Preliminary Evaluation and Analysis of

LTPP Faulting Data — Final Report

PUBLICATION NO. FHWA-RD-00-076 JUNE 2000

Prepared by:

FHWA LTPP Data Analysis Technical Support Contractor

ERES Consultants, Inc.

9030 Red Branch Road, Suite 210

Columbia, Maryland 21045

Prepared for:

Office of Engineering and Highway Operations R&D

Long-Term Pavement Performance Division, HNR-30

Federal Highway Administration

6300 Georgetown Pike

McLean, Virginia 22101

(703) 285-2730

 Research and Development

Turner-Fairbank Highway Research Center

U.S. Department of Transportation 6300 Georgetown Pike

Federal Highway Administration McLean, Virginia 22101-2296
FOREWORD

Joint and crack faulting measurements are among the key data collected to monitor the

performance of concrete pavements in the Long Term Pavement Performance (LTPP) Program. 

This report documents an assessment of LTPP faulting data undertaken to evaluate their potential

use in more in-depth performance analyses. Results of this investigation included: (1)

identification, investigation, and correction (as appropriate) of anomalous faulting data; (2) the

creation of an LTPP database table with section summary statistics for faulting; and (3) findings

regarding the effect of various design features on the occurrence of faulting and the relationship

between ride quality and faulting.

This report will be of interest to those concerned with the management and design of portland

cement concrete pavements.

T. Paul Teng, P.E.

Director

Office of Infrastructure

 Research and Development

NOTICE

This document is disseminated under the sponsorship of the Department of Transportation in the

interest of information exchange. The United States Government assumes no liability for its

contents or use thereof. This report does not constitute a standard, specification, or regulation.

The United States Government does not endorse products or manufacturers. Trademarks or

manufacturers’ names appear herein only because they are considered essential to the object of

this document.
Technical Report Documentation Page

1. Report No.

FHWA-RD-00-076

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

4. Title and Subtitle

PRELIMINARY EVALUATION AND ANALYSIS OF LTPP FAULTING

DATA – Final Report

5. Report Date

6. Performing Organization Code

7. Author(s)

Olga Selezneva, Jane Jiang, and Shiraz D. Tayabji

8. Performing Organization Report No.

9. Performing Organization Name and Address

ERES Consultants, Inc.

9030 Red Branch Road, Suite 210

Columbia, Maryland 21045

10. Work Unit No. (TRAIS)

C6B

11. Contract or Grant No.

DTFH61-96-C-00003

12. Sponsoring Agency Name and Address

Office of Engineering Research and Development

Federal Highway Administration

6300 Georgetown Pike

McLean, Virginia 22101-2296

13. Type of Report and Period Covered

Draft Final Report

May 1998 to Dec 1998

14. Sponsoring Agency Code

15. Supplementary Notes

Contracting Officer’s Technical Representative (COTR): Cheryl Allen Richter, HNR-30

Technical Review by TRB ETG on LTPP Data Analysis

16. Abstract

A major goal of the Long-Term Pavement Performance (LTPP) study is the development of recommendations for

improving the design and construction of new and rehabilitated pavements to provide longer lasting pavements.

As part of the condition monitoring of the LTPP test sections, joint and crack faulting data are being collected on

a regular basis at each jointed concrete pavement test site. 

The LTPP faulting data are collected using the Georgia Faultmeter. Data are collected at joints and cracks along

the wheelpath and along the outside pavement edge. As part of the study reported here, the quality of the faulting

data was evaluated, and missing and questionable data were identified. The data were then used to develop faulting

data indices (average joint faulting for each visit) and related statistical parameters.

Also, data analysis was carried out to determine the usefulness of joint faulting and related data in identifying

factors that affect joint faulting. The analysis indicated that doweled joints exhibit very little faulting even after

many years of service and that the effect of design features such as drainage, tied-concrete shoulder use, and joint

spacing is not as significant when doweled joints are used. For non-doweled jointed plain concrete (JPC)

pavements, the following design features were found to significantly reduce faulting: use of widened lanes, effective

drainage system, stabilized base/subbase, and shorter joint spacing. Effect of faulting on ride quality was also

investigated using jointed plain concrete pavements (JPCP) sections with three or more faulting and International

Roughness Index (IRI) surveys. A strong correlation was found between rate of change in faulting values versus

rate of change in IRI values for JPCP sections. The results indicate that faulting is a major component of increased

roughness of JPC pavements.

17. Key Words

Concrete pavements, faulting, joint faulting, jointed

concrete pavements, LTPP, pavement performance

18. Distribution Statement

No restrictions. This document is available to the

public through the National Technical Information

Service, Springfield, VA 22161.

19. Security Classification (of this report)

Unclassified

20. Security Classification (of this page)

Unclassified

21. No. of Pages

149

22. Price

Form DOT F 1700.7 (8-72) Reproduction of completed page authorized

iii

TABLE OF CONTENTS

Section Page

1. INTRODUCTION ..........................................................1

Background ............................................................1

Objectives and Scope of Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

Report Organization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

2. ASSESSMENT OF FAULTING AND COMPLEMENTARY DATA QUALITY ........5

Joint Faulting Data Quality Evaluation .......................................5

Missing Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

Negative Faulting Values . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

Mismatched Joints . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

Comparison of Wheelpath and Edge Faulting Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20

Complementary Data Quality Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20

Summary and Recommendations for Resolving Data Quality Issues . . . . . . . . . . . . . . . 33

3. REPRESENTATIVE FAULTING INDICES AND STATISTICS . . . . . . . . . . . . . . . . . . . . 39

Development of a New IMS Table, MON_DIS_JPCC_FAULT_SECT . . . . . . . . . . . . . 39

Criteria for Valid Faulting Observations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39

Computation Algorithms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41

Outlier Faulting Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45

Summary of Representative Faulting Indices and Statistics . . . . . . . . . . . . . . . . . . . . . . 49

4. FAULTING TREND ANALYSIS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51

Initial Faulting Measurement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51

Time-Series Faulting Trend Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51

Faulting Versus IRI Data Trends . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55

Effect of Various Design Features and Site Conditions on Faulting . . . . . . . . . . . . . . . . 61

Summary of Faulting Trend Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71

5. SUMMARY . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75

Faulting Data Availability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75

Faulting Data Quality . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75

Computed Faulting Indices and Summary Statistics . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77

Faulting Trend Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77

Faulting Rate Versus IRI Rate for JPCP Sections . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77

Effects of Various Design Features and Site Conditions on Faulting . . . . . . . . . . . . . . . 78

6. RECOMMENDATIONS FOR FUTURE RESEARCH . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79

REFERENCES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81
iv

TABLE OF CONTENTS (Continued)

Section Page

APPENDIX A – LTPP PCC JOINT FAULTING DATABASE SCHEMA AND QC

SPECIFICATIONS (Dated June 29, 1999) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83

APPENDIX B – AVERAGE FAULTING AND AVERAGE IRI TIME HISTORY

PLOTS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87
v

LIST OF FIGURES

Figure Page

 1 Example of faulting profile for GPS-3 section 063005, survey date: 

August 10, 1992. ........................................................6

 2 Example of faulting profile for GPS-4 section 314019. ..........................6

 3 Faulting of transverse joints and cracks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

 4 Example of the negative mirror image faulting (GPS-3 section 067458) . . . . . . . . . . . . 13

 5 Example of consistent negative faulting (GPS-3 section 893016) . . . . . . . . . . . . . . . . . 14

 6 Comparison of faulting frequency distribution for sections subjected to traffic loading

and sections without traffic (SPS-8 experiment) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16

 7 Frequency distribution of the difference between wheelpath and edge faulting data . . . 21

 8 Example of questionable trend between historical and monitoring ESALs for section

124000. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29

 9 Flow chart for computation of faulting statistics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42

 10 Outlier frequency distribution plots for sections with outliers . . . . . . . . . . . . . . . . . . . . 47

 11 Representative examples of sections with outliers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48

 12 Frequency distribution of the average faulting values from the first survey after

construction for SPS-2 sites . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52

 13 Example of a reasonable average faulting trend with time . . . . . . . . . . . . . . . . . . . . . . . 54

 14 Example of a questionable average faulting trend with time . . . . . . . . . . . . . . . . . . . . . . 54

 15 Example of "good" correlation between faulting and IRI trends . . . . . . . . . . . . . . . . . . . 56

 16 Example of "poor" correlation between faulting and IRI trends . . . . . . . . . . . . . . . . . . . 56

 17 Average faulting versus average IRI for JPCP sections (initial IRI not considered) . . . . 57

 18 Average faulting versus average IRI for JRCP sections (initial IRI not considered) . . . . 57

 19 Cumulative faulting versus average IRI for JPCP sections . . . . . . . . . . . . . . . . . . . . . . . 58

 20 Cumulative faulting versus average IRI for JRCP sections (initial IRI not

considered) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58

 21 Rate of change of IRI versus rate of change of average faulting values . . . . . . . . . . . . . 60

 22 Frequency distribution of pavement section ages at the times of faulting surveys . . . . . 62

 23 General trend of faulting development with time for doweled and non-doweled JPCP

sections . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64

 24 Effect of dowel bar diameter on faulting of JPCP . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66

 25 Joint spacing frequency distribution for different types of JCP . . . . . . . . . . . . . . . . . . . . 68

 26 Distribution of CESALs with age . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70

 27 Comparison of mean faulting values for JPCP and JRCP sections located in different

climatic zones . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72

 28 Time series plots for GPS-3 sections 013028, 047614, 053011, 063005, 063010,

and 063013 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 88

 29 Time series plots for GPS-3 sections 063017, 063019, 063021, 063024, 063030,

and 063042 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89

 30 Time series plots for GPS-3 sections 067456, 067493, 083032, 087776, 123804,

and 123811 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90
vi

LIST OF FIGURES (Continued)

Figure Page

 31 Time series plots for GPS-3 sections 124000, 124057, 124059, 124109, 124138,

and 133007 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91

 32 Time series plots for GPS-3 sections 133011, 133015, 133016, 133017, 133018,

and 133019 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92

 33 Time series plots for GPS-3 sections 133020, 163017, 163023, 183002, 183003,

and 183030 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93

 34 Time series plots for GPS-3 sections 183031, 193006, 193009, 193028, 193055,

and 203013 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 94

 35 Time series plots for GPS-3 sections 203060, 213016, 233013, 233014, 263069,

and 273003 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95

 36 Time series plots for GPS-3 sections 273005, 273007, 273009, 273010, 273012,

and 273013 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 96

 37 Time series plots for GPS-3 sections 283018, 283019, 313018, 313023, 313028,

and 323010 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97

 38 Time series plots for GPS-3 sections 323013, 327084, 353010, 373008, 373011,

and 373044 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 98

 39 Time series plots for GPS-3 sections 373807, 373816, 383005, 383006, 393013,

and 393801 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99

 40 Time series plots for GPS-3 sections 403018, 404157, 404160, 404162, 421623,

and 423044 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 100

 41 Time series plots for GPS-3 sections 453012, 463009, 463010, 463012, 463013,

and 463053 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101

 42 Time series plots for GPS-3 sections 466600, 483003, 483010, 483589, 493010,

and 493011 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102

 43 Time series plots for GPS-3 sections 493015, 497082, 497083, 497085, 497086, 

and 533011 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103

 44 Time series plots for GPS-3 sections 533013, 533014, 533019, 533812, 533813,

and 537409 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 104

 45 Time series plots for GPS-3 sections 553008, 553009, 553010, 553012, 553015,

and 553016 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105

 46 Time series plots for GPS-3 sections 556352, 556353, 556354, 556355, 563027,

and 723008 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 106

 47 Time series plots for GPS-3 sections 724121, 833802, 843803, 893001, 893002,

and 893015 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107

 48 Time series plots for GPS-3 section 893016 and GPS-4 sections 014007, 014084,

053059, 053073, and 053074 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 108

 49 Time series plots for GPS-4 sections 054019, 054021, 054023, 054046, 094008,

and 101201 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 109

 50 Time series plots for GPS-4 sections 104002, 174074, 174082, 175217, 179327,

and 184021 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 110
vii

LIST OF FIGURES (Continued)

Figure Page

 51 Time series plots for GPS-4 sections 204016, 204052, 204053, 204054, 214025,

and 224001 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 111

 52 Time series plots for GPS-4 sections 264015, 274033, 274034, 274037, 274040,

and 274054 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 112

 53 Time series plots for GPS-4 sections 274055, 284024, 294036, 294069, 295000,

and 295081 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 113

 54 Time series plots for GPS-4 sections 295091, 295503, 314019, 344042, 364017,

and 364018 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 114

 55 Time series plots for GPS-4 sections 394018, 421606, 421690, 421691, 483699,

and 484142 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 115

 56 Time series plots for GPS-4 sections 484143, 484146, 484152, 544003, and 544004,

and GPS-9 section 069048 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 116

 57 Time series plots for GPS-9 sections 069049, 069107, 089019, 089020, 189020,

and 209037 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 117

 58 Time series plots for GPS-9 sections 269029, 269030, 279075, 289030, 395569,

and 399022 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 118

 59 Time series plots for GPS-9 sections 421627, 429027, 489167, 489355, and 899018,

and SPS-2 section 040213 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 119

 60 Time series plots for SPS-2 sections 040214, 040215, 040216, 040217, 040218,

and 040219 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 120

 61 Time series plots for SPS-2 sections 040220, 040221, 040222, 040223, 040224,

and 080213 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 121

 62 Time series plots for SPS-2 sections 080214, 080215, 080218, 080220, 080222,

and 190213 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 122

 63 Time series plots for SPS-2 sections 190214, 190215, 190216, 190217, 190218,

and 190219 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 123

 64 Time series plots for SPS-2 sections 190220, 200201, 200202, 200203, 200204,

and 200205 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 124

 65 Time series plots for SPS-2 sections 200206, 200207, 200208, 260213, 260214,

and 260215 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 125

 66 Time series plots for SPS-2 sections 260217, 260218, 260219, 260220, 260221,

and 260222 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 126

 67 Time series plots for SPS-2 sections 260223, 260224, 370201, 370202, 370203,

and 370204 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 127

 68 Time series plots for SPS-2 sections 370205, 370206, 370207, 370208, 370209,

and 370210 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 128

 69 Time series plots for SPS-2 sections 370211, 370212, 380213, 380214, 380215,

and 380216 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 129

 70 Time series plots for SPS-2 sections 380217, 380218, 380219, and 380220 and

SPS-4 sections 04A410 and 04A430 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 130
viii

LIST OF FIGURES (Continued)

Figure Page

 71 Time series plots for SPS-4 sections 05B410, 05B430, 05C410, 05C430, 06A410,

and 06A420 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 131

 72 Time series plots for SPS-4 sections 06A430, 06B410, 06B420, 06B430, 18A410,

and 18A430 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 132

 73 Time series plots for SPS-4 sections 19B410, 19B430, 32A410, 32A420, 32A430,

and 39B410 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 133

 74 Time series plots for SPS-4 sections 39B430, 42A410, 42A430, 42C410, 42C430,

and 48B410 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 134

 75 Time series plots for SPS-4 sections 48B420, 48B430, 48C410, 48C420, 48C430,

and 48D410 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 135

 76 Time series plots for SPS-4 sections 48D420, 48D430, 48E410, 48E420, 48E430,

and 49C410 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 136

 77 Time series plots for SPS-4 sections 49C430, 49D410, 49D430, 49E410, and

49E430, and SPS-6 section 170601 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 137

 78 Time series plots for SPS-6 sections 170602, 170605, 180602, 180605, and 460601,

and SPS-8 section 390809 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 138

 79 Time series plots for SPS-8 section 390810 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 139
ix

LIST OF TABLES

Table Page

 1 Summary of the availability of faulting data by experiment type ...................7

 2 Total number of faulting surveys ............................................7

 3 List of sections with excessive number of missing crack faulting observations ........8

 4 List of sections with excessive number of missing joint faulting observations .........9

 5 List of surveys with 25 percent or more negative faulting values less than -1 mm. . . . . 12

 6 Mismatched joints in faulting surveys, inventory records, and distress maps . . . . . . . . . 17

 7 Missing GPS inventory information considered critical for faulting analysis . . . . . . . . . 23

 8 Missing noncritical GPS inventory information useful for faulting analysis . . . . . . . . . . 24

 9 Missing SPS inventory information considered critical for faulting analysis . . . . . . . . . 25

 10 Missing noncritical SPS inventory information useful for faulting analysis . . . . . . . . . . 26

 11 Traffic (80-kN ESALs) data availability summary for sections with available

faulting data. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27

 12 Sections missing both historical and monitoring 80-kN ESAL traffic data . . . . . . . . . . . 28

 13 Sections with questionable traffic data (80-kN ESALs, truck factors) . . . . . . . . . . . . . . 29

 14 Data elements used for calculation of climatic parameters . . . . . . . . . . . . . . . . . . . . . . . 31

 15 Sections missing links to weather stations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32

 16 Summary of feedback reports . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35

 17 Faulting data status . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40

 18 Summary of the status of faulting data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41

 19 Outlier statistics summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46

 20 Summary of faulting time-series trend analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53

 21 Sections with questionable faulting time trends . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53

 22 Regression statistics summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59

 23 Regression statistics summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61

 24 t-test results for JPCP sections without dowels . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62

 25 t-test results for doweled JPCP sections . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63

 26 t-test results for JRCP sections . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63

 27 Effect of dowels on JPCP faulting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64

 28 Effect of dowel bar diameter on faulting of JPC pavements over time . . . . . . . . . . . . . . 65

 29 Number of available observations with faulting status 1 . . . . . . . . . . . . . . . . . . . . . . . . . 71

 30 Schema and field definition for table MON_DIS_JPCC_FAULT_SECT . . . . . . . . . . 85

 31 Code list for fields WHEELPATH_FAULT_STATUS and

EDGE_FAULT_STATUS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 86
x
1

1. INTRODUCTION

Background

Faulting of transverse joints and cracks is one of the key distress types for jointed rigid

pavements. The change in faulting with time serves as an important indicator of jointed concrete

pavement (JCP) performance. The greater the faulting, the greater the pavement roughness and

potential erosion and loss of support beneath the slab. Faulting is defined as the difference in

elevation across a joint or a crack and is measured at each joint or crack at two locations, 0.3 m

and 0.76 m from the outside slab edge.

The electronic digital faultmeter developed by the Georgia Department of Transportation is used

for the faulting measurements in LTPP program [1]. The Faultmeter readout provides the

faulting measurement in millimeters and indicates whether the measurement is positive or

negative. Typically, joint mean faulting in excess of 3 mm is considered unacceptable for jointed

plain concrete pavements (JPCP), and mean joint faulting in excess of 6 mm is considered

unacceptable for jointed reinforced concrete pavements (JRCP). The readout unit essentially

limits the precision of faulting measurement to ±1 mm. There have been some concerns that this

level of precision may not be adequate to allow the desired degree of sensitivity in joint faulting

prediction procedures.

Transverse joint and crack faulting is being monitored regularly at the jointed concrete pavement

test sections under the Long-Term Pavement Performance (LTPP) program. Faulting data are

available for the following pavement types: 

• JPCP (GPS-3, SPS-2, SPS-4, SPS-6, and SPS-8).

• JRCP (GPS-4).

• JPCP over JRCP (GPS-9).

• JPCP over JPCP (GPS-9).

• JRCP over JPCP (GPS-9).

• JRCP over JRCP (GPS-9).

• JPCP over continuously reinforced concrete pavement (CRCP) (GPS-9).

• JRCP over CRCP (GPS-9).

The General Pavement Studies (GPS) experiment looks at existing pavements. These pavement

materials and structural designs reflect standard engineering practices in the United States and

Canada. The Specific Pavement Studies (SPS) were designed and constructed to provide the

data required to investigate and quantify the critical factors that affect pavement performance. 

Each SPS project consists of a series of sections at a single location. The sections vary in

structure, maintenance treatments, or rehabilitation strategy, with all other factors being similar. 

The purpose of the SPS-2 experiment is to evaluate the effect of structural factors on rigid

pavement performance. The SPS-4 experiment is designed to study the effectiveness of

preventive maintenance on rigid pavements. The SPS-6 experiment evaluates the rehabilitation

of jointed concrete pavements, and the SPS-8 experiment evaluates environmental effects in the

absence of heavy loads.
2

The spring 1998 LTPP data (release 8.2) were used in this study. Faulting data in the LTPP

Information Management System (IMS) database include faulting measurements at doweled and

non-doweled joints and also some measurements at transverse crack locations. In addition to the

joint-by-joint faulting data currently available in the IMS, it is desirable to have available

representative faulting values and companion statistics for each site for each measurement cycle

(site visit). The availability of computed representative values of edge and wheelpath faulting

would minimize duplication of effort in future analysis work and provide a consistent set of data

to be used for joint and crack faulting studies. The representative faulting values for each test

section can be used for the investigation of time-series trends and for proof testing of the faulting

data. Pavement analysts can make use of the representative faulting indices and statistics to

develop mechanistic-based prediction models for joint faulting. To provide LTPP users with

representative faulting indices and statistics, a new table was developed during the course of the

present study and was proposed for inclusion in the IMS database.

Previous analysis of joint faulting data has identified concerns with some of the data, including

the following:

• The presence of negative faulting values.

• The poor correlation between joint faulting and roughness (in terms of International

Roughness Index [IRI]) at some sites.

• Joint faulting decreasing with time at some sites.

• Large differences between wheelpath and edge faulting at each joint/crack location.

To date, no serious attempt has been made to assess the quality of the faulting data. This report

addresses the assessment of the quality of the faulting data and the development of representative

faulting indices and companion statistics for each site for each measurement cycle (site visit). 

This report also contains the results of faulting data analysis that was obtained using computed

faulting indices. These results address the effect of key pavement design features on faulting

values.

Objectives and Scope of Work

Following are the objectives of this study:

• Examine the quality of the joint faulting data.

• Identify questionable data.

• Provide recommendations for resolving questionable data.

• Develop representative faulting indices and statistics for each JCP test section.

• Perform a limited study of factors that affect joint faulting.

The scope of work included a detailed evaluation of the joint faulting data for 307 doweled and

non-doweled pavement sections. The variability of the faulting data over the 154-m length of

each section was studied, and representative faulting indices and statistics for each section were

determined and grouped in the new MON_DIS_FAULT_SUMMARY table that is included in

the LTPP database. 
3

Report Organization

This report documents the results of the LTPP faulting data assessment. The report consists of

six chapters and two appendices. Chapter 1 discusses the background information, objectives,

and scope of work. Issues related to the assessment of faulting and complementary data quality

and recommendations for resolving data quality issues are addressed in chapter 2. Chapter 3

describes the development of representative faulting indices and statistics for the proposed

computed parameter tables to be included in the IMS database. Chapter 4 presents the results of

the faulting trend analysis conducted using the developed representative faulting indices and

statistics. A summary of findings is presented in chapter 5. Finally, recommendations for future

advances in faulting data quality are presented in chapter 6.

The description of the new IMS table MON_DIS_JPCC_FAULT_SECT containing faulting

indices and summary statistics is given in appendix A. Appendix B provides time-series plots

showing faulting and IRI trends with time for the test sections included in this study.
4
5

2. ASSESSMENT OF FAULTING AND COMPLEMENTARY DATA QUALITY

To determine availability and usability of data for the development of representative faulting

statistics and for the preliminary faulting data analysis, data from LTPP IMS release Quarter 1,

1998, were reviewed in terms of faulting and companion inventory information, including

environmental factors, material characteristics, and traffic. As a result of this study, a

comprehensive list of missing and questionable data was developed. The missing data, which

include both faulting and companion data, were categorized by experiment type, section number,

identification of the missing parameters, and the source of the extracted data. 

Questionable data include both faulting and traffic data. The criteria necessary to identify

questionable faulting data were developed and applied to the faulting database. The criteria

include assessment of negative faulting values, a comparison of wheelpath and edge faulting, rate

of faulting (for sections with two or more observations), and additional factors based on a

thorough review of the faulting data. The questionable traffic data were assessed through

comparison of trends in historical and monitoring equivalent single axle load (ESAL) data and by

comparison of calculated truck factors for each section to an acceptable range (0.5 to 2.5). A

discussion of the quality of the joint (and crack) faulting data and related data in the LTPP IMS

database is presented below.

Joint Faulting Data Quality Evaluation

The April 1998 version of the MON_DIS_JPCC_FAULT data table (LTPP data module MO8)

was obtained from the LTPP IMS. The table contained 24,018 records. This table contained

faulting data for both wheelpath and edge (corner) locations at each joint or transverse crack for

each jointed concrete pavement section in the inventory. Joints and cracks were designated by

the letters J and C, respectively. The locations of the cracks or joints were given as the distance,

in meters, from the beginning of the test section. Each data record provided additional

information regarding joint or crack spalling and sealing. The faulting data records were sorted

by survey date and crack or joint location. Example faulting profiles are given in figure 1 for a

JPCP section with a single faulting survey and in figure 2 for a JRCP section with three faulting

surveys.

Faulting data are available for the sections conducted under GPS-3, GPS-4, GPS-9, 

SPS-2, SPS-4, SPS-6, and SPS-8 experiments. A thorough review of the data indicated that not

all of the sections within these experiments have available faulting data. A summary of the

availability of faulting data within each experiment is presented in table 1. Based on the

currently available faulting data, a total of 307 sections were considered suitable for development

of joint faulting indices and statistics. 
6

-4

-2

0

2

4

6

8

10

12

0 10 20 30 40 50 60 70 80 90 100 110 120 130 140 150

Location, m

Wheelpath Faulting, mm

-2

-1

0

1

2

3

4

5

6

7

8

9

0 10 20 30 40 50 60 70 80 90 100 110 120 130 140 150

Location, m

Wheelpath Faulting, mm

7/31/91 7/19/93 8/12/97

Figure 1. Example of faulting profile for GPS-3 section 063005, survey date: August 10, 1992.

Figure 2. Example of faulting profile for GPS-4 section 314019.
7

Table 1. Summary of the availability of faulting data by experiment type.

Experiment

Type

Total Number of

Sections Released

Number of Sections With

Available Faulting Data

Number of Sections

Lacking Faulting Data

GPS-3 133 121 12

GPS-4 69 52 17

GPS-9 26 18 8

SPS-2 75 65 10

SPS-4 68 43 25

SPS-6 39 6 33

SPS-8 12 2 10

All 422 307 115

For any of the sections with faulting data, the number of faulting surveys varies from one to nine. 

The total number of faulting surveys per section per experiment is presented in table 2. Note that

more than half of the sections with faulting data reported contain two or fewer observations.

Table 2. Total number of faulting surveys.

Experiment

Type

Number of Sections With

Total

1 Sections

Survey

2

Surveys

3

Surveys

4

Surveys

5

Surveys

6

Surveys

7

Surveys

8

Surveys

9

Surveys

10

Surveys

GPS-3 36 33 24 20 2 1 3 1 1 — 121

GPS-4 17 8 13 7 2 1 1 2 1 — 52

GPS-9 10 2 5 1 — — — — — — 18

SPS-2 20 31 8 4 — 1 — 1 — — 65

SPS-4 — 8 9 8 3 6 3 1 — 5 43

SPS-6 — 2 1 2 1 — — — — — 6

SPS-8 — 2 — — — — — — — — 2

Total Sections 83 86 60 42 8 9 7 5 2 5 307

% Distribution 27.0 28.0 19.5 13.7 2.6 2.9 2.3 1.6 0.7 1.6 100

Missing Data

Data for a total of 422 JCP sections were available in the IMS database at the time of the study. 

Out of 422 released sections, 115 sections did not have any records in the faulting data table

MON_JPCC_FAULT. This magnitude of missing data is considered very serious because

faulting is one of the key distress types associated with jointed concrete pavements. Future

efforts should be focused on ensuring that faulting data are collected as required.
8

The records in the faulting table exhibit numerous missing observations. The missing

information may be either a complete lack of faulting data for a section or more than 25 percent

observations missing for a given survey and section. The missing faulting data, differentiated by

GPS or SPS section number—as well as the percentage and type of missing information—were

reported to the Federal Highway Administration (FHWA) in an LTPP Feedback Report. The list

of sections and survey dates with excessive numbers of missing faulting observations is given in

tables 3 and 4 for crack and joint observations, respectively. Missing data were further classified

to identify whether the edge or wheelpath is missing faulting data.

Table 3. List of sections with excessive number of missing crack faulting observations.

Section ID Survey Date Experiment

Type

Number of

Missing

Edge Crack

Point

Locations

Number of

Missing

Wheelpath

Crack Point

Locations

Total

Number of

Crack Point

Locations

% Missing

Edge

Faulting

Data

% Missing

Wheelpath

Faulting

Data

014084 19-Sep-91 GPS-4 13 17 76

053059 11-Sep-91 GPS-4 2 2 100

054021 10-Sep-91 GPS-4 3 3 100

054021 29-Nov-94 GPS-4 4 4 4 100 100

054046 11-Jun-97 GPS-4 1 1 1 100 100

063030 18-Mar-97 GPS-3 3 7 43

06B410 11-Jan-94 SPS-4 13 13 13 100 100

06B420 11-Jan-94 SPS-4 12 12 12 100 100

06B430 11-Jan-94 SPS-4 6 6 6 100 100

123811 03-Oct-91 GPS-3 4 14 29

174074 08-May-91 GPS-4 13 13 100

174082 07-May-91 GPS-4 16 16 100

224001 11-Jul-94 GPS-4 8 8 8 100 100

294069 04-Feb-91 GPS-4 24 24 100

32A420 09-Aug-91 SPS-4 6 6 100

483699 09-Jul-91 GPS-4 1 2 50

484152 02-Apr-92 GPS-4 10 10 100

48C410 20-Jun-91 SPS-4 1 1 100

48C420 20-Jun-91 SPS-4 1 1 100

48C430 20-Jun-91 SPS-4 3 3 100

48D410 29-Jun-90 SPS-4 9 9 100

48D410 10-Jul-91 SPS-4 9 9 100

48D410 02-Apr-92 SPS-4 9 9 100

48D420 29-Jun-90 SPS-4 16 16 100

48D420 10-Jul-91 SPS-4 15 15 100

48D420 02-Apr-92 SPS-4 16 16 100

48D430 29-Jun-90 SPS-4 13 13 100

48D430 10-Jul-91 SPS-4 16 16 100

48D430 02-Apr-92 SPS-4 16 16 100

49C410 27-Jan-92 SPS-4 1 1 1 100 100

49C430 21-Jul-93 SPS-4 1 1 1 100 100

724121 18-Jan-90 GPS-3 13 13 100

724121 28-Feb-91 GPS-3 13 13 100
9

Table 4. List of sections with excessive number of missing

joint faulting observations.

Section ID Survey Date Experiment

Type

Number of

Missing

Edge Joint

Point

Locations

Number of

Missing

Wheelpath

Joint Point

Locations

Total

Number of

Joint Point

Locations

% Missing

Edge

Faulting

Data

% Missing

Wheelpath

Faulting

Data

01-3028 19-Sep-91 GPS-3 23 25 92

01-4007 19-Sep-91 GPS-4 5 14 36

01-4084 19-Sep-91 GPS-4 7 9 78

04-7614 15-Dec-94 GPS-3 34 34 100

04-A410 13-Jul-95 GPS-4 35 35 35 100 100

04-A430 13-Jul-95 GPS-4 39 39 39 100 100

05-3059 11-Sep-91 GPS-4 33 33 100

05-3073 10-Sep-91 GPS-4 9 33 27

05-3074 11-Sep-91 GPS-4 28 33 85

05-4019 11-Sep-91 GPS-4 34 34 100

05-4021 15-May-91 GPS-4 33 33 100

05-4021 10-Sep-91 GPS-4 32 33 97

05-4021 29-Nov-94 GPS-4 33 33 33 100 100

05-4046 10-Sep-91 GPS-4 9 33 27

05-4046 11-Jun-97 GPS-4 35 35 35 100 100

05-B410 11-Sep-91 GPS-4 33 33 100

05-B430 11-Sep-91 GPS-4 33 33 100

05-C410 09-Sep-91 GPS-4 34 34 100

05-C430 09-Sep-91 GPS-4 34 34 100

06-B410 11-Jan-94 GPS-4 32 32 32 100 100

06-B420 11-Jan-94 GPS-4 29 29 29 100 100

06-B430 11-Jan-94 GPS-4 31 31 31 100 100

12-3811 03-Oct-91 GPS-3 22 25 88

13-3011 24-Sep-91 GPS-3 9 26 35

13-3015 24-Sep-91 GPS-3 10 25 40

13-3016 23-Sep-91 GPS-3 9 25 36

13-3017 24-Sep-91 GPS-3 21 26 81

17-4074 08-May-91 GPS-4 13 13 100

17-4082 07-May-91 GPS-4 13 13 100

18-3031 01-May-91 GPS-3 32 32 100

20-0201 06-Apr-93 SPS-2 32 32 32 100 100

20-0202 07-Apr-93 SPS-2 32 32 32 100 100

20-0203 05-Apr-93 SPS-2 31 31 31 100 100

20-0204 06-Apr-93 SPS-2 31 31 31 100 100

20-0205 08-Apr-93 SPS-2 32 32 32 100 100

20-0206 07-Apr-93 SPS-2 27 27 27 100 100

20-0207 08-Apr-93 SPS-2 32 32 32 100 100

20-0208 09-Apr-93 SPS-2 32 32 32 100 100

22-4001 04-Nov-91 GPS-4 9 9 100

22-4001 11-Jul-94 GPS-4 9 9 9 100 100

28-3018 01-Nov-91 GPS-3 14 26 54

28-4024 11-Sep-91 GPS-4 8 8 100

29-4069 04-Feb-91 GPS-4 8 8 100

32-A420 09-Aug-91 GPS-4 33 33 100

40-3018 08-Oct-91 GPS-3 26 33 79
Table 4. List of sections with excessive number of missing

joint faulting observations (continued).

Section ID Survey Date Experiment

Type

Number of

Missing

Edge Joint

Point

Locations

Number of

Missing

Wheelpath

Joint Point

Locations

Total

Number of

Joint Point

Locations

% Missing

Edge

Faulting

Data

% Missing

Wheelpath

Faulting

Data

10

40-3018 03-Nov-94 GPS-3 27 27 27 100 100

40-4160 16-Oct-91 GPS-3 19 32 59

45-3012 16-Mar-92 GPS-3 23 23 100

48-3010 02-Apr-92 GPS-3 30 30 100

48-3589 20-Jun-91 GPS-3 33 34 97

48-3699 09-Jul-91 GPS-4 11 24 46

48-4142 03-Apr-92 GPS-4 25 25 100

48-4146 02-Apr-92 GPS-4 33 33 100

48-4152 02-Apr-92 GPS-4 17 17 100

48-B410 05-Sep-89 GPS-4 25 25 100

48-B410 29-Jun-90 GPS-4 25 25 100

48-B410 11-Jul-91 GPS-4 25 25 100

48-B410 03-Apr-92 GPS-4 25 25 100

48-B420 05-Sep-89 GPS-4 25 25 100

48-B420 29-Jun-90 GPS-4 25 25 100

48-B420 11-Jul-91 GPS-4 25 25 100

48-B420 03-Apr-92 GPS-4 25 25 100

48-B430 05-Sep-89 GPS-4 25 25 100

48-B430 29-Jun-90 GPS-4 25 25 100

48-B430 11-Jul-91 GPS-4 25 25 100

48-B430 03-Apr-92 GPS-4 25 25 100

48-C410 03-Dec-90 GPS-4 33 33 33 100 100

48-C410 20-Jun-91 GPS-4 33 33 100

48-C420 03-Dec-90 GPS-4 33 33 33 100 100

48-C420 20-Jun-91 GPS-4 33 33 100

48-C430 03-Dec-90 GPS-4 33 33 33 100 100

48-C430 20-Jun-91 GPS-4 33 33 100

48-D410 29-Jun-90 GPS-4 18 18 100

48-D410 10-Jul-91 GPS-4 18 18 100

48-D410 02-Apr-92 GPS-4 18 18 100

48-D420 29-Jun-90 GPS-4 17 17 100

48-D420 10-Jul-91 GPS-4 17 17 100

48-D420 02-Apr-92 GPS-4 17 17 100

48-D430 29-Jun-90 GPS-4 17 17 100

48-D430 10-Jul-91 GPS-4 17 17 100

48-D430 02-Apr-92 GPS-4 17 17 100

48-E410 28-Jun-90 GPS-4 25 25 100

48-E410 11-Jul-91 GPS-4 25 25 100

48-E410 03-Apr-92 GPS-4 25 25 100

48-E420 28-Jun-90 GPS-4 24 24 100

48-E420 11-Jul-91 GPS-4 24 24 100

48-E420 03-Apr-92 GPS-4 24 24 100

48-E430 28-Jun-90 GPS-4 25 25 100

48-E430 11-Jul-91 GPS-4 25 25 100

48-E430 03-Apr-92 GPS-4 25 25 100
Table 4. List of sections with excessive number of missing

joint faulting observations (continued).

Section ID Survey Date Experiment

Type

Number of

Missing

Edge Joint

Point

Locations

Number of

Missing

Wheelpath

Joint Point

Locations

Total

Number of

Joint Point

Locations

% Missing

Edge

Faulting

Data

% Missing

Wheelpath

Faulting

Data

11

49-3010 01-Aug-91 GPS-3 33 33 100

49-3011 30-Nov-93 GPS-3 33 33 33 100 100

49-7086 06-Jul-94 GPS-3 38 38 40 95 95

49-C410 21-Jul-93 GPS-4 41 41 41 100 100

49-C430 21-Jul-93 GPS-4 47 47 47 100 100

49-E410 10-Jul-91 GPS-4 40 40 40 100 100

49-E430 10-Jul-91 GPS-4 40 40 40 100 100

56-3027 18-Aug-94 GPS-3 32 32 32 100 100

72-3008 22-Jan-90 GPS-3 22 26 85

72-4121 18-Jan-90 GPS-3 27 27 100

72-4121 28-Feb-91 GPS-3 27 27 100

83-3802 09-Jun-93 GPS-3 35 35 35 100 100

Negative Faulting Values

 A slab that is lower on the leave side of the joint will register as positive faulting, which is the

typical case. If the leave side of the joint is higher, then negative faulting will be registered. 

Cases of positive and negative faulting are shown in figure 3. The preliminary assessment of

questionable faulting data revealed a number of sections with negative faulting values. At least

one negative faulting value was recorded for 52 percent of all sections evaluated. However, the

total number of negative faulting measurements per section is very low. As a result, the total

number of negative faulting points in the faulting data table is 4 percent of the total number of

faulting measurements, and negative faulting measurements less than -1 mm are only 1 percent. 

In most cases, the negative values were random occurrences, with a few repeated at the same

joint/crack locations. Since only 45 percent of all the sections considered had faulting data from

more than two surveys, trends in the negative values are difficult to assess. A list of sections and

survey dates with a large number of points with negative faulting values less than -1 mm (25

percent or more) is given in table 5.

While reasons for negative faulting values of -1 mm (majority of negative faulting

measurements) can be attributed to the precision of the Georgia Faultmeter, reasons for negative

faulting values that are less than -1 mm were investigated. It was discovered that on certain

survey dates, sections 067456, 344042, 364018, 497085, 533019, 533813, and 833802 exhibited

negative faulting profiles that are mirror images of the positive faulting profile measured on a

different date. An example of the negative mirror image faulting is given in figure 4. This

phenomenon was reported to FHWA, and the response from the Regional Centers indicated that

in some cases the mirror image occurred because the faultmeter was turned in the wrong

direction during data collection. In other cases, negative faulting values were attributed to

faulting measurements over patched or sealed joints.
12

Traffic

Traffic

A B

CL Joint

Joint Crack

A B

Fault (positive) Fault (negative)

Shoulder

Edge

Joint

(Plan View)

(Section)

Figure 3. Faulting of transverse joints and cracks.

Table 5. List of surveys with 25 percent or more negative faulting values less than -1 mm.

Section ID Survey Date Crack or

Joint

% Edge

Faulting Data

<= -2 mm 

 % Wheelpath

Faulting Data

<= -2 mm

Total Number

of Point

Locations

06-9048 29-Jan-97 C 50 2

55-3010 25-Feb-92 C 100 100 1

53-3813 18-Jul-95 C 50 2

05-4021 15-May-91 J 58 33

06-3042 20-Jun-96 J 100 97 32

06-7456 19-Oct-95 J 82 70 33

17-0602 26-Jun-92 J 31 13

34-4042 28-Jun-95 J 29 7

34-4042 10-Oct-96 J 29 7

48-3589 05-Aug-93 J 29 34

49-3015 07-Jul-94 J 100 100 40

49-7085 08-Jul-94 J 100 100 40

53-3813 25-Jun-96 J 100 97 34

83-3802 29-Mar-95 J 30 33 27
13

Joint and Crack Faulting Along Wheelpath

-10

-8

-6

-4

-2

0

2

4

6

8

0 10 20 30 40 50 60 70 80 90 100 110 120 130 140 150

Location, m

Faulting, mm

Wheelpath, 12/17/91 Wheelpath, 10/19/95

Figure 4. Example of the negative mirror image faulting (GPS-3 section 067458).

Several sections consistently show high negative faulting values from visit to visit at the same

locations. These sections are: 063010, 180602, 180605, 553010, and 893016. Also, section

180602 exhibits negative faulting of almost 20 mm at one edge joint location. Examples of

consistent negative faulting are shown in figure 5.

To investigate the possible reasons for negative faulting, SPS-2 sites were used to examine “asbuilt” faulting. These sites contain monitoring history from the beginning of a pavement’s inservice life. When faulting records obtained from the first faulting survey since construction

were initially investigated, it was found that 40 percent of SPS-2 sections had at least one joint

that exhibited a negative faulting value. However, this number of sections was greatly reduced to

4.6 percent when negative faulting records of -1 mm were excluded. The substantial number of

joints with negative faulting of -1 mm on a first survey since construction could be attributed to

random positive and negative measurement variation taken on joints with zero faulting, as would

be expected for new construction (built-in surface texture and the precision of the Georgia

Faultmeter being ±1 mm).

To find an explanation for negative faulting, a hypothesis was tested whereby negative faulting

values can be explained by the fact that “faulting is more of a joint step-off due to slab curling

and/or warping caused by environmental factors rather than ESAL loading.” This hypothesis was

developed and well documented by Gordon Wells of Caltrans [2]. To test the hypothesis,

frequency distributions of measured faulting values were compared between SPS-8

environmental (no traffic loading) sections and the rest of LTPP concrete sections.
14

Joint and Crack Faulting Along W heelpath

-4

-3

-2

-1

0

1

2

0 10 20 30 40 50 60 70 80 90 100 110 120 130 140 150

Location, m

Wheelpath Faulting, mm

7/17/91 7/16/93 7/20/95 6/3/97

Joint and Crack Faulting Along Edge

-4

-3

-2

-1

0

1

2

0 10 20 30 40 50 60 70 80 90 100 110 120 130 140 150

Location, m

Edge Faulting, mm

7/17/91 7/16/93 7/20/95 6/3/97

Figure 5. Example of consistent negative faulting (GPS-3 section 893016).
15

The histograms of comparison in figure 6 show that, in absence of traffic loading, a very minor

percentage of faulting measurements for SPS-8 sections were outside the limits of the Georgia

Faultmeter precision of ±1 mm. For SPS-8 sections tested, only 4 percent of measurements were

outside the precision range of the Georgia Faultmeter (±1 mm). For these 4 percent of faulting

observations, the probability of positive and negative faulting values was about the same for

SPS-8 sections. 

The frequency distributions of faulting measurements for sections exposed to traffic loading were

found to be distinctly different from the frequency distributions for environmental sections. 

These distributions were clearly skewed toward positive faulting values with a very small

percentage of measurements being less than -1 mm. This observation means that LTPP sites,

unlike the sites used in Caltrans study, develop faulting primarily because of traffic loading rather

than environmental curling or warping. Traffic loads lead to the positive faulting values. For

LTPP sites examined in this study, negative faulting values less than -1 mm constituted only 1

percent of total faulting observations and were found to be the exception than the rule.

To develop representative faulting indices and statistics, negative faulting values of -1 mm were

included. Negative faulting values of -2 mm or less were not considered because of the

inconsistency with faulting development mechanisms. Whenever a large negative faulting occurs

at a joint, it is usually caused by a settlement of the approach joint, or a repair placed at the joint

that was not finished properly, or excessive sealant on the leave side of the joint. These causes

are very different from the pumping-erosion mechanism that traditionally causes faulting. The

decision was made not to use surveys with more than 25 percent of excessive negative faulting

measurements (-2 mm or less) in the development of representative faulting indices and statistics

until the reasons for excessive negative faulting could be explained. Since the number of surveys

with more than 25 percent of excessive negative faulting measurements was only 1 percent of the

total number of surveys, the decision had little impact on the quantity of data used for faulting

trend analysis. Responses to the submitted LTPP Feedback Reports indicated that, in some

cases, excessive negative faulting values resulted from faulting measurements over:

• Improperly sealed joints.

• Partial depth spall repairs.

• Full-depth repair patches.

• Misuse of the device (faultmeter was turned in the wrong direction).

These measurements did not represent true faulting at the joint and, therefore, were not used in

computing faulting indices and summary statistics.

Mismatched Joints

Faulting data were recorded for each crack or joint within a section. The crack and joint

locations are based on a measurement from the beginning of the section. During the process of

faulting data evaluation, a large number of mismatched joints was encountered. The cases of

mismatched joint locations can be divided into the following groups: 
16

Figure 6. Comparison of faulting frequency distribution for sections subjected to traffic

 loading and sections without traffic (SPS-8 experiment).
17

• Joint locations do not coincide from one survey to another.

• Number of joints changes from one survey to another.

• Number of joints from faulting survey is significantly different from the number obtained

from the inventory record or distress survey maps.

If joint locations did not coincide from one survey to another, the differences were assumed to be

the result of measurement errors or oversight. Joints and cracks that showed similar locations

(within 0.5 m) between surveys were considered reliable data. Joint and crack locations that

were not within 0.5 m were regarded as erroneous data.

Furthermore, the total number of joint data was compared with the following three sources: 1)

number of joints surveyed at a specific survey date, 2) number of joints counted from PASCO

distress maps, and 3) number of joints computed from joint spacing data in IMS database table

INV_PCC_JOINT. For a large number of section surveys, the number of joints from faulting

surveys did not match the number based on inventory joint spacing data, and for some sections

the number of joints from PASCO distress maps is different from the number from the faulting

survey. It is likely that data in INV_PCC_JOINT are incorrect for those test sections where there

is no difference between the number of joints from the distress map and the number of joints

from the faulting survey. Table 6 contains a list of sections and survey dates for which the

number of joints from faulting surveys did not coincide with the number from an inventory

record or with the number from the distress maps. A total of 124 faulting survey dates had

numbers of joints that did not match the inventory-based number, and 35 faulting survey dates

had numbers of joints that did not match the number of joints from PASCO distress maps. 

Sections with mismatched numbers of joints were reported to FHWA in LTPP Feedback Reports.

Table 6. Mismatched joints in faulting surveys, inventory records, and distress maps.

State

Code

SHRP

ID

Experiment

Type Survey Date Random JointSpacing

Number of

Joints From

Inventory

Number of

Joints

From

Distress

Map

Number of

Joints

From

Faulting

Survey

Difference

B/w Inventory

and Faulting

Number of

Joints

Difference

Between

Distress

Faulting and

No. of Joints

5 3059 GPS-4 09-Sep-91 11 33 30 -19 3

5 3059 GPS-4 11-Sep-91 11 33 33 -22 0

5 3059 GPS-4 06-Dec-94 11 33 33 -22 0

5 3059 GPS-4 07-Aug-97 11 33 33 -22 0

5 3073 GPS-4 10-Sep-91 11 33 33 -22 0

5 3073 GPS-4 28-Nov-94 11 33 33 -22 0

5 3073 GPS-4 09-Jun-97 11 33 33 -22 0

5 3074 GPS-4 11-Sep-91 11 33 33 -22 0

5 3074 GPS-4 02-Dec-94 11 33 33 -22 0

5 3074 GPS-4 11-Jun-97 11 33 33 -22 0

5 4019 GPS-4 11-Sep-91 11 33 34 -23 -1

5 4019 GPS-4 05-Dec-94 11 33 33 -22 0

5 4019 GPS-4 05-Aug-97 11 33 33 -22 0

5 4021 GPS-4 15-May-91 11 33 33 -22 0

5 4021 GPS-4 10-Sep-91 11 33 33 -22 0

5 4021 GPS-4 29-Nov-94 11 33 33 -22 0

5 4023 GPS-4 10-Sep-91 11 33 32 -21 1

5 4023 GPS-4 30-Nov-94 11 33 34 -23 -1
Table 6. Mismatched joints in faulting surveys, inventory records, and distress maps (continued).

State

Code

SHRP

ID

Experiment

Type Survey Date Random JointSpacing

Number of

Joints From

Inventory

Number of

Joints

From

Distress

Map

Number of

Joints

From

Faulting

Survey

Difference

B/w Inventory

and Faulting

Number of

Joints

Difference

Between

Distress

Faulting and

No. of Joints

18

95 4023 GPS-4 06-Aug-97 11 33 33 -22 0

5 4046 GPS-4 10-Sep-91 11 33 33 -22 0

5 4046 GPS-4 01-Dec-94 11 33 33 -22 0

5 4046 GPS-4 11-Jun-97 11 33 35 -24 -2

6 3005 GPS-3 10-Aug-92 12,13,19,18 32 35 35 -3 0

6 3024 GPS-3 20-Nov-91 13,12,15,14 37 32 32 5 0

6 3024 GPS-3 13-Feb-97 13,12,15,14 37 32 32 5 0

6 7493 GPS-3 20-Nov-91 13,19,18,12 32 38 38 -6 0

6 7493 GPS-3 30-Jan-97 13,19,18,12 32 38 37 -5 1

8 7776 GPS-3 10-Apr-92 36 38 38 -2 0

8 9020 GPS-9 09-Apr-92 25 37 38 -13 -1

10 4002 GPS-4 15-Jun-93 11 12 17 -6 -5

12 4138 GPS-3 05-Oct-91 20-22-18 25 29 22 3 7

12 4138 GPS-3 21-Apr-97 20-22-18 25 30 29 -4 1

17 0602 SPS-6 17-Dec-91 10 13 13 -3 0

17 0602 SPS-6 26-Jun-92 10 13 13 -3 0

17 0602 SPS-6 03-Aug-93 10 13 13 -3 0

17 0602 SPS-6 20-Jun-95 10 13 13 -3 0

17 0605 SPS-6 17-Dec-91 5 27 27 -22 0

17 0605 SPS-6 02-Jul-92 5 27 27 -22 0

17 0605 SPS-6 03-Aug-93 5 27 27 -22 0

17 0605 SPS-6 21-Jun-95 5 27 27 -22 0

17 0605 SPS-6 02-Jul-95 5 27 23 -18 4

18 0602 SPS-6 10-Sep-92 25 50 50 -25 0

18 0602 SPS-6 10-Aug-93 25 50 50 -25 0

18 0605 SPS-6 10-Sep-92 25 50 50 -25 0

18 0605 SPS-6 10-Aug-93 25 50 50 -25 0

19 0213 SPS-2 18-Oct-94 33 N/A 30 3

19 0214 SPS-2 18-Oct-94 33 N/A 31 2

19 0215 SPS-2 17-Oct-94 33 N/A 30 3

19 0216 SPS-2 18-Oct-94 33 N/A 31 2

19 0217 SPS-2 17-Oct-94 33 N/A 31 2

20 0203 SPS-2 05-Apr-93 33 33 31 2 2

20 0204 SPS-2 06-Apr-93 33 33 31 2 2

20 0205 SPS-2 27-May-97 33 33 31 2 2

20 0206 SPS-2 07-Apr-93 33 33 27 6 6

20 4016 GPS-4 01-May-91 8 10 10 -2 0

20 4016 GPS-4 27-Apr-93 8 10 10 -2 0

20 9037 GPS-9 12-May-94 15’+-12" 33 34 36 -3 -2

21 3016 GPS-3 18-Apr-91 12-13-17-18 33 34 1 32 33

27 4040 GPS-4 28-Jul-93 19 19 23 -4 -4

31 3018 GPS-3 19-Apr-95 18 ft. 32 33 36 -4 -3

37 3044 GPS-3 05-Nov-91 17 20 20 -3 0

37 3044 GPS-3 27-Apr-93 17 20 20 -3 0

37 3044 GPS-3 17-Jul-95 17 20 21 -4 -1

39 4018 GPS-4 30-Apr-91 13 12 11 2 1

40 3018 GPS-3 03-Nov-94 33 33 27 6 6
Table 6. Mismatched joints in faulting surveys, inventory records, and distress maps (continued).

State

Code

SHRP

ID

Experiment

Type Survey Date Random JointSpacing

Number of

Joints From

Inventory

Number of

Joints

From

Distress

Map

Number of

Joints

From

Faulting

Survey

Difference

B/w Inventory

and Faulting

Number of

Joints

Difference

Between

Distress

Faulting and

No. of Joints

19

40 4160 GPS-3 18-Feb-91 33 32 10 23 22

42 1691 GPS-4 13-Oct-89 8 N/A 13 -5

46 6600 GPS-3 24-Apr-95 16’-17’-21’-22’ 27 27 30 -3 -3

48 3010 GPS-3 10-Jul-91 33 33 30 3 3

48 3010 GPS-3 02-Apr-92 33 33 30 3 3

48 3699 GPS-4 09-Jul-91 8 24 24 -16 0

48 3699 GPS-4 27-Apr-93 8 24 24 -16 0

48 3699 GPS-4 06-Jun-95 8 24 24 -16 0

48 4142 GPS-4 11-Jul-91 8 25 25 -17 0

48 4142 GPS-4 03-Apr-92 8 25 25 -17 0

48 4142 GPS-4 30-Apr-93 8 25 25 -17 0

48 4142 GPS-4 11-Jan-95 8 25 25 -17 0

48 4142 GPS-4 11-Apr-95 8 25 25 -17 0

48 4142 GPS-4 08-Jun-95 8 24 24 -16 0

48 4142 GPS-4 08-Jul-97 8 25 25 -17 0

48 4142 GPS-4 26-Sep-97 8 25 25 -17 0

48 4143 GPS-4 11-Jul-91 8 25 25 -17 0

48 4143 GPS-4 03-Apr-92 8 25 25 -17 0

48 4143 GPS-4 29-Apr-93 8 25 25 -17 0

48 4143 GPS-4 10-Jan-95 8 25 25 -17 0

48 4143 GPS-4 10-Apr-95 8 25 25 -17 0

48 4143 GPS-4 08-Jun-95 8 25 25 -17 0

48 4143 GPS-4 09-Jul-97 8 25 25 -17 0

48 4143 GPS-4 25-Sep-97 8 25 25 -17 0

48 4146 GPS-4 10-Jul-91 8 33 33 -25 0

48 4146 GPS-4 02-Apr-92 8 33 33 -25 0

48 4146 GPS-4 28-Apr-93 8 33 33 -25 0

48 4146 GPS-4 07-Jun-95 8 33 33 -25 0

53 3011 GPS-3 07-May-97 44 43 42 2 1

53 3813 GPS-3 18-Jul-95 13,19,18,12 32 34 34 -2 0

53 3813 GPS-3 20-Nov-95 13,19,18,12 32 34 34 -2 0

53 3813 GPS-3 21-Feb-96 13,19,18,12 32 34 34 -2 0

53 3813 GPS-3 25-Jun-96 13,19,18,12 32 34 34 -2 0

54 4003 GPS-4 13-Nov-91 8 12 12 -4 0

54 4003 GPS-4 04-Nov-93 8 12 12 -4 0

54 4003 GPS-4 26-Oct-95 8 12 12 -4 0

56 3027 GPS-3 06-Feb-92 14/16/13/12 36 32 32 4 0

56 3027 GPS-3 18-Aug-94 14/16/13/12 36 32 32 4 0

56 3027 GPS-3 25-Jul-97 14/16/13/12 36 32 31 5 1

72 4121 GPS-3 18-Jan-90 25 N/A 27 -2

72 4121 GPS-3 28-Feb-91 25 N/A 27 -2

72 4121 GPS-3 10-Feb-93 25 N/A 27 -2

72 4121 GPS-3 10-Mar-94 25 N/A 27 -2

83 3802 GPS-3 09-Jun-93 12-13-17-18 33 35 35 -2 0

83 3802 GPS-3 15-Feb-94 12-13-17-18 33 35 35 -2 0

83 3802 GPS-3 22-Aug-94 12-13-17-18 33 35 27 6 8

83 3802 GPS-3 29-Mar-95 12-13-17-18 33 35 27 6 8
Table 6. Mismatched joints in faulting surveys, inventory records, and distress maps (continued).

State

Code

SHRP

ID

Experiment

Type Survey Date Random JointSpacing

Number of

Joints From

Inventory

Number of

Joints

From

Distress

Map

Number of

Joints

From

Faulting

Survey

Difference

B/w Inventory

and Faulting

Number of

Joints

Difference

Between

Distress

Faulting and

No. of Joints

20

Difference = W heelpath Faulting - Edge Faulting

0

2000

4000

6000

8000

10000

12000

14000

16000

<11 -10 -6 -4 -3 -2 -1 0 1 2 3 4 6 10 11

Difference, mm

Frequency

0%

20%

40%

60%

80%

100%

120%

Cumulative percentage

83 3802 GPS-3 15-Oct-96 12-13-17-18 33 35 35 -2 0

83 3802 GPS-3 15-Sep-97 12-13-17-18 33 35 35 -2 0

84 3803 GPS-3 22-Aug-91 13,17,16,12 34 35 1 33 34

89 3015 GPS-3 16-Jul-91 31 26 26 5 0

89 3015 GPS-3 16-Jul-93 31 26 26 5 0

89 3015 GPS-3 19-May-94 31 26 26 5 0

89 3015 GPS-3 11-Aug-94 31 26 26 5 0

89 3015 GPS-3 13-Jun-95 31 26 26 5 0

89 3015 GPS-3 19-Nov-96 31 26 25 6 1

89 3015 GPS-3 20-May-97 31 26 24 7 2

89 3015 GPS-3 23-Sep-97 31 26 25 6 1

89 9018 GPS-9 05-Oct-94 31 31 29 2 2

Comparison of Wheelpath and Edge Faulting Data

One concern about the faulting data quality from previous analysis was the large differences

between wheelpath and edge faulting at each joint/crack location. To examine these two paired

measurements, frequency distributions of the differences of wheelpath faulting and edge faulting

values are provided in figure 7. As shown, for more than 90 percent of the cases, the difference

is between -1 mm and 1 mm. Since this is the same as the precision of the faultmeter, these

discrepancies are considered insignificant.

Figure 7. Frequency distribution of the difference between

wheelpath and edge faulting data.
21

Complementary Data Quality Evaluation

As part of this study, a complementary database with joint and pavement design features, traffic,

and environmental data that may be of use in analysis of joint/crack faulting was assembled and

examined. The results of the evaluation of this data are presented in this section. 

Selection of the related data was based on data used in existing faulting models and engineering

judgment. The faulting complementary data were extracted from the inventory, material testing,

environmental, and traffic modules of the April 1998 release of the IMS database. These data

were subsequently divided into critical information and other information deemed useful but not

critical. Assessments of missing and questionable data are based on the critical/noncritical

classification.

Inventory Data

Inventory data include general information about each section, including section identification,

pavement type, construction date, original design, shoulder type, drainage type, load transfer

information, and joint spacing. The variables considered are shown below:

• Construction Number

• Status

• Assign Date

• Deassign Date

• Construction Date

• Traffic Open Date

• Year Widened

• Original Number of Lanes

• Final Number of Lanes

• Lane Added Number

• Pavement Type

• Pavement Type (Other)

• Number of Lanes

• Lane Width

• Subdrainage Location

• Subdrainage Type

• Subdrainage Type (Other)

• Longitudinal Drain Diameter

• Outlet Lateral Spacing

• Depth to Rigid Foundation

• Construction Number

• Layer Number

• Average Contraction Joint Spacing

• Random Joint Spacing

• Mean Expansion Joint Spacing

• Joint Skewness

• Joint Load Transfer Type

• Joint Load Transfer Type (Other)
22

• Dowel Bar Spacing

• Dowel Diameter

• Distance Between Edge and Dowel

• Dowel Coating

• Dowel Coating (Other)

• Load Transfer Device Placement Method

• Load Transfer Device Placement Method (Other)

• Transverse Joint Cut Method

• Transverse Joint Cut Method (Other)

• Longitudinal Joint Cut Method

• Longitudinal Joint Cut Method (Other)

• Shoulder Traffic Lane Joint Method

• Shoulder Traffic Lane Joint Method (Other)

• Percent Longitudinal Steel (JRCP)

Missing information from the inventory was differentiated by GPS or SPS sections, critical or

other information, and the design parameters. A compilation of the sections with missing

information and the corresponding IMS table and file extension are shown in tables 7 through 10. 

The feedback reports for each of the tables with missing information were submitted to FHWA.

Material Characterization

To characterize material type for pavement layers, field core testing information was used from

the file TST_L05B.T32. For sections with missing testing information, the values from the

inventory table INV_LAYER.I03 were used. There is adequate information on base/subbase

type and thickness and on subgrade type. The list of material variables considered is shown

below:

• Subgrade Type

• Subgrade Material Type

• Subbase Type

• Subbase Material Type

• Subbase Representative Thickness

• Base Type

• Base Material Type

• Base Representative Thickness

• Binder Course Type

• Binder Material Type

• Binder Course Representative Thickness

• Original Surface Type

• Original Surface Material Type 

• Original Surface Representative Thickness

• Overlay Type

• Overlay Material Type

• Overlay Representative Thickness
23

Table 7. Missing GPS inventory information considered critical for faulting analysis.

Design Parameter

No. of

Sections

With Missing

Data

Sections With Missing Data Table File Extension

LANE_WIDTH 1 27-3009 INV_GENERAL I01TRANS_CONT_JLTS 14 08-3032, 13-3011, 16-3023, 20-3060, 32-3010, 32-3013, 37-3807,

72-4121, 89-3001, 17-9327, 29-4036, 08-9019, 08-9020, 18-9020

INV_PCC_JOINT I06

ROUND_DOWEL_DIAMETER

15 08-3032, 13-3011, 16-3023, 20-3013, 20-3060, 32-3010, 32-3013,

37-3807, 72-4121, 89-3001, 89-3002, 05-3074, 08-9019, 08-9020,18-9020

INV_PCC_JOINT I06

DOWEL_MLTD_SPACING 17 08-3032, 13-3011, 16-3023, 20-3013, 20-3060, 32-3010, 32-3013,

37-3807, 39-3801, 72-4121, 89-3001, 89-3002, 05-3074, 05-4021,08-9019, 08-9020, 18-9020

INV_PCC_JOINT I06
24

Table 8. Missing noncritical GPS inventory information useful for faulting analysis.

Design Parameter

No. of

Sections

With

Missing

Data

Sections With Missing Data Table File Extension

DOWEL_DISTANCE 33 08-3032, 13-3011, 16-3023, 18-3003, 20-3013, 20-3060, 21-3016,

26-3069, 32-3010, 32-3013, 37-3008, 37-3011, 37-3044, 37-3807,37-3816, 72-4121, 89-3001, 89-3002, 05-3074, 05-4021, 21-4025,26-4015, 27-4033, 27-4034, 27-4037, 27-4040, 27-4054, 27-4055,08-9019, 08-9020, 18-9020, 26-9029, 26-9030

INV_PCC_JOINT I06

DOWEL_COATING 26 08-3032, 13-3011, 16-3023, 18-3003, 20-3013, 20-3060, 21-3016,

26-3069, 32-3010, 32-3013, 39-3801, 45-3012, 72-4121, 89-3001,05-3074, 05-4021, 17-5217, 17-9327, 21-4025, 26-4015, 39-4018,08-9019, 08-9020, 18-9020, 26-9029, 26-9030

INV_PCC_JOINT I06

MLTD_METHOD 28 08-3032, 13-3011, 16-3023, 18-3003, 20-3013, 20-3060, 21-3016,

26-3069, 32-3010, 32-3013, 35-3010, 55-6352, 55-6355, 72-4121,89-3001, 89-3002, 05-3074, 05-4046, 17-5217, 21-4025, 26-4015,27-4040, 27-4055, 08-9019, 08-9020, 18-9020, 26-9029, 26-9030

INV_PCC_JOINT I06

TRANS_METHOD 13 12-4000, 12-4138, 32-3010, 40-4160, 40-4162, 72-3008, 89-3001,

89-3002, 17-9327, 22-4001, 18-9020, 27-9075, 89-9018

INV_PCC_JOINT I06

LONG_TYPE 20 01-3028, 32-3010, 40-4160, 40-4162, 46-3053, 55-6352, 55-6353,

55-6354, 55-6355, 72-3008, 89-3001, 89-3002, 89-3015, 89-3016,17-5217, 17-9327, 27-4054, 18-9020, 27-9075, 89-9018

INV_PCC_JOINT I06

SH_TRAFFIC_LANE_TYPE 44 01-3028, 12-4000, 12-4059, 12-4109, 12-4138, 13-3011, 13-3018,

13-3019, 19-3006, 19-3028, 27-3003, 27-3013, 32-3010, 37-3008,37-3011, 37-3044, 37-3816, 39-3013, 39-3801, 40-3018, 40-4162,48-3003, 55-3015, 55-3016, 72-3008, 89-3001, 89-3002, 89-3015,89-3016, 05-3074, 17-9327, 20-4016, 20-4052, 27-4034, 27-4037,27-4040, 27-4054, 27-4055, 54-4003, 54-4004, 18-9020, 20-9037,27-9075, 89-9018

INV_PCC_JOINT I06
25

Table 9. Missing SPS inventory information considered critical for faulting analysis.

Design Parameter

No. of

Sections

With

Missing

Data

Sections With Missing Data Table File Extension

DATE_COMPLETE 12 38-0213, 38-0214, 38-0215, 38-0216, 38-0217, 38-0218, 38-0219,

38-0220, 17-0601, 17-0602, 17-0605, 46-0601

SPS_ID X01

LANE_WIDTH 9 20-0201, 20-0202, 20-0203, 20-0204, 20-0205, 20-0206, 20-0207,

20-0208, 46-0601

SPS_GENERAL X02

DRAINAGE_LOCATION 9 20-0201, 20-0202, 20-0203, 20-0204, 20-0205, 20-0206, 20-0207,

20-0208, 46-0601

SPS_GENERAL X02

DRAINAGE_TYPE 9 20-0201, 20-0202, 20-0203, 20-0204, 20-0205, 20-0206, 20-0207,

20-0208, 46-0601

SPS_GENERAL X02

AVG_CONTRACTION_SPACING 6 04-0217, 08-0213, 08-0215, 08-0218, 08-0220, 08-0222 SPS2_PCC_JOINT_DATA 211AVG_CONTRACTION_SPACING 1 46-0601 INV_PCC_JOINT I06RANDOM_SPACING 6 04-0217, 08-0213, 08-0215, 08-0218, 08-0220, 08-0222 SPS2_PCC_JOINT_DATA 211RANDOM_SPACING 1 46-0601 INV_PCC_JOINT I06JOINT_SKEWNESS 6 04-0217, 08-0213, 08-0215, 08-0218, 08-0220, 08-0222 SPS2_PCC_JOINT_DATA 211JOINT_SKEWNESS 1 46-0601 INV_PCC_JOINT I06TRANS_CONT_JLTS 6 04-0217, 08-0213, 08-0215, 08-0218, 08-0220, 08-0222 SPS2_PCC_JOINT_DATA 211TRANS_CONT_JLTS 1 46-0601 INV_PCC_JOINT I06ROUND_DOWEL_DIAMETER 6 04-0217, 08-0213, 08-0215, 08-0218, 08-0220, 08-0222 SPS2_PCC_JOINT_DATA 211ROUND_DOWEL_DIAMETER 1 46-0601 INV_PCC_JOINT I06ROUND_DOWEL_DIAMETER 2 39-0809, 39-0810 SPS8_PCC_JOINT_DATA 811DOWEL_SPACING 6 04-0217, 08-0213, 08-0215, 08-0218, 08-0220, 08-0222 SPS2_PCC_JOINT_DATA 211DOWEL_SPACING 1 46-0601 INV_PCC_JOINT I06
Table 10. Missing noncritical SPS inventory information useful for faulting analysis.

Design Parameter

No. of

Sections With

Missing Data

Sections With Missing Data Table File Extension

DATE_OPEN_TRAFFIC 4 17-0601, 17-0602, 17-0605, 46-0601 SPS_ID X01DOWEL_DISTANCE 6 04-0217, 08-0213, 08-0215, 08-0218, 08-0220, 08-0222 SPS2_PCC_JOINT_DATA 211DOWEL_DISTANCE 1 46-0601 INV_PCC_JOINT I06DOWEL_COATING 6 04-0217, 08-0213, 08-0215, 08-0218, 08-0220, 08-0222 SPS2_PCC_JOINT_DATA 211DOWEL_COATING 1 46-0601 INV_PCC_JOINT I06MLTD_METHOD 6 04-0217, 08-0213, 08-0215, 08-0218, 08-0220, 08-0222 SPS2_PCC_JOINT_DATA 211MLTD_METHOD 1 46-0601 INV_PCC_JOINT I06TRANS_METHOD 6 04-0217, 08-0213, 08-0215, 08-0218, 08-0220, 08-0222 SPS2_PCC_JOINT_DATA 211TRANS_METHOD 4 17-0601, 17-0602, 17-0605, 46-0601 INV_PCC_JOINT I06LONG_TYPE 6 04-0217, 08-0213, 08-0215, 08-0218, 08-0220, 08-0222 SPS2_PCC_JOINT_DATA 211LONG_TYPE 4 17-0601, 17-0602, 17-0605, 46-0601 INV_PCC_JOINT I06SH_TRAFFIC_LANE_TYPE 13 04-0217, 08-0213, 08-0215, 08-0218, 08-0220, 08-0222, 38-

0213, 38-0215, 38-0216, 38-0217, 38-0218, 38-0219, 38-0220

SPS2_PCC_JOINT_DATA 211

SH_TRAFFIC_LANE_TYPE 1 46-0601 INV_PCC_JOINT I06SH_SURFACE_TYPE 9 20-0201, 20-0202, 20-0203, 20-0204, 20-0205, 20-0206, 20-

0207, 20-0208, 46-0601

SPS_GENERAL X02

26
27

Traffic

Traffic loading is an important factor affecting joint faulting [3, 4]. Quality traffic data over the

whole pavement life is very important for the study of the effect of cumulative traffic on faulting. 

To obtain traffic data, the following data tables from the traffic module of IMS database, April

1998 release, were used:

• TRF_MONITOR_BASIC_INFO.F00

• TRF_EST_ANL_TOT_LTPP_LN.F02

Analysis of monitoring traffic data (measured by automated equipment) from the table

TRF_MONITOR_BASIC_INFO revealed that data were missing for a number of sections and

that the available ESAL data were reported for very few recent years. Some of the sections with

missing monitoring information had historical ESAL data (estimated by the State highway

agencies [SHAs]) available in the table TRF_EST_ANL_TOT_LTPP_LN. A summary table of

traffic data availability (in terms of ESALs) for sections with available faulting data is presented

in table 11. A list of sites missing both historical and monitoring data is given in table 12.

Table 11. Traffic (80-kN ESALs) data availability summary for sections with

available faulting data. 

Description of Traffic Data Availability (80-kN ESALs) No. of Sections Percent of Sections

Both Historical and Monitoring Data Available 104 34

Only Monitoring Data Available 14 5

Only Historical Data Available 64 21

No Data Available 125 41

Total Number of Sections Considered 307 100
28

Table 12. Sections missing both historical and monitoring 80-kN ESAL traffic data.

State

Code

SHRP

ID

Experiment

Type

State

Code

SHRP

ID

Experiment

Type

State

Code

SHRP

ID

Experiment

Type

1 4007 GPS-4 19 0218 SPS-2 38 0218 SPS-2

4 0213 SPS-2 19 0219 SPS-2 38 0219 SPS-2

4 0214 SPS-2 19 0220 SPS-2 38 0220 SPS-2

4 0215 SPS-2 19 B410 SPS-4 39 0809 SPS-8

4 0216 SPS-2 20 0202 SPS-2 39 0810 SPS-8

4 0217 SPS-2 20 0203 SPS-2 39 B410 SPS-4

4 0218 SPS-2 20 0204 SPS-2 40 3018 GPS-3

4 0219 SPS-2 20 0205 SPS-2 40 4157 GPS-3

4 0220 SPS-2 20 0206 SPS-2 42 1623 GPS-3

4 0221 SPS-2 20 0207 SPS-2 42 1691 GPS-4

4 0222 SPS-2 20 0208 SPS-2 42 A410 SPS-4

4 0223 SPS-2 26 0214 SPS-2 42 A430 SPS-4

4 0224 SPS-2 26 0215 SPS-2 42 C410 SPS-4

4 A410 SPS-4 26 0217 SPS-2 42 C430 SPS-4

4 A430 SPS-4 26 0218 SPS-2 48 9355 GPS-9

5 B410 SPS-4 26 0219 SPS-2 48 B410 SPS-4

5 B430 SPS-4 26 0220 SPS-2 48 B420 SPS-4

5 C410 SPS-4 26 0221 SPS-2 48 B430 SPS-4

5 C430 SPS-4 26 0222 SPS-2 48 C410 SPS-4

6 A410 SPS-4 26 0223 SPS-2 48 C420 SPS-4

6 A420 SPS-4 26 0224 SPS-2 48 C430 SPS-4

6 A430 SPS-4 32 A410 SPS-4 48 D410 SPS-4

6 B410 SPS-4 32 A420 SPS-4 48 D420 SPS-4

6 B420 SPS-4 32 A430 SPS-4 48 D430 SPS-4

6 B430 SPS-4 37 0201 SPS-2 48 E410 SPS-4

8 0213 SPS-2 37 0202 SPS-2 48 E420 SPS-4

8 0214 SPS-2 37 0203 SPS-2 48 E430 SPS-4

8 0215 SPS-2 37 0204 SPS-2 49 7083 GPS-3

8 0218 SPS-2 37 0205 SPS-2 49 7085 GPS-3

8 0220 SPS-2 37 0206 SPS-2 49 7086 GPS-3

8 0222 SPS-2 37 0207 SPS-2 49 C410 SPS-4

9 4008 GPS-4 37 0208 SPS-2 49 C430 SPS-4

10 1201 GPS-4 37 0209 SPS-2 49 D410 SPS-4

17 0602 SPS-6 37 0210 SPS-2 49 D430 SPS-4

17 0605 SPS-6 37 0211 SPS-2 49 E410 SPS-4

18 0605 SPS-6 37 0212 SPS-2 49 E430 SPS-4

18 A410 SPS-4 37 3008 GPS-3 53 3014 GPS-3

19 0213 SPS-2 38 0213 SPS-2 72 3008 GPS-3

19 0214 SPS-2 38 0214 SPS-2 72 4121 GPS-3

19 0215 SPS-2 38 0215 SPS-2 89 3001 GPS-3

19 0216 SPS-2 38 0216 SPS-2 89 3002 GPS-3

19 0217 SPS-2 38 0217 SPS-2
29

0

200

400

600

800

1000

1200

1400

1600

197419751976197719781979198019811982198319841985198619871988198919901991199219931994199519961997

Average ESALs per Day

0

0.5

1

1.5

2

2.5

3

ESALs per Truck

Monitoring ESALs Historical ESALs

Monitoring ESALs per truck Historical ESALs per truck

Table 13. Sections with questionable traffic data (80-kN ESALs, truck factors).

No. of Observations Sections With Questionable and Missing Data

42

124000, 124138, 133007, 163017, 183002, 283018, 283019, 313018, 313028,

353010, 453012, 533812, 537409, 284024, 295503, 364018, 394018, 484146,

289030, 429027, 489167, 260213 

Figure 8. Example of questionable trend between historical and monitoring

ESALs for section 124000.
30

Validity of the available traffic data was assessed through comparison of historical and

monitoring ESAL data and by comparison of calculated truck factors for each section to an

acceptable range (0.5 to 2.5). Analysis of the ESAL values for the sections that have both

monitoring and historical ESALs showed that the quality and quantity of the available historical

and monitoring traffic data vary considerably. Sections with questionable data were defined as

those that showed unusually high or low values (ESALs or truck factors) or major discrepancies

between the surveys. A list of sites with questionable data is given in table 13, and an example

of a questionable trend between historical and monitoring ESAL data is presented in figure 8. 

The historical trend for this section indicates a substantial increase in truck loads, whereas the

trend for monitoring data is declining. Particularly disturbing is the decline in ESALs per truck

between 1992 and 1997. An opposite trend is expected, especially for recent years, because of

increased competitiveness in the trucking/shipping industry and advances in wireless

communications. Sites with missing and questionable traffic data were reported to FHWA in an

LTPP Feedback Report.

Since knowledge of cumulative traffic loads is crucial for the performance analysis process,

cumulative traffic loads for the entire pavement lifespan need to be estimated using the available

fragmented historical and monitoring traffic data. Closer examination of the available

monitoring data revealed that, in order to obtain cumulative traffic loads for the entire in-service

life of pavement sections, monitoring data have to be projected to cover the years with missing

monitoring information–starting from section opening to traffic date and up to the year of the last

available faulting survey. The method for traffic projection used in this study is discussed in

more detail in chapter 4.

Environmental Factors

Climate is another important factor that affects faulting measurement. Climatic parameters

considered for this study include annual precipitation, annual freeze index, maximum

temperature range for the day and for the year, and annual numbers of days above 32EC and

below 0EC. Some of these parameters are not directly available in the current version of the IMS

and had to be calculated based on the available monthly parameters. Variables used to calculate

these parameters with the source of information for each variable are given in table 14.

Annual parameters were calculated based on available monthly variables. Several GPS sections

and all SPS-6 and SPS-8 sections are missing links to weather stations. A list of these sections is

given in table 15. Environmental data from a nearby GPS section may be considered for SPS

sections missing links to weather stations. The possibility of these sections being linked to other

GPS experiments is currently being investigated.

It should be noted that after the analysis presented here was completed, the climatic data

available as part of the ENV module were removed from the LTPP IMS and replaced with a new

set of climatic data stored in the CLIMATE module in the IMS. As such, some of the

information presented in tables 14 and 15 may not be applicable.
31

Table 14. Data elements used for calculation of climatic parameters.

IMS File

Name

File

Ext. Table Name Experiment

Type List of Parameters

Q1_1998 Env E02 ENV_MONTHLY_

DERIVED All GPS

WEATHER STATION

ANNUAL FREEZE INDEX

ANNUAL FREEZE THAW CYCLES

AVG DAILY TEMP RANGE OVER YEAR

ANNUAL SNOWFALL

ANNUAL PRECIP

Q4_1997 Env E03 ENV_MONTHLY_

PARAMETER All GPS AVG MAX MONTHLY TEMP

AVG MIN MONTHLY TEMP

Q4_1997 Aws82 w06

AWS_

PRECIPITATION_

MONTH All SPS

AWS_ID

TOTAL_MON_PRECIP

WET DAYS

Q4_1997 Aws82 w08 AWS_TEMP_MONTH All SPS

MAX_MON_TEMP_AVG

MIN_MON_TEMP_AVG

MAX_MON_TEMP

MIN_MON_TEMP

DAYS ABOVE 32EC

DAYS BELOW 0EC

FREEZE-THAW CYCLES

FREEZE INDEX
32

Table 15. Sections missing links to weather stations.

Experiment Type Section ID

GPS-3 047614

GPS-3 273005

GPS-3 273007

GPS-3 273009

GPS-3 273010

GPS-3 273012

GPS-3 327084

GPS-3 466600

GPS-3 497085

GPS-3 497086

GPS-3 553008

GPS-4 264015

GPS-4 295503

GPS-9 395569

GPS-9 399022

GPS-6 170601

GPS-6 170602

GPS-6 170605

GPS-6 180602

GPS-6 180605

GPS-6 460601

GPS-8 390809

GPS-8 390810
33

Summary and Recommendations for Resolving Data Quality Issues

Data for a total of 422 jointed concrete pavement sections were available in the IMS database at

the time of the study. Out of 422 sections, only 307 sections had records in the faulting data

table MON_JPCC_FAULT, for a total of 24,108 records. This magnitude of missing data is

considered very serious because faulting is one of the key distress types associated with JCP. 

Future efforts should focus on ensuring that faulting data are collected as required. 

The available faulting data were evaluated in terms of missing and questionable data. The

records in the faulting table exhibit numerous missing observations. Missing faulting data were

difficult to quantify for many sections because of a lack of one-to-one mapping of the crack and

joint locations. Measurement errors in stationing are the most probable cause of this problem. 

However, crack development between surveys and full-depth repairs are other plausible reasons. 

A 0.5-m allowable deviation in the location of a crack or joint was used between surveys for

purposes of establishing missing data.

The assessment of questionable faulting data revealed a number of sections with negative

faulting values. The number and location of negative faulting values within each section were

determined. A comparison of faulting values at the same location (in the case of multiple

surveys) and between the wheelpath and edge was made to determine if the negative values were

a random occurrence (survey error) or were actually negative values.

The possible reasons for negative faulting were investigated using SPS-2 sites. It was found that

40 percent of SPS-2 sections had at least one joint that exhibited a negative faulting value on the

first faulting survey after construction. Most of the negative faulting values were equal to -1 mm. 

The -1 mm values could be attributed to random positive and negative measurement variation

taken on joints with zero faulting, as would be expected for new construction (built-in surface

texture and the precision of the Georgia Faultmeter being ±1 mm). 

The responses to the submitted LTPP Feedback Reports indicated that excessive negative

faulting values were sometimes attributable to having taken faulting measurements over the

patched or sealed joints. These measurements did not represent the “true” faulting at the joint

and, therefore, were not used in computation of faulting indices and summary statistics. In some

other cases, negative faulting values were recorded because the faultmeter was turned in the

wrong direction during data collection. The decision was made not to use excessive negative

faulting data (-2 mm or less) in the development of representative faulting indices and statistics

until the reasons for excessive negative faulting could be explained.

The companion data were evaluated in terms of critical and noncritical parameters. Critical

parameters were those previously used in the development of faulting models and other

potentially important factors identified by the project team. The noncritical factors have a lesser

effect on faulting. The missing critical companion data and noncritical data were reported to

FHWA in LTPP Feedback Reports. 
34

An assessment of availability and quality of the traffic data revealed that 41 percent of sections

with faulting data were missing traffic data. Sections with missing traffic data and sections with

questionable data quality were reported in an LTPP Feedback Report. Validity of the available

traffic data was assessed through comparison of historical and monitoring ESAL data and by

comparison of calculated truck factors for each section to an acceptable range (0.5 to 2.5). It is

recommended that, in order to improve traffic data quality, SHAs and regional offices need to

resolve data conflicts between historical and monitoring data and conflicts in traffic data trends

observed in time-series analysis. Because of the pressing need for cumulative traffic values, a

systematic procedure is needed for establishing traffic growth rates using available limited traffic

data.

The overall quality of the faulting data reported in the IMS database was found to be acceptable

for the development of faulting indices and summary statistics in terms of data availability. 

Assessment of the available data indicated that up to 95 percent of faulting surveys could be used

for the development of representative indices and summary statistics. Only 1 percent of surveys

were dismissed because of a large number of points with excessive negative faulting (more than

25 percent of measurements per survey with less than -2 mm), and 4 percent of surveys were

dismissed because of a large number of points with missing faulting observations (more than 25

percent of measurements per survey).

The faulting data quality issue addressed in this study was limited by the precision of the Georgia

Faultmeter that is standard equipment for LTPP program faulting measurements. A review of

numerous faulting records indicated that the equipment’s accuracy of ±1 mm is inadequate due to

the fact that representative maximum faulting values, obtained as an average of all maximum

faulting values for all sections and surveys, were about 5 mm for undoweled sections and 3 mm

for doweled sections.

It is recommended that the Georgia Faultmeter be modified to read to 0.1 mm. Use of a more

precise device would significantly improve the quality of future faulting data collection and

benefit future pavement performance analysis, especially for the SPS-2 and Seasonal Monitoring

Program (SMP) sections that are still in the early stages of pavement service life.

Available faulting data were also evaluated in terms of usefulness for faulting trend analysis. It

was found that less than 45 percent of sections had faulting data available from three or more

surveys. Therefore, the trend analysis reported in this report is to be viewed as “limited” or

“preliminary.” It is recommended that more extensive trend analysis be conducted as more data

become available. The lack of faulting measurements over time must be corrected in the future if

the LTPP program is to produce significant findings on ways to reduce faulting.

Questionable faulting and companion data, reported to FHWA, are summarized in table 16. This

table contains a summary of the data quality issues, actions recommended, and response status of

each Feedback Report. As shown in this table, a total of 20 Feedback Reports were submitted to

FHWA as part of this study. As of January 20, 2000, partial or complete responses were received

from LTPP regional offices for 11 of the 20 Feedback Reports. These responses were very

helpful for resolving faulting data quality issues.
WRCO = Western Region Coordination Office NARCO = North Atlantic Region Coordination Office

NCRCO = North Central Region Coordination Office SRCO = Southern Region Coordination OfficeRCOC = Regional Coordination Office Coordinator

35

Table 16. Summary of feedback reports.

Feedback No. Issues Recommended Action Date Responded Resolved

ERES_BW_31 Missing data from the SPS2_PCC_JOINT_DATA table, extension 211. WRCO to collect the design or as- built missing data. WRCO - 11/01/98 Y

ERES_BW_32 Missing data from the SPS8_PCC_JOINT_DATA table, extension 811. NCRCO to collect the design or as- built missing data.

N

ERES_BW_33 Missing data from the SPS_GENERAL table, extension X02. NCRCO to collect the design or as- built missing data.

N

ERES_BW_34 Missing data from the SPS_ID table, X01. NCRCO to collect the design or as- built missing data.

N

ERES_BW_35 Missing data from the INV_PCC_JOINT table, extension I06. RCOCs to collect the design or as- built missing data. SRCO - 1/28/00 Y for SRCO only

ERES_BW_36 Missing data from the INV_GENERAL table, extension I01. NCRCO to collect the design or as- built missing data.

N

ERES_BW_37 Missing data from the INV_PCC_STEEL table, extension I07. RCOCs to collect the design or as- built missing data. SRCO - 1/27/00 Y

ERES_BW_38

For SPS-6 experiment sections information is needed regarding thelocation of full depth patching or undersealing to account forrehabilitation effects on joint faulting values. Available IMS tablesSPS6_PCC_FULL_DEPTH and SPS6_UNDERSEALING do notcontain such information.

RCOCs to collect the design or asbuilt missing data. SRCO - 1/27/00 Y

ERES_BW_39

Section 55-3009 has surface layer milled (0.38 inch) in 1995. Dueto the change in surface condition, joint faulting data collected forthis section after 1995 will not be useful for joint faulting study ofthe original surface. This section need no longer be monitored as itwill become a case study.

For information only. N/A N/A

ERES_BW_40

137 sections have mismatched joint locations (mismatched greaterthan 0.5 m)from one survey to another in faulting tableMON_DIS_JPCC_FAULT with file extension M09.

RCOCs to use a template for eachsection to record the joint faultingdata. 

N/A N
Table 16. Summary of feedback reports (continued).

Feedback No. Issues Recommended Action Date Responded Resolved

WRCO = Western Region Coordination Office NARCO = North Atlantic Region Coordination OfficeNCRCO = North Central Region Coordination Office SRCO = Southern Region Coordination OfficeRCOC = Regional Coordination Office Coordinator

ERES_BW_41

Sections 05-3074, 12-4138, 27-4040, 32-3010, 34-4042, 40-4160,46-0601, 46-6600, 48-4143, 49-3011, 53-3813, and 83-3802 haveaverage faulting values decreasing or fluctuating with time. 

RCOCs to review the data.

WRCO - 11/30/98

NCRCO - 12/03/98

NARCO - 04/06/99

SRCO - 01/27/00

Y

ERES_BW_42

Negative faulting values were recorded for 160 of the 264 sectionsevaluated. Several special cases involving high values of negativefaulting are discussed in the Feedback Reports. 

RCOCs to review the data.

NCRCO - 12/08/98

SRCO - 12/11/98

WRCO - 11/02/98

NARCO - 10/06/99

Y

ERES_BW_43

On some survey dates, sections 06-7456, 34-4042, 36-4018, 49-7085, 53-3019, 53-3813, and 83-3802 exhibited negative faultingprofiles that are mirror images of the positive faulting profilemeasured on a different date. 

RCOCs to review the data.

WRCO - 11/30/98,

NARCO - 11/17/98,

NCRCO - 12/03/98

Y 

ERES_BW_44

Sections 06-3010, 18-0602, 18-0606, 55-3010, and 89-3016consistently show high negative faulting values from visit to visit atthe same locations. Also, section 18-0602 exhibits faulting of almost20 mm at one edge joint location. This appears to be too high avalue.

RCOCs to review the data.

WRCO - 11/30/98,

NCRCO - 12/03/98

NARCO - 11/03/99

Y

ERES_BW_45

Seven survey dates were excluded from the faulting analysis tabledue to large amount of high value negative faulting data, and 52survey dates were excluded due to large amount of missing data.

RCOCs to perform faulting surveysat all available joint (crack)locations and to verify negativefaulting values of -2 mm or larger.Provide a comment identifying apossible reason for negative faulting(e.g., spalling at the joint/crack,patching at joint/crack, etc.).

N

ERES_BW_46

Total of 124 faulting survey dates had number of joint mismatchedwith inventory-based number and 35 faulting surveys had number ofjoints mismatched with the number of joints from PASCO distressmaps.

RCOCs to review the data and make corrections, as necessary.

WRCO - 10/21/98,

NARCO - 11/05/98,

NCRCO - 12/03/98,

SRCO - 12/02/98

Y 

36
Table 16. Summary of feedback reports (continued).

Feedback No. Issues Recommended Action Date Responded Resolved

WRCO = Western Region Coordination Office NARCO = North Atlantic Region Coordination OfficeNCRCO = North Central Region Coordination Office SRCO = Southern Region Coordination OfficeRCOC = Regional Coordination Office Coordinator

ERES_BW_47

For several JRCP sections from GPS-4 experiment, both faultingsurvey data and PASCO distress maps gave an unusually largenumber of joints (resulting average joint spacing was found around4.5-6 m [15-20 ft]) . All of these sections are located in twoSouthern Region states: Arkansas and Texas.

SRCO to review the data andprovide appropriate feedback to theData Analysis Technical Support(DATS) team. Also, makecorrections, as necessary

SRCO - 11/19/98 Y

ERES_BW_48

Sections 84-3803, 21-3016, and 40-4160 had faulting measured atfewer locations than the number of joints indicated as available ininventory data and on PASCO distress maps.

RCOCs to review and update thedata. If no update is available, werecommend the removal of theabove records or to maintain them atnon-level E QC.

SRCO - 11/30/98

Y for

SRCO

data only

ERES_BW_58

Outlier faulting data for each section survey have been identifiedusing the following criteria: any point from a faulting survey wasconsidered as an outlier if its value was outside the region boundedby the values of the section average faulting for the survey date +/-two standard deviations.

RCOCs to review faulting data atthe locations provided in theattached tables to make sure that thedata are valid.

N

ERES_BW_61

Sections used in faulting data analysis that miss historical andmonitoring ESAL data and sections with questionable trends inESAL values and truck factor values outside the acceptable range(0.5 to 2.5).

RCOCs to review the availabletraffic data and provide reasons (orresolve situation) for missing orquestionable ESAL data.

N

37
38
39

3. REPRESENTATIVE FAULTING INDICES AND STATISTICS

Representative faulting indices and statistics for transverse joint and crack faulting will serve the

needs of pavement engineers interested in evaluating time-series trends of the faulting data and in

developing prediction models for joint faulting. A new database table, entitled

MON_DIS_JPCC_FAULT_SECT, has been developed to make this information available in the

LTPP database.

Development of a New IMS Table, MON_DIS_JPCC_FAULT_SECT

MON_DIS_JPCC_FAULT includes representative faulting indices and statistics that summarize

faulting data for each survey at each monitored section. Faulting statistics that may be useful for

future analysis of faulting data are also included in this table. Following is a list of the faulting

computed parameters for each test section for each survey date (site visit):

• Location type (joint or crack).

• Total number of points available for wheelpath or edge faulting measurements.

• Average edge faulting in mm.

• Minimum edge faulting in mm.

• Maximum edge faulting in mm.

• Standard deviation for edge faulting in mm.

• Number of edge faulting observations per survey with values greater than -2 mm.

• Number of missing edge faulting observations per survey.

• Number of negative edge faulting observations per survey with values less than -1 mm.

• A code describing reasons for absence of computed edge faulting indices.

• Average wheelpath faulting in mm.

• Minimum wheelpath faulting in mm.

• Maximum wheelpath faulting in mm.

• Standard deviation for wheelpath faulting in mm.

• Number of wheelpath faulting observations per survey with values greater than -2 mm.

• Number of missing wheelpath faulting observations per survey.

• Number of negative wheelpath faulting observations per survey with values less than -1

mm.

• A code describing reasons for absence of computed wheelpath faulting indices.

The schema for the new table, as well as quality control (QC) and filter specifications, are

presented in appendix A.

Criteria for Valid Faulting Observations

To develop meaningful faulting statistics, raw faulting data obtained from IMS table

MON_DIS_JPCC_FAULT were first examined and filtered using the criteria discussed below. 

MON_DIS_JPCC_FAULT contains point-by-point joint and crack faulting data collected along

the outer pavement edge and wheelpath. The number of crack and joint locations within each
40

section typically exceeds 30 observations for JPCP sections and 15 for JRCP sections. Ideally,

the number of joints and joint locations surveyed for faulting should remain identical between

surveys. This is not the case in reality, since some surveys contain either missing faulting

measurements or invalid values at certain joint locations. A threshold of 25 percent missing

observations within a section was deemed acceptable. The following additional criteria were

used to identify sections with valid faulting observations: a section was considered to be

acceptable for the faulting statistics calculation if the faulting data contained no more than 25

percent of missing data, negative data with values -2 mm or less, or a combination of missing

and negative data with values -2 mm or less. Based on these criteria, the four possible faulting

data statuses, presented in table 17, were identified and used as a guideline for the faulting

statistics calculation.

Table 17. Faulting data status.

Faulting

Status Description

1 Faulting statistics are acceptable since more than 75 percent of points have

reasonable faulting values.

2 Faulting statistics were not calculated because of a large number of points

with missing faulting observations (25 percent or more).

3 Faulting statistics were not calculated because of a large number of points

with negative faulting values in excess of 1 mm (25 percent or more).

4 Faulting statistics were not calculated because of a large number of points

with either missing or negative faulting values in excess of 1 mm (25 percent

or more combined).

Using the above criteria, 66 edge and 171 wheelpath survey dates were excluded from faulting

statistics calculation because of missing data, and 9 edge and 10 wheelpath survey dates were

excluded from the study because of negative data. Edge faulting records for sections 174074,

174082, and 294069 do not have enough valid information for any of the available survey dates. 

Wheelpath faulting records for section 040602 do not have enough valid information for any of

the available survey dates. No surveys were excluded because of a combination of missing and

negative data. Joint and crack faulting statistics were evaluated for 1427 edge and 1322

wheelpath survey dates. For the total of 1,503 survey dates, 95 percent of edge faulting surveys

and 88 percent of wheelpath surveys contained faulting records valid for faulting statistics

calculation based on the established 25 percent data availability threshold. A summary of the

status of faulting data is given in table 18.
41

Table 18. Summary of the status of faulting data.

STATUS

No. of Surveys With

EDGE_STATUS

No. of Surveys With

WHEELPATH_STATUS Total Edge

Surveys, %

Total

Wheelpath

Crack Joint Total Crack Joint Total Surveys, %

1 297 1130 1427 276 1046 1322 95 88

2 19 47 66 39 132 171 4 11

3 3 6 9 4 6 10 1 1

4 0110 00 0 0

TOTAL 319 1184 1503 319 1184 1503 100 100

Computation Algorithms

To compute representative faulting indices and statistics for the proposed new IMS table

MON_DIS_JPCC_FAULT_SECT, a computational algorithm (presented as a flowchart in figure

9) was developed. Step-by-step procedures for the routine calculation of faulting statistics are

given below.

Step 1. Obtain Faulting Data From IMS Database

Raw faulting data should be obtained from table MON_DIS_JPCC_FAULT in the IMS database. 

This table needs to be imported into Access® or another database management package for

further processing.

Step 2. Pre-Process Faulting Data

Step 2.1 – Create a template for the new table MON_DIS_JPCC_FAULT_SECT according to

the schema provided in appendix A.

Step 2.2 – Use table MON_DIS_JPCC_FAULT to obtain counts of faulting records with values

above -2 mm, NULL faulting records, and records with negative faulting values less than

or equal to -2 mm. To accomplish these activities, use SQL statements to group the data

in the table MON_DIS_JPCC_FAULT by STATE_CODE, SHRP_ID, SURVEY_DATE,

and CRACK_OR_JOINT and obtain the following counts for the grouped data:

2.2.a Number of POINT_LOC to get total number of points for the column 

NO_TOTAL_POINT_LOC of the new table MON_DIS_JPCC_FAULT_SECT.

2.2.b Number of empty fields per column for columns EDGE_AVG_MM and

WHEELPATH_AVG_MM to get number of NULL faulting observations for the 

fields NO_NULL_EDGE_FAULT and NO_NULL_WHEELPATH_FAULT of

the new table MON_DIS_JPCC_FAULT_SECT.
42

Raw data from

MON_DIS_JPCC_FAULT

Group data by STATE_CODE, SHRP_ID, SURVEY_DATE, and

CRACK_OR_JOINT to obtain counts and populate columns:

NO_TOTAL_POINT_LOC NO_NULL_WHEELPATH_FAULT

NO_NULL_EDGE_FAULT NO_NEG2_WHEELPATH

NO_NEG2_EDGE_FAULT NO_PASSED_WHEELPATH_FAUL

T

NO_PASSED_EDGE_FAULT

Assign values to

EDGE_FAULT_STATUS

and WHEELPATH_FAULT_STATUS

Evaluate

WHEELPATH_FAULT_STATUS

Group data by

STATE_CODE, SHRP_ID,

SURVEY_DATE, and

CRACK_OR_JOINT and

calculate wheelpath faulting

statistics

QC the results

= 1

Replace old

MON_DIS_JPCC_FAULT_SECT

table with a newly computed one

Other

(2, 3, 4)

Evaluate

EDGE_FAULT_STATUS

Group data by

STATE_CODE, SHRP_ID,

SURVEY_DATE, and

CRACK_OR_JOINT and

calculate edge faulting

statistics

= 1

Create a template for a new table

MON_DIS_JPCC_FAULT_SECT

Other

(2, 3, 4)

Populate columns of the new table MON_DIS_JPCC_FAULT_SECT:

AVG_EDGE_FAULT AVG_WHEELPATH_FAULT

MIN_EDGE_FAULT MIN_WHEELPATH

MAX_EDGE_FAULT MAX_WHEELPATH_FAULT

STD_EDGE_FAULT STD_WHEELPATH_FAULT

Figure 9. Flowchart for computation of faulting statistics.
43

2.2.c. Number of fields with negative values equal to or less than -2 mm per column for

columns EDGE_AVG_MM and WHEELPATH_AVG_MM to get number of

observations with negative faulting values for the fields

NO_NEG2_EDGE_FAULT and NO_NEG2_WHEELPATH_FAULT of the new

table MON_DIS_JPCC_FAULT_SECT.

2.2.d. Number of non-empty fields with values above -2 mm per column for columns

EDGE_AVG_MM and WHEELPATH_AVG_MM to get number of valid

faulting observations for the fields NO_PASSED_EDGE_FAULT and

NO_PASSED_WHEELPATH_FAULT of the new table

MON_DIS_JPCC_FAULT_SECT.

Step 2.3 – In the new table MON_DIS_JPCC_FAULT_SECT, populate columns containing key

fields and columns of the following parameters calculated in step 2.2:

NO_TOTAL_POINT_LOC, NO_PASSED_EDGE_FAULT,

NO_PASSED_WHEELPATH_FAULT, NO_NULL_EDGE_FAULT,

NO_NULL_WHEELPATH_FAULT, NO_NEG2_EDGE_FAULT, and

NO_NEG2_WHEELPATH_FAULT. Sort records in the new table by STATE_CODE,

SHRP_ID, SURVEY_DATE, and CRACK_OR_JOINT designation.

Step 2.4 – To populate columns EDGE_FAULT_STATUS and

WHEELPATH_FAULT_STATUS of the new table MON_DIS_JPCC_FAULT_SECT,

run a search routine through the columns NO_TOTAL_POINT_LOC, 

NO_NULL_EDGE_FAULT, NO_NULL_WHEELPATH_FAULT,

NO_NEG2_EDGE_FAULT, and NO_NEG2_WHEELPATH_FAULT and assign the

values in the EDGE_FAULT_STATUS and WHEELPATH_FAULT_STATUS columns

according to the following logic:

If 100*( NO_PASSED_EDGE_FAULT over NO_TOTAL_POINT_LOC )> 75 percent

then EDGE_FAULT_STATUS = 1

If 100*( NO_NULL_EDGE_FAULT over NO_TOTAL_POINT_LOC ) > = 25 percent

then EDGE_FAULT_STATUS = 2

If 100*( NO_NEG2_EDGE_FAULT over NO_TOTAL_POINT_LOC ) > = 25 percent

then EDGE_FAULT_STATUS = 3

If 100*( (NO_NULL_EDGE_FAULT + NO_NEG2_EDGE_FAULT) over

NO_TOTAL_POINT_LOC ) > = 25 percent then EDGE_FAULT_STATUS = 4

If 100*( NO_PASSED_WHEELPATH_FAULT over NO_TOTAL_POINT_LOC ) > 75

percent then WHEELPATH_FAULT_STATUS = 1

If 100*( NO_NULL_WHEELPATH_FAULT over NO_TOTAL_POINT_LOC ) > = 25

percent then WHEELPATH_FAULT_STATUS = 2
44

If 100*( NO_NEG2_WHEELPATH_FAULT over NO_TOTAL_POINT_LOC ) > = 25

percent then WHEELPATH_FAULT_STATUS = 3

If 100*(( NO_NULL_WHEELPATH_FAULT + NO_NEG2_EDGE_FAULT) over

NO_TOTAL_POINT_LOC ) > = 25 percent then WHEELPATH_FAULT_STATUS = 4

Step 3. Conduct Faulting Statistics Calculation

Step 3.1 – Use tables MON_DIS_JPCC_FAULT and MON_DIS_JPCC_FAULT_SECT to query

the records from the table MON_DIS_JPCC_FAULT that have corresponding records

with EDGE_FAULT_STATUS = 1, WHEELPATH_FAULT_STATUS = 1, or both in

the table MON_DIS_JPCC_FAULT_SECT. Save query results for edge and wheelpath

faulting in two separate intermediate tables EDGE_STATISTICS and

WHEELPATH_STATISTICS.

Step 3.2 – In the intermediate tables EDGE_STATISTICS and WHEELPATH_STATISTICS,

group records by STATE_CODE, SHRP_ID, SURVEY_DATE, and

CRACK_OR_JOINT designation to calculate joint or crack faulting statistics for each

group following the steps below: 

Use column WHEELPATH_AVG_MM of the table WHEELPATH_STATISTICS to

evaluate:

3.2.a. Average wheelpath faulting to populate a column AVG_WHEELPATH_FAULT.

3.2.b. Minimum wheelpath faulting to populate a column

MIN_WHEELPATH_FAULT.

3.2.c. Maximum wheelpath faulting to populate a column

MAX_WHEELPATH_FAULT.

3.2.d. Standard deviation of wheelpath faulting to populate a column

STD_WHEELPATH_FAULT.

Use column EDGE_AVG_MM of the table EDGE_STATISTICS to evaluate:

3.2.e. Average edge faulting to populate a column AVG_EDGE_FAULT.

3.2.f. Minimum edge faulting to populate a column MIN_EDGE_FAULT.

3.2.g. Maximum edge faulting to populate a column MAX_EDGE_FAULT.

3.2.h. Standard deviation of edge faulting to populate a column STD_EDGE_FAULT.
45

Average

x

N

N

=

∑

1

StDevP

x x

N = ∑ ( ) − 2

Mean values are calculated based on the entire population using the following formula:

Standard deviations are calculated based on the entire population using the following formula:

All the computed quantities should be rounded to a single decimal place.

Step 3.3 – Populate columns AVG_WHEELPATH_FAULT, MIN_WHEELPATH_FAULT,

MAX_WHEELPATH_FAULT, STD_WHEELPATH_FAULT, AVG_EDGE_FAULT,

MIN_EDGE_FAULT, MAX_EDGE_FAULT, STD_EDGE_FAULT of the table

MON_DIS_JPCC_FAULT_SECT.

Step 4. Upload Data Into IMS

Perform all QC checks (levels A to E) and upload the newly created table

MON_DIS_JPCC_FAULT_SECT into IMS.

Outlier Faulting Data

The computed faulting statistics were used to determine the outlier faulting observations. Outlier

data testing was performed based on ASTM E-178 guidelines [5]. Any point from a faulting

survey was considered an outlier if its value was outside the region bounded by the values of the

section average faulting for the survey date ± two standard deviations. Since the precision of the

Georgia Faultmeter equals ±1 mm, this should be considered the second source of error for the

faulting measurements. The two error sources should be pooled together to provide the overall

estimate of the error bounds. 

The distribution of the faulting measurements at different locations within a section can be

approximated as a normal distribution. The variance of this distribution is then:

(Computed Standard Deviation)2

The maximum rounding error caused by the ±1 mm precision of the Georgia Faultmeter is ±0.5

mm. The distribution of the faulting measurement error can be thought of as a completely

random occurrence. In other words, the error distribution is a uniform distribution from -0.5 mm

to +0.5 mm of the device reading. The variance of the measurement errors with a uniformly

distribution is:

1/12 * [+0.5-(-0.5)]2 = 1/12
46

Outlier_Limit ' Section_Average ± 2 ( Standard_Deviation 2 % 1/12

Therefore, the limits of the outliers should be computed as follows:

where: Outlier_Limit = Limit for evaluating outlier observations of each

faulting survey.

Section_Average = Section average faulting for the survey.

Standard_Deviation = Standard deviation of the faulting observations for a

specific survey.

Both crack and joint faulting data were investigated along the pavement outer edge and

wheelpath. Only section surveys with FAULTING_STATUS equal to 1 (indicates more than 75

percent of valid faulting observations) were used in this study. For these sections, only points

with faulting values -1 mm or larger were considered. The results of the outlier study are

summarized in table 19. As shown in this table, the average percentage of outlying observations

per survey with outliers is about 4 percent for edge and wheelpath observations. Frequency

distribution plots in figure 10 show that the sections with the most frequent outliers have about 4

percent of outlying observations. Representative examples of sections with outliers are given in

figure 11. Most of the cases with outlying observations can be attributed to random variability

inherent in the data.

Table 19. Outlier statistics summary.

Edge Observations Wheelpath Observations

Crack Joint Total Crack Joint Total

Number of Surveys With Outliers 38 182 220 36 132 168

Total Number of Valid Surveys 299 1137 1436 278 1052 1330

Percent of Surveys With Outliers 22 34 31 20 28 26

Number of Outlier Points in Surveys

With Outliers 45 194 239 49 145 194

Total Number of Points in Surveys

With Outliers 889 5339 6345 963 3858 4821

Percent of Outlier Points in Surveys

With Outliers 5 4 4 5 44
47

0

10

20

30

40

50

60

70

80

90

100

0 1 2 3 4 5 6 7 8 9 10 11 12 13

Percent of Edge Outliers at the Joints per Survey

Frequency

0

10

20

30

40

50

60

70

80

90

100

Cumulative Percentage

Frequency Cumulative %

0

10

20

30

40

50

60

70

0 1 2 3 4 5 6 7 8 9 10 11 12 13

Percent of W heelpath Outliers at the Joints per Survey

Frequency

0

10

20

30

40

50

60

70

80

90

100

Cumulative Percentage

Frequency Cumulative %

Figure 10. Outlier frequency distribution plots for sections with outliers.
48

Joint Faulting Along Edge for Section 55-3009, Survey Date 04-May-95

0

2

4

6

8

10

12

14

16

0 10 20 30 40 50 60 70 80 90 100 110 120 130 140 150

Point Location, m

Edge Joint Faulting, mm

Edge Faulting, mm Average Edge Faulting, mm

Upper Outlier Limit Lower Outlier Limit

Crack Faulting Along Edge for Section 26-0601, Survey Date 19-May-95

0

1

2

3

4

5

6

7

8

0 10 20 30 40 50 60 70 80 90 100 110 120 130 140 150

Point Location, m

Edge Crack Faulting, mm

Edge Faulting, mm Average Edge Faulting, mm

Upper Outlier Limit

Figure 11. Representative examples of sections with outliers.
49

Summary of Representative Faulting Indices and Statistics

In this chapter, the necessity of representative faulting indices for evaluation of faulting timeseries trends and for the development of joint faulting prediction models was discussed. To

satisfy this need, a set of representative faulting indices and statistics for transverse joint and

crack faulting, summarizing faulting data for each survey at each monitored section, was

developed for all LTPP sites with monitoring faulting survey data that satisfied the proposed

criteria for valid faulting observations. The computed faulting summary will be stored in the new

LTPP database table entitled MON_DIS_JPCC_FAULT_SECT. The algorithm for computation

of representative faulting indices and summary statistics for transverse joints and cracks was

developed as part of this study and presented in this chapter of the report. A description of the

new table MON_DIS_JPCC_FAULT_SECT is given in appendix A.

The computed faulting statistics were used to determine the outlier crack and joint faulting

observations for each faulting survey reported in the LTPP database. As a result of the outlier

study, it was determined that the average percentage of outlying observations per survey that had

outliers is about 4 percent for edge and wheelpath observations. These percentages seem

reasonable in light of the random variability inherent in the faulting measurement data and the

limitations in precision of measurement using the Georgia Faultmeter. Therefore, the developed

representative faulting indices could serve as faulting indicators for each LTPP section. The

computational procedure is set to account for possible improvements in future faulting data

resolution (through use of more precise equipment for faulting measurements).
50
51

4. FAULTING TREND ANALYSIS

To proof-test computed faulting indices and statistics, the following types of limited data analysis

were conducted:

• Initial faulting measurement.

• Time-series faulting trend analysis.

• Faulting versus IRI data trend.

• Effect of various design features and site conditions on faulting.

Initial Faulting Measurement

An assumption of zero average faulting values at the start of pavement in-service life was tested

using computed average faulting values for SPS-2 experiment sites. A frequency distribution

plot was also developed to show the average edge and wheelpath faulting values computed using

faulting data collected on the first faulting survey after construction, as shown in figure 12. The

mean faulting measurements for all SPS-2 sections was 0.2 mm for both wheelpath and edge on

the first survey since construction. Also, the distributions indicate that 97 percent of the

computed absolute average edge faulting values and 99 percent of the computed absolute average

wheelpath faulting values are less than 1 mm. These results indicate that the mean faulting of

newly constructed SPS-2 joints is very close to zero, as would be expected.

Time-Series Faulting Trend Analysis

The time histories of the computed average faulting of each section were generated and

examined. Several criteria characterizing faulting trends were developed, as discussed below. 

• If the difference between faulting values from different surveys is within 1 mm, this trend

is considered “stable.”

• If the difference between faulting values from different surveys is above 1 mm and the

values do not show a clear increase or decrease with time, this trend is called

“fluctuating.”

• If the difference between faulting values from different surveys is above 1 mm and the

values show a clear increase with time, this trend is called “increasing.”

• If the difference between faulting values from different surveys is above 1 mm and the

values show a clear decrease with time, this trend is called “decreasing.”

• If only one faulting survey was available for a section, then no faulting trend could be

determined, and such sections were not considered in the faulting trend analysis.

The threshold of 1 mm was established on the basis of the precision of the Georgia Faultmeter. 

A summary of faulting trend analysis is presented in table 20. Faulting data time 
52

0

10

20

30

40

50

60

70

-2 -1.7 -1.4 -1.1 -0.8 -0.5 -0.2 0.1 0.4 0.7 1 1.3 1.6 1.9

Average Edge Faulting on the 1st Survey After Construction, mm

Frequency

0

10

20

30

40

50

60

70

80

90

100

Cumulative %

Frequency Cumulative %

0

10

20

30

40

50

60

70

80

-2 -1.7 -1.4 -1.1 -0.8 -0.5 -0.2 0.1 0.4 0.7 1 1.3 1.6 1.9

Average W heelpath Faulting on the 1st Survey After Construction, mm

Frequency

0

10

20

30

40

50

60

70

80

90

100

Cumulative %

Frequency Cumulative %

Figure 12. Frequency distribution of the average faulting values from the first survey after

construction for SPS-2 sites.
53

Table 20. Summary of faulting time-series trend analysis.

No. of

Surveys

Time-series

Trend 

No. of Sections Along the

Edge

No. of Sections Along the

Wheelpath

1 Not Applicable 98 108

2 Stable 70 78

2 Increasing 14 20

2 Decreasing 2 1

3 or more Stable 80 66

3 or more Increasing 8 8

3 or more Decreasing 1 0

3 or more Fluctuating 31 24

histories of all the sections are presented in appendix B. Most of the sections show a reasonable

trend with time—average faulting values increasing or remaining stable with age, as shown in

figure 13. However, a few sections exhibited questionable time trends of average

faulting—either decreasing or fluctuating with time. Figure 14 provides an example graph of the

questionable faulting time trend. A list of questionable sections is given in table 21. Sections

with questionable trends were reported in LTPP Feedback Reports. Most of the questionable

trends resulted from zero faulting reported on one of the surveys when non-zero faulting was

reported on the previous or on the following surveys. As was found through the response from

the Regional Centers, zero values were entered by default when null values should have been

used instead.

Table 21. Sections with questionable faulting time trends.

No. Section

ID Description of Questionable Trends and Possible Causes

1 053074 Average faulting decreases to "0" at last survey date.

2 170605 Average faulting decreases to "0" at last survey date.

3 323010 Average faulting decreases to "0" at last survey date.

4 533813 Average faulting decreases to "0" at last survey date.

5 124138 Average faulting decreases to "0" at one survey date then goes up again.

6 466600 Average faulting decreases to "0" at one survey date then goes up again.

7 833802 Average faulting decreases to "0" at one survey date then goes up again.

8 493011 Average faulting decreases to "0" at one survey date then goes up again,

then decreases again for three consecutive survey dates.

9 404160 Decrease in average faulting can be explained by a small number of points

used in calculation of statistics for one of the surveys; this survey date was

included in a Feedback Report to be dropped from statistics calculation.

10 460601 Decrease is only in crack average faulting for one survey date. This can be

explained by a small number of points used in statistics calculation (three

and four points). Average faulting decrease was caused by inclusion of an

extra "0" faulting observation (new crack) in the calculation of the average.

11 284024 Wheelpath faulting is "0" at all points on all surveys. Average edge faulting

increases first, then goes down for three following survey dates.
54

0

0.5

1

1.5

2

2.5

3

3.5

5/7/90 9/19/91 1/31/93 6/15/94 10/28/95 3/11/97 7/24/98

Section 31-4019, GPS-4

Average Faulting, mm

Joint Edge Faulting Joint Wheelpath Faulting

Crack Edge Faulting Crack Wheelpath Faulting

0

1

2

3

4

5

6

1/1/1991 1/1/1992 1/1/1993 1/1/1994 1/2/1995 1/2/1996

Survey dates, Section 46-6600, GPS-3

Average Faulting, mm

Joint Edge Faulting Joint W heelpath Faulting

Figure 13. Example of a reasonable average faulting trend with time.

Figure 14. Example of a questionable average faulting trend with time.
55

Faulting Versus IRI Data Trends

Previous studies have indicated that, at some sections, there is a questionable relationship

between joint faulting and pavement roughness. This section presents the results of an analysis

performed to compare faulting time history data and IRI time history data.

General Trend Comparison

Plots of computed average faulting values and average IRI values for each section and each

survey date were developed, as presented in appendix B. Only sections with two or more valid

faulting and IRI surveys were considered in the trend analysis. Using this filter, a total of 162

sections were considered. For the sections under investigation, approximately 54 percent showed

good correlation, meaning that both faulting and IRI trends followed a similar pattern: either

increasing in value or staying stable with time. Examples of “good” and “bad” correlation

examples in faulting and IRI trends are given in figures 15 and 16, respectively.

Computed average faulting values were compared with average IRI data collected at the closest

dates to faulting surveys. Plots of average faulting values versus IRI are presented in figures 17

and 18 for JPCP and JRCP, respectively. To account for the sensitivity of IRI values to the

number of joints, cumulative joint faulting values were computed and used instead of average

values. With this approach, between two sections with the same average faulting values, the

section with the larger number of joints will have higher cumulative faulting values. Cumulative

faulting of each section was also compared with the average IRI data from the IMS database. 

Figures 19 and 20 provide cumulative faulