, and finally, significant recommendations also 

organized by topic.

SUMMARY

For the benefit of the reader, this section is divided into several paragraphs. Each paragraph 

includes the summary of a particular chapter of the final report.

At the outset, the research team conducted a comprehensive review of the state of the practice of 

various State transportation departments with regard to several aspects of pavement condition 

measures, pavement condition and distress data analyses, and treatment selection. The review 

also included previous related studies that were conducted using the LTPP database. The detailed 

literature review can be found in chapter 2. The topics covered include the following:

• Pavement distress severity levels.

• Pavement condition and distress descriptions.

• Pavement performance modeling and treatment benefit calculations.

• Treatment type and time selection.

• Preservation costs and LCCA.

• Effectiveness of pavement treatments at the project and network levels.

• LTPP Program, its objectives, and the SPS and GPS test sections.

• Previous findings regarding the impacts of pavement treatments and various design 

factors on pavement performance.

At the same time, the research team downloaded from the six data volumes housed in the LTPP

database Standard Data Release 28.0 the data elements of the more than 2,500 test sections 

included in the LTPP Program. The data were organized in a special format and readied for 

analyses. In addition, the pavement management databases from three State transportation 

departments (CDOT, WSDOT, and LADOTD) were requested and received. From each 

database, several pavement projects that were subjected to certain treatments in the past were 

identified, and their data were downloaded from the respective databases and formatted for 

analyses. The details of the LTPP and State data mining are in chapter 4.

Based on the literature review, and general review of the LTPP data, dual pavement condition 

rating systems were developed based on the pavement function and its structural integrity. One 

system was based on three CSs, and the other was based on five CSs. For each pavement section, 

the functional CS was based on ride quality in terms of IR) and safety in terms of rut depth. The 
294

functional CS was expressed in term of RFP in years for the pavement to reach the prespecified 

threshold value for IRI or rut depth. The structural CS was based on RSP in years for the 

pavement section to reach the threshold values in terms of alligator, transverse, or longitudinal 

cracking or rut depth. The rating system for each CS consisted of numerical classification, color 

coding, range of RFP and RSP, and the average cost per 0.1 mi (0.16 km) of preserving the 

pavement. Further, based on the literature review and common engineering practice, individual

threshold values for IRI, rut depth, alligator, transverse, and longitudinal cracking were 

recommended and used in the analyses of the LTPP and State data. Details of the two pavement 

rating systems and the threshold values used in the analyses are in chapter 3 of this report.

The performance of each of the LTPP flexible pavement test sections included in the SPS-1, -3,

and -5, and GPS-6 experiments was analyzed. In the analyses, the available before and after 

treatments time-series pavement condition (IRI and rut depth) and distress (rut depth, alligator, 

longitudinal, and transverse cracking) data were used. The data were modeled as a function of 

time using the proper mathematical function form (power function for rut depth, exponential for 

IRI, and logistic for cracking). Results of the analyses were expressed in terms of RFP for IRI, 

RFP/RSP for rut depth, and RSP for each cracking type. Thus, for each test section, two RFPs

and four RSPs were calculated. These values were used to assess the impacts of regional climatic 

and design factors on pavement performance. In addition, the LTPP data on the flexible 

pavement test sections were used to develop a new and novel method to estimate the pavement 

performance based on a single data point. The ORCSE method was developed to be applied to 

pavement sections that had experienced less frequent data collection and/or pavement sections 

that might not yet have sufficient data records for modeling owing to age. The analyses and the 

results of the analyses of the SPS-1, -3, and -5, and GPS-6 test sections and the development and 

validation of the ORCSE method are detailed in chapter 5.

The performance of each of the LTPP rigid pavement test sections included in the SPS-2, -4, -6, 

and -7, and GPS-7 experiments was also analyzed. In the analyses, the available before and after 

treatments time-series pavement condition (IRI and rut depth) and distress (rut depth, alligator, 

longitudinal, and transverse cracking) data were used. The intent was to study the impact of each 

design variable on pavement performance. When the data were divided into various groups based 

on separation of variables, the number of test sections under each design variable was 

statistically insignificant (i.e., for some variables, there was only one or no test section). 

Therefore, the impacts of the design variables on pavement performance were not analyzed or 

discussed any further. Rather, the data were used to study the impacts of the conditions in 

climatic regions on pavement performance. Results of those analyses are detailed in chapter 6.

The measured flexible and rigid pavement deflection data were organized and analyzed. The 

analyses were conducted to accomplish the following two objectives:

• Determine whether the measured flexible pavement deflection data could be used to 

estimate the time to initiate bottom-up cracking before those cracks appeared on the 

pavement surface and whether those data could be used in the RFP and RSP algorithms.

• Study the LTE using the measured rigid pavement deflection data and its impact on ride 

quality in terms of IRI.
295

At the onset, it was envisioned that measured pavement deflections and/or LTE data would be 

correlated with pavement condition or distress, and hence, they could be used as an early 

warning of pending deterioration before it became visible on the pavement surface. For flexible 

pavements, the measured pavement deflections are a function of the pavement temperatures. 

Therefore, the accuracy of the existing temperature adjustment methods were reviewed and 

scrutinized using the measured LTPP deflection data. Based on the results, a new global 

temperature correction methodology was developed that could be applied to all deflection 

sensors and all climatic regions. Results of the analyses of the measured deflection data and the 

methodology and algorithms of the newly developed temperature correction method are detailed 

in chapter 7.

The pavement condition and distress databases of three pavement networks were requested and 

received from CDOT, LADOTD, and WSDOT. Each database was searched and pavement 

projects that received one of the following five treatment types were identified:

• Thin overlay (≤ 2.5 inches (63.5 mm)).

• Thick overlay (> 2.5 inches (63.5 mm)).

• Thin mill and fill (≤ 2.5 inches (63.5 mm)).

• Thick mill and fill (> 2.5 inches (63.5 mm)).

• Single chip seal.

The research team analyzed the pavement condition and distress data measured before and after 

treatment of each 0.1-mi (0.16-km)-long pavement segment along each selected pavement 

project that was treated using one of these five treatments. The main objective of the analyses 

was to calculate the treatment benefits in terms of the following:

• RFPs and RSPs before and after treatment.

• CFP and CSP resulting from the treatment.

• FCROP and SCROP.

For each treatment type, the weighted average treatment benefits, in terms of each pavement 

condition and distress type, were then calculated. The results were submitted to FHWA and are 

available from the LTPP Customer Support Services.(79) The weighted average treatment benefits 

were then compared with the weighted average treatment benefits of the LTPP test sections. 

Results of the comparison are detailed in chapter 8.

CONCLUSIONS 

Based on the review of various national and international papers published in various journals 

and mainly on the results of the analyses, various conclusions (detailed in each chapter) were 

drawn that cover various study-related topics. For convenience, only significant conclusions 

were selected and listed by topic in the follow subsections. 
296

Pavement Performance Measures

The following significant conclusions were drawn regarding pavement performance measures:

• The pavement cracking data were typically collected and stored based on three severity 

levels (low, medium, and high). For most cases, the problem was that the data could not 

be analyzed per severity level because of their excessive variability from one year to the 

next. Analyses of the cracking data based on the sum of all severity levels were proven to 

overcome the problem.

• For pavement projects received the same treatment type, T2Ms were developed to display 

the distribution of the pavement conditions along the project before and after treatment. 

The data in the T2Ms were used to estimate the benefits of the various treatments.

• Pavement condition rating should be based on current conditions and distresses as well as 

the pavement’s rates of deterioration. 

• The three- and five-level dual pavement condition rating systems developed in this study 

were useful and were equally applied to both State and LTPP data. The systems were 

flexible and could be easily tailored to fit the needs and constraints of any road agency.

• The estimated average cost of pavement preservation for each level of the dual pavement 

rating system could be used in the lifecycle cost analyses and in strategy optimization. 

• Threshold values were provided for calculation of RFP and RSP. The values were based 

on minimum level of service to the user (functional), and loss of structural integrity 

(structural). 

Flexible Pavements

The following significant conclusions were drawn regarding flexible pavements:

• The conditions in WF regions had significant adverse impacts on pavement performance 

in terms of IRI, rut depth, and cracking. 

• Drainable bases decreased the impacts of the conditions in WF regions on pavement 

performance. The pavement performance owing to drainable bases was slightly better 

than that owing to increasing the AC thickness from 4 to 7 inches (102 to 178 mm). 

Therefore, the use of drainable bases in the WF region was cost effective. 

• The inclusion of drainable bases in the DF and DNF regions did not affect pavement 

performance in terms of RFP or RSP. This was expected because the volume and frequency 

of available water was low. Furthermore, most rainfall occurred over short periods of time 

with most water running off the surface and not penetrating the pavement layers.

• Increasing the thickness of the AC layer from 4 to 7 inches (102 to 178 mm) increased

the frost protection of the lower layers, and hence decreased the impacts of the climatic 
297

conditions in the WF region on pavement performance. However, this option was not a 

cost-effective one.

• The climatic conditions in the WNF, DF, and (DNF) regions did not affect the pavement 

performance in terms of rutting potential and IRI.

• The climatic conditions in the DF region had more adverse effects on cracking potential 

than those in the DNF region. This was mainly attributed to the higher oxidation (aging) 

potential of the AC layer in the DF region.

• The thin overlay treatment improved the pavement performance of the SPS-3 test 

sections in terms of IRI and rut depth in all climatic regions except the DNF region. This 

could be related to construction issues and, perhaps, the relatively high solar radiation 

(accelerated oxidation/aging) in the DNF region.

• In general, the thin overlay treatment did not improve the pavement performance of the 

SPS-3 test sections in terms of alligator, longitudinal, and transverse cracking. This is 

mainly because of the high rate of reflective cracking. Immediately after treatment, all 

cracks were hidden by the thin overlay. However, one or few years later, most cracks were 

reflected through the overlay, which implied relatively high rate of deterioration and hence 

short RSP. The exception was in the DNF region where the two test sections showed an 

increase of 12 years in the average RSP compared with the one control section. This 

oddity was mainly attributable to the limited number of sections. That is, the conclusion 

was not reliable because of the limited number of test sections and control sections.

• The slurry seal treatment improved the pavement performance of the SPS-3 test sections 

in terms of IRI and rut depth but did not have much impact on alligator, longitudinal, and 

transverse cracking. 

• Crack sealing appeared to improve pavement performance of the SPS-3 test sections in terms 

of rutting. However, it did not improve the pavement performance in terms of cracking.

• Aggregate seal coats appeared to improve the pavement performance of the SPS-3 test 

sections in all climatic regions in terms of IRI, rut depth, and cracking. 

• In general, the worse the pavement conditions were before treatment, the shorter the 

benefits of treatments were in terms of RFP and/or RSP. 

• On average, the impact of 2- and 4-inch (51- and 102-mm)-thick virgin or recycled AC 

overlays on pavement performance of the SPS-5 test sections was almost the same.

• The 2-inch (51-mm)-thick AC overlay (virgin or recycled mix) did not provide long-term 

remediation of transverse cracking. The cracks in the lower pavement structure typically 

reflect through the overlay in few years.
298

• On average, SLE in terms of alligator crack for a flexible pavement structure as a result 

of application of a 4-inch (102-mm) AC overlay was slightly better than that which 

resulted from application of a 2-inch (51-mm) overlay.

• In each climatic region, the impacts of the thin and thick overlay or thin and thick milland-fill treatments on IRI and rut depths were almost the same. This was expected 

because good quality construction can decrease the pavement surface roughness 

substantially regardless of the overlay thickness and because most pavement rutting 

occurs early in the pavement life and can be removed during the treatment.

ORCSE Method

The following significant conclusions were drawn regarding the ORCSE method:

• The ORCSE method was successful in predicting the CSs of the test sections based on 

RFP and RSP. For older test sections, the average estimation error for the most critical 

CSs was less than 1 percent. 

• The ORCSE method would have significant potential benefit for local roadway owners as 

well as State transportation department managers when only two or fewer data points 

were available.

Rigid and Composite Pavements

Significant conclusions regarding rigid and composite pavements were the following:

• The conditions in the WF region had more damaging impacts on the performance of the 

SPS-4 test sections in terms of transverse cracking than on test sections located in the 

WNF, DF, and DNF regions. This was expected owing to the combined effects of 

subfreezing temperatures and moisture.

• On average, the majority of the SPS-2 test sections located in the WNF region performed 

worse relative to longitudinal cracking than those in the DNF region. This was mainly 

due to the impact of excessive moisture on pavement performance.

• On average, in terms of IRI, joint and crack sealing treatment had a positive impact on 

the performance of the SPS-4 test sections located in the WNF region, no impact in the 

WF region, and negative impact in the DF and DNF regions. 

• Joint and crack sealing was effective in the WF region and not effective in the other 

three climatic regions, and joint undersealing was not effective in any region.

• The performance of treated SPS-6 test sections in terms of IRI was independent of the 

climatic region and pavement type. It was also independent of treatment type in terms of

rut depth.

• The alligator cracking data in the SPS-6 database were highly likely an advanced form of 

top-down fatigue cracking. (Top-down cracks are fatigue cracks that initiate at the 
299

pavement surface and, over time, propagate downward.) The short transverse and 

longitudinal cracks resembled the traditional bottom-up alligator cracking pattern.

• The performance of the test sections in terms of longitudinal cracking was worse after 

subjecting the section to any of the seven analyzed treatment types.

• Minimum and maximum pavement restoration with no AC overlay treatments did not 

improve the performance of the JRCP test sections.

• The IRI-based performance of the treated CRCPs (SPS-7) test sections was independent 

of the eight treatment types and the two climatic regions (WF and WNF).

• The performance of the JPCP test sections subjected to a 3-inch (76-mm) concrete 

overlay with milling (703) or with shot blasting (704) treatments was lower than the 

performance of the other JPCP test sections subjected to the other six treatments.

• None of the eight applied treatments were effective in treating transverse cracking 

problems of the CRCP test sections.

Deflection

The following significant conclusions were drawn regarding deflection:

• The LTPP deflection data collected for the SMP test sections did not support the use of 

the AI temperature adjustment procedure for the measured deflection data. 

• The newly developed global flexible pavement deflection temperature adjustment 

algorithm was applicable to all deflection sensors and in all climatic regions. 

• The LTPP deflection data collected for the SMP test sections did not correlate with the 

measured pavement condition and distress data. Therefore, the data were not included in 

the RFP and RSP algorithms.

• The LTPP deflection and LTE data collected for the SPS-2 test sections in Arizona, 

Colorado, Delaware, and Michigan did not support modeling of the data as a function 

of time. 

State Data

The following significant conclusions were drawn regarding State data:

• The weighted average benefits of each of five treatment types (thin overlay (≤ 2.5 inches

(63.5 mm)), thick overlay (> 2.5 inches (63.5 mm)), thin mill and fill (≤ 2.5 inches

(63.5 mm)), thick mill and fill (> 2.5 inches (63.5 mm)), and single chip seal) with regard

to each pavement condition and distress type, obtained from the analyses of the LTPP 

data, were similar to the benefits obtained from the three sets of State data. 
300

• The methodologies described in this report for the analyses of the LTPP pavement 

condition and distress data applied to the State data.

• The three-level (poor, fair, and good) and five-level (very poor, poor, fair, good, and very 

good) pavement rating systems developed in this study, and presented in chapter 4 based 

on the time-series pavement condition and distress, were equally applicable to the LTPP 

and the State data. 

• The average variability in the pavement condition and distress data over time for the LTPP 

test sections was almost the same as the average variability of the State-measured data. 

• The percent of the LTPP test sections that were excluded from the analyses because of an

inadequate number of data points or because of improving pavement condition and/or 

distress over time without the application of treatments was similar to the percent of the 

0.1-mi (0.16-km)-long pavement segments of a given pavement project that was excluded 

from the analyses for the same reasons.

RECOMMENDATIONS

Based on the results of the LTPP and State data analyses and the conclusions listed in the 

previous section, various recommendations were developed. For convenience, the significant 

recommendations are listed by topic in the following subsections. 

Performance Measures

The following recommendations were developed regarding performance measures:

• The sum of crack lengths or crack areas of all severity levels should be used to model the 

data as a function of time.

• Accurate pavement planning and management decisions should be based on the 

pavement conditions and rates of deterioration.

• The three and/or the five-level rating systems should be adopted by FHWA and 

submitted to AASHTO for approval.

• The threshold values used in this study should be adopted or similar ones should be 

developed by highway owners to estimate RFP and RSP of the various pavement sections.

• Each highway agency should develop the average cost of pavement preservation for each 

RFP and RSP level of the dual pavement rating systems using their own cost records.

• LCCA should be performed at the project level and strategy optimization at the network 

level to improve the overall cost effectiveness of the pavement management application.

• The T

2M procedure should be adopted and used by road owners to assess treatment 

effectiveness and to select the optimum treatment time.
301

• The dual pavement rating system described in chapter 3 should be used in future analyses 

and assessments of the benefits of pavement rehabilitation and/or maintenance treatments.

Flexible Pavements

The following recommendations were developed regarding flexible pavements:

• Drainable bases should be constructed to enhance the performance of pavement sections 

located in the WF region.

• For future study, the control or linked test sections should be selected to border the 

regular test sections in question, and their history should be included in the database. This 

would eliminate unnecessary variability.

• The pavement condition and distress data should be measured before and after treatments. 

The quality control data for project acceptance should be included in the PMS database.

• The frequency of pavement condition and distress data collection should be a function of 

treatment type. Treatments having short TL should be surveyed more often than longlife treatments. 

ORCSE Method

The following recommendation was developed regarding the ORCSE method:

• The ORCSE method should be expanded to address other conditions and distresses as 

well as be applied to a wider range of LTPP and State transportation department data to 

further verify its successful prediction of the pavement CSs.

Deflection

The following recommendations were developed regarding deflection:

• The new global flexible pavement deflection temperature adjustment algorithm should be 

adopted and used to adjust the measured pavement deflection to a standard temperature 

of 70 oF (21 oC).

• The estimation of the AC modulus at the standard temperature of 70 oF (21 oC)could be 

accomplished using one of the following two procedures:

o Adjust the measured deflection data to 70 oF (21 oC) using the newly developed 

global model and then backcalculate the modulus.

o Conduct a minimum of three FWD tests at temperatures above and below the 

standard temperature of 70 oF (21 oC) and measure the pavement deflections. 

Backcalculate the AC modulus value for each of the three FWD tests. Plot the 

modulus as a function of the measured pavement temperature and estimate the AC 

modulus at 70 oF (21 oC) from the graph.
302

• For new LTPP experiments or road tests, FWD tests should be conducted before and 

after treatment.

State Data

The following recommendations were developed regarding State data:

• The dual pavement condition rating systems should be adopted by FHWA, AASHTO, 

and the State transportation departments. This would unify the analyses of pavement 

performance nationwide.

• The benchmark benefit values of the five treatment types included in this study should be 

expanded to include additional pavement treatments.

• The treatment benefits described in this report that were found using the LTPP data, 

could be used as benchmark values for the national practice. State transportation 

departments could use such data to do the following:

o Gauge the effectiveness of their current practices using similar analyses.

o Conduct lifecycle cost analyses of various treatment alternatives to optimize the 

pavement rehabilitation and treatment strategy at the network level.

Future Studies

The following actions are recommended regarding future studies:

• Initiate studies to produce a national catalog regarding the service life of various 

treatment types as a function of pretreatment pavement conditions and distress. The 

studies should be based mainly on the LTPP data and applied to some State data.

• Initiate studies to establish automated data collection processes, quality control 

procedures, and data storage to minimize the impact of subjective factors and human 

errors on pavement conditions and distress. 

• Initiate studies to scrutinize the newly developed self-powered wireless Pico sensors 

(1012 mm) that can be embedded in pavement and transportation infrastructures to 

measure their performance in terms of cracking and induced stresses and strains.

• Explore the accuracy of the newly developed chemical sensors that can be included in 

concrete and asphalt mixes to measure future pavement conditions and distresses. This 

would eliminate the need for traditional data collection. 

• Develop short course materials with examples to train engineers and staff of State 

transportation departments to use the MATLAB® computer program for the analyses of 

pavement condition and distress and to emphasize the benefits of the LTPP Program. 
303

REFERENCES

1. Federal Highway Administration. (2016) Long-Term Pavement Performance (LTTP) Data 

Analysis Program—Expanded Strategic Plan. Federal Highway Administration, Washington, 

DC. Available at https://www.fhwa.dot.gov/research/tfhrc/programs/infrastructure/ 

pavements/ltpp/analysisplan/index5.cfm. Last accessed January 2013.

2. Baladi, G.Y., Haider, S.W., Chatti, K., Galehouse, L., Dawson, T.A., Dean, C.M., Muscott, 

R., Tecca, N., and McClosky, M. (2009) Optimization of and Maximizing the Benefits from 

Pavement Management Data Collection: Interim Report 1. Federal Highway Administration, 

Washington, DC.

3. Dawson, T.A. (2012) Evaluation of Pavement Management Data and Analysis of Treatment 

Effectiveness Using Multi-Level Treatment Transition Matrices. Doctoral Dissertation. 

Michigan State University, East Lansing, MI.

4. Baladi, G.Y., Snyder, M., and McKelvey, F. (1992) Highway Pavements. Volumes I, II, and 

III. National Highway Institute, Washington, DC.

5. Dawson, T.A., Baladi, G.Y., Dean, C.M., Haider, S.W., and Chatti, K. (2011) Defining 

Benefits from Pavement Rehabilitation and Preservation. American Society of Civil 

Engineers Conference: Integrated Transportation and Development for a Better Tomorrow, 

Chicago, IL.

6. Khattak, J.M. and Baladi, G.Y. (2007) Deduct Points and Distress Indices. University of 

Louisiana at Lafayette, Lafayette, LA.

7. Baladi, G.Y., Buch, N., and Van Dam, T. (1999) Michigan Pavement Preservation Manual.

Michigan Department of Transportation, Michigan State University, East Lansing, MI.

8. Khazanovich L., Darter, M., Barlett, R., and McPeak, T. (1998) Common Characteristics of 

Good and Poorly Performing PCC Pavements. Report No. FHWA-RD-97-131, Federal 

Highway Administration, Washington, DC.

9. Guerre, J., Groeger, J., Hecke, S.V., Simpson, A., Rada, G., and Visintine, B. (2011)

Improving FHWA’s Ability to Assess Highway Infrastructure Health, Phase 1 Results. Final 

Report. Report No. FHWA-HIF-12-049, Federal Highway Administration, Washington DC.

10. Rauhut, J.B., Eltahan, A., and Simpson, A.L. (1999) Common Characteristics of Good and 

Poorly Performing AC Pavements. Report No. FHWA-RD-99-193, Federal Highway 

Administration, Washington, DC.

11. Guerre, J., Groeger, J., Hecke, S.V., Simpson, A., Rada, G., and Visintine, B. (2012) 

Improving FHWA’s Ability to Assess Highway Infrastructure Health. Pilot Study Report. 

Report No. FHWA-HIF-12-049, Federal Highway Administration, Washington, DC.
304

12. Chatti, K., Buch, N., Haider, S.W., Pulipaka, A.S., Lyles, R.W., Gilliland, D., and Desaraju, 

P. (2005) LTPP Data Analysis: Influence of Design and Construction Features on the 

Response and Performance of New Flexible and Rigid Pavements. Web-Only Document 74 

(Project 2050 [10/16]), National Cooperative Highway Research Program, Washington, DC.

13. Ahmed, K., Abu-Lebdeh, G., and Lyles, R.W. (2006) “Prediction of Pavement Distress Index 

With Limited Data on Causal Factors: an Auto-Regression Approach,” The International 

Journal of Pavement Engineering, 7(1), p. 23–35, London, England.

14. Irfan, M., Khurshid, M.B., and Labi, S. (2009) Service Life of Thin HMA Overlay Using 

Different Performance Indicators. Transportation Research Board Annual Meeting, 

Washington, DC.

15. Luo, Z. (2006) Flexible Pavement Condition Model Using Cluster-wise Regression and 

Mechanistic-Empirical Procedure for Fatigue Cracking Modeling. Doctoral Dissertation. 

The University of Toledo, Toledo, OH.

16. Geoffrey, D.N. (1996) “Cost-Effective Preventive Pavement Maintenance,” Synthesis of 

Highway Practice 223. Transportation Research Board, National Research Council, National 

Cooperative Highway Research Program, Washington, DC.

17. Hicks, G.R., Seeds, S.B., and Peshkin, D.G. (2000) Selecting a Preventive Maintenance 

Treatment for Flexible Pavements. Report No. FHWA-IF-00-027. Foundation for Pavement 

Preservation, Federal Highway Administration, Washington, DC. 

18. Johnson, A.M. (2000) Best Practices Handbook on Asphalt Pavement Maintenance. Report 

No. 200004, Center for Transportation Studies (CTS), Minnesota T2/LTAP Program, 

University of Minnesota, Minneapolis, MN.

19. Ohio Department of Transportation. (2001) Pavement Preventive Maintenance Program 

Guidelines. Ohio Department of Transportation, Office of Pavement Engineering Report, 

Columbus, OH.

20. Wade, M., DeSombre, R.I., and Peshkin, D.G. (2001) High Volume/High Speed Asphalt 

Roadway Preventive Maintenance Surface Treatments. Report No. SD9909, South Dakota 

Department of Transportation, Pierre, SD.

21. Michigan Department of Transportation. (2001) Effectiveness of the Capital Preventive 

Maintenance Program. Michigan Department of Transportation, Lansing, MI.

22. Peshkin, D.G., Hoerner, T.E., and Zimmerman, K.A. (2004) Optimal Timing of Pavement 

Preventive Maintenance Treatment Applications. Report 523. Transportation Research 

Board, National Cooperative Highway Research Program, Washington, DC.

23. Federal Highway Administration. (2010) Performance Evaluation of Various Rehabilitation 

and Preservation Treatments. Report No. FHWA-HIF-10-020, Federal Highway 

Administration, Washington, DC.
305

24. Bolander, P.W. (2005) “Seal Coat Options: Taking Out the Mystery,” Transportation 

Research Circular No. EC078: Roadway Pavement Preservation 2005: Papers from the First 

National Conference on Pavement Preservation, Transportation Research Board, 

Washington, DC.

25. Gransberg, D. and James, D.M.B. (2005) “Chip Seal Best Practice,” Synthesis of Highway 

Practice 342. Transportation Research Board, National Cooperative Highway Research 

Program, Washington, DC.

26. Maher, M., Marshall, C., Harrison, F., and Baumgaertner, K. (2005) Context Sensitive 

Roadway Surfacing Selection Guide. Publication No. FHWA-CFL/TD-05-004a. , Central 

Federal Lands Highway Division, Federal Highway Administration, Washington, DC. 

27. Morian, D.A., Oswalt, J., and Deodhar, A. (2004) “Experience With Cold In-Place Recycling 

as a Reflective Crack Control Technique: Twenty Years Later,” Transportation Research 

Record 1869, p. 47–55, Transportation Research Board, Washington, DC.

28. Smith, R.E. and Beatty, C.K. (1999) “Microsurfacing Usage Guidelines,” Transportation 

Research Record 1680, p. 13–17, Transportation Research Board, Washington, DC.

29. Labi, S., Hwee, M.S., Lamptey G., and Nunoo, C. (2006) Long-Term Benefits of 

Microsurfacing Applications in Indiana—Methodology and Case Study. Transportation 

Research Board Annual Meeting, Washington, DC.

30. Spath, H. (1979) “Clusterwise Linear Regression,” Computing 22(4), p. 376–373, New 

York, NY.

31. Spath, H. (1981) “Correction to Algorithm 39: Clusterwise Linear Regression,” Computing

26, p. 275–275, New York, NY.

32. Spath, H. (1982) “Algorithm 48: A Fast Algorithm for Clusterwise Linear Regression,” 

Computing, 29, p. 187–189, New York, NY.

33. Meier, J. (1987) “A Fast Algorithm for Clusterwise Linear Absolute Deviations Regression,” 

OR Spectrum 9(3), p. 187–189, Woodstock, GA.

34. DeSarbo, W.S., Oliver, R.L., and Rangaswamy, A. (1989) “A Simulated Annealing 

Methodology for Clusterwise Linear Regression,” Psychometrika 54(4), p. 707–736, 

Greensboro, NC.

35. Lau, K.N., Leung, P.L., and Tse, K.K. (1999) “A Mathematical Programming Approach to 

Clusterwise Regression Model and its Extensions,” European Journal of Operational 

Research 116(3), 1 p. 640–652, Amsterdam, The Netherlands.

36. American Association of State Highway and Transportation Officials. (1993) American 

Association of State Highway and Transportation Officials, Guide for Design of Pavement 

Structures. American Association of State Highway and Transportation Officials, 

Washington, DC.
306

37. American Association of State Highway and Transportation Officials. (2004) “Chapter 6: 

HMA Rehabilitation of Existing Pavements,” Guide for Mechanistic-Empirical Design of 

New and Rehabilitated Pavement Structures, Part 3. NCHRP Project 137A Final Report. 

National Cooperative Highway Research Program, Transportation Research Board, and 

American Association of State Highway and Transportation Officials, Washington, DC.

38. Labi, S. and Sinha, K.C. (2003) The Effectiveness of Maintenance and Its Impact on Capital 

Expenditures, Joint Transportation Research Program, West Lafayette, IN.

39. Morgado, J. and Neves, J. (2008) Accounting for User Costs When Planning Pavement 

Maintenance and Rehabilitation Activities. Technical University of Lisbon, Lisbon, Portugal.

40. Reigle, J. and Zaniewski, J. (2002) “Risk-Based Life-Cycle Analysis for Project-Level 

Pavement Management,” Transportation Research Record 1816, p. 34–42, Transportation 

Research Board, Washington, DC.

41. Khurshid, M.B., Irfan, M., and Labi, S. (2008) Comparing the Methods for Evaluating 

Pavement Interventions—A Discussion and Case Study. Transportation Research Board 

Annual Meeting, Washington, DC.

42. Mouaket, I.M. and Sinha, K.C. (1991) Cost Effectiveness of Routine Maintenance on Rigid 

Pavements. Report FHWA/JHRP-9111. Federal Highway Administration, Joint Highway 

Research Project, Purdue University, West Lafayette, IN. 

43. Lamptey, G., Ahmad, M., Labi, S., and Sinha, K.C. (2004) Life Cycle Cost Analysis for 

INDOT Pavement Design Procedures. Report FHWA/IN/JTRP-2004/28. Federal Highway 

Administration, Purdue University, West Lafayette, IN.

44. Fazil, T.N. and Parades, V. (2001) “Cost-Benefit Highway Pavement Maintenance,” 

Transportation Research Record 1749, p. 22–27, Transportation Research Board, 

Washington DC.

45. Baladi, G.Y., Dawson, T.A., Dean, C.M., Haider, S.W., Chatti, K., and Galehouse, L. (2011) 

Optimization of and Maximizing the Benefits From Pavement Management Data Collection: 

Thirteenth Quarterly Report, Federal Highway Administration, Washington, DC.

46. Chien, S., Tang, Y., and Schonfeld, P. (2002) “Optimizing Work Zones for Two Lane 

Highway Maintenance Projects,” Journal of Transportation Engineering, 128(2), p. 145–

155, American Society of Civil Engineers, Reston, VA.

47. Lewis, D.L. (1999) “Road User and Mitigation Costs in Highway Pavement Projects,” 

Synthesis of Highway Practice 269. Transportation Research Board, National Cooperative 

Highway Research Program, Washington, DC.

48. Daniels, G., Stockton, W.R., and Hundley, R. (2000) “Estimating Road User Costs 

Associated with Highway Construction Projects,” Transportation Research Record 1732, 

p. 70–79, Transportation Research Board, Washington, DC.
307

49. Berthelot, C.F., Sparks, G.A., Blomme, T., Kajner, L., and Nickeson, M. (1996) 

“Mechanistic-Probabilistic Vehicle Operating Cost Model,” Journal of Transportation 

Engineering, 122(5), p. 337–341. American Society of Civil Engineers, Reston, VA. 

50. Walls, J. and Smith, M.R. (1998) Life-Cycle Cost Analysis in Pavement Design—Interim 

Technical Bulletin. Publication No. FHWA-SA-98-079, Federal Highway Administration, 

Washington, DC.

51. Bureau of Labor Statistics. (2011) Consumer Price Index. Bureau of Labor Statistics, 

Washington, DC. Available at http://www.bls.gov/cpi/. Last accessed August 1, 2015.

52. Geiger, D.R. (2005) ACTION: Pavement Preservation Definitions. Federal Highway 

Administration, Washington, DC. Available at www.fhwa.dot.gov/pavement/preservation/ 

091205.cfm. Last accessed August 1, 2015.

53. Federal Highway Administration. (2000) “Optimizing Highway Performance: Pavement 

Preservation,” Construction and Maintenance Fact Sheets. Publication No. FHWA-IF-00-

013. Federal Highway Administration, Washington, DC. Available at http://www.fhwa.dot. 

gov/pavement/t2/fs00013.cfm. Last accessed August 1, 2015.

54. Baladi, G.Y., Svasdisant, T., Van Dam, T., Buch, N., and Chatti, K. (2002) “Cost-Effective 

Preventive Maintenance: Case Studies,” Transportation Research Record 1795, p. 17–26, 

Transportation Research Board, Washington, DC.

55. Adams, T.M. and Kang, M. (2006) Considerations for Establishing a Pavement Preservation 

Program. Transportation Research Board Annual Meeting, Washington, DC.

56. Delaware Department of Transportation. (2001) Pavement Management: A Guide for Local 

Officials. Delaware Department of Transportation, Pavement Preservation 2, Foundation for 

Pavement Preservation, Salina, KS.

57. Al-Mansour, A.I. and Sinha, K.C. (1994) “Economic Analysis of Effectiveness of Pavement 

Preventive Maintenance,” Transportation Research Record 1442, p. 31–37, Transportation 

Research Board, Washington DC.

58. Labi, S. and Sinha, K.C. (2004) “Effectiveness of Highway Pavement Seal Coating 

Treatments,” Journal of Transportation Engineering 130(1), p. 14–23, American Society of 

Civil Engineers, Reston, VA.

59. Elkins, G.E., Thompson, T., Simpson, A., and Ostrom, B. (2012) Long-Term Pavement 

Performance Information Management System Pavement Performance Database User 

Reference Guide. Report No. FHWA-RD-03088, Federal Highway Administration, 

Washington, DC.

60. Hall, K.T., Correa, C.E., and Simpson, A.L. (2002) LTPP Data Analysis: Effectiveness of 

Pavement Maintenance and Rehabilitation Options. Web-Only Document 74 (Project 2050 

[10/16]), National Cooperative Highway Research Program, Transportation Research Board, 

Washington, DC.
308

61. Morian, D.A., Epps, J.A., and Gibson, S.D. (1997) Pavement Treatment Effectiveness, 1995 

SPS-3 and SPS-4 Site Evaluations, National Reports. Report No. FHWA-RD-96-208, 

Federal Highway Administration, Washington, DC. 

62. Morian, D.A., Gibson, S.D., and Epps, J.A. (1998) Maintaining Flexible Pavements—The 

Long-Term Pavement Performance Experiment SPS-3: 5-Year Data Analysis. Report No. 

FHWA-RD-97-102, Federal Highway Administration, Washington, DC. 

63. Carvalho, R., Ayres, M., Shirazi, H., Selezneva, O., and Darter M. (2011) Impact of Design 

Features on Pavement Response and Performance in Rehabilitated Flexible and Rigid 

Pavements. Report No. FHWA-HRT-10-066, Federal Highway Administration, 

Washington, DC. 

64. Rauhut, J.B., Von Quintus, H.L., and Eltahan, A. (2000) Performance of Rehabilitated 

Asphalt Concrete Pavements in LTPP Experiments—Data Collected Through February 

1997. Report No. FHWA-RD-00-029, Federal Highway Administration, Washington, DC.

65. Von Quintus, H.L., Simpson, A.L., and Elatahan, A.A. (2006) Rehabilitation of Asphalt 

Concrete Pavements: Initial Evaluation of the SPS-5 Experiment—Final Report. Report No. 

FHWA-RD-01-168, Federal Highway Administration, Washington, DC.

66. Ambroz, J.K. and Darter, M.I. (2005) Rehabilitation of Jointed Portland Cement Concrete 

Pavements: SPS-6 Initial Evaluation and Analysis. Report No. FHWA-RD-01-169, Federal 

Highway Administration, Washington, DC.

67. Selezneva, O.I., Jiang, Y.J., and Tayabji, S.D. (2002) Preliminary Evaluation and Analysis of 

LTPP Faulting Data—Final Report. Report No. FHWA-RD-00-076, Federal Highway 

Administration, Washington, DC.

68. Jiang, Y.J. and Darter, M.I. (2005) Structural Factors of Jointed Plain Concrete Pavements: 

SPS-2—Initial Evaluation and Analysis. Report No. FHWA-RD-01-167, Federal Highway 

Administration, Washington, DC.

69. Perera, R.W. and Kohn, S.D. (2001) LTPP Data Analysis: Factors Affecting Pavement 

Smoothness. National Cooperative Highway Research Program, Transportation Research 

Board, Washington, DC.

70. Simpson, A.L., Emmanuel, O., Pendleton, O., and Lee, Y. (1994) Sensitivity Analyses for 

Selected Pavement Distresses. Strategic Highway Research Program, Report No. SHRPP393, Transportation Research Board, Washington, DC. 

71. Mladenovic, G., Jiang, Y.J. and Darter, M.I. (2002) Study of Environmental Effects in the 

Absence of the Heavy Load-SPS-8—Initial Evaluation and Analysis. Report No. FHWA-RD02-087, Federal Highway Administration, Washington, DC.

72. Hall, K.T., Darter, M.I., Hoerner, T.H., and Khazanovich, L. (1997) LTPP Data Analysis Phase 

I: Validation of Guidelines for k-Value Selection and Concrete Pavement Performance 

Prediction. Report No. FHWA-RD-96198, Federal Highway Administration, Washington, DC.
309

73. Stubstad, R.N. (2002) LTPP Data Analysis: Variations in Pavement Design Inputs. National 

Cooperative Highway Research Program, Transportation Research Board, Washington, DC.

74. Harrigan, T. E. (2002) “Performance of Pavement Subsurface Drainage,” Research Results 

Digest, No. 268. National Cooperative Highway Research Program, Transportation Research 

Board, Washington, DC.

75. Titus-Glover, L., Owusu-Antwi, E.B., and Darter, M.I. (1998) Design and Construction of 

PCC Pavements Volume III: Improved PCC Performance Models. Report No. FHWA-RD98-113, Federal Highway Administration, Washington, DC. 

76. Rada, G.R., Wu, C.L., and Bhandari, R.K., Law, P.C.S. (1999) Study of LTPP Distress Data 

Variability, Volume 1. Report No. FHWA-RD-99-074, Federal Highway Administration, 

Washington, DC.

77. American Association of State Highway and Transportation Officials. (2013) Transportation 

Asset Management Guide—A Focus on Implementation. American Association of State 

Highway and Transportation Officials. Washington, DC.

78. Houston, W.N., Mamlouck, M.S., and Perera, R.W.S. (1992) “Laboratory Versus 

Nondestructive Testing for Pavement Design,” Journal of Transportation Engineering

118(2), p. 202–232, American Society of Civil Engineers, Reston, VA.

79. LTPP Customer Support Services. Detailed data and results from this study are available 

through LTPP Customer Support Services by phone at 202–493–3035 or by email at 

ltppinfo@fhwa.dot.gov.

80. Miller, S.G. and Bellinger, W.J. (2003) Distress Identification Manual for the Long-Term 

Pavement Performance Program. Publication No. FHWA-RD-03-031, Washington, DC.

81. Dawson T.A., Baladi, G., Beach, A., Dean, C., Haider, S., and Chatti, K. (2012) “Impact of 

Three State Practices on Effectiveness of Hot-Mix Asphalt Overlay,” Transportation 

Research Record 2292, 1, p. 52–61, Transportation Research Board, Washington, DC.

82. Federal Highway Administration. (2010) Local Calibration of the MEPDG Using Pavement 

Management Systems, Final Report Volume I. Report No. FHWA-HIF-11-026, Federal 

Highway Administration, Washington, DC.

83. Dawson, T. (2008) Backcalculated Subgrade Resilient Modulus Design Values for the State 

of Michigan. Master’s Thesis. Michigan State University, E. Lansing, MI.

84. Elseifi, M.A., Gaspard, K., Wilke, P.W., Zhang, Z., and Hegab, A. (2015) Evaluation and 

Validation of a Model to Predict Pavement Structural Number Using Rolling Wheel 

Deflectometer (RWD) Data. Transportation Research Board, Annual Meeting, 

Washington, DC.

85. Asphalt Institute. (1983) Asphalt Overlays for Highway and Street Rehabilitation. Manual 

No. MS17, Lexington, KY.
310

86. Lukanen E.O., Stubstad, R., and Briggs, R. (2000) Temperature Predictions and Adjustment 

Factors for Asphalt Pavement. Report No. FHWA-RD-98-085, Federal Highway 

Administration, McLean, VA.

HRDI-30/09-17(400)E Recycled

Recyclable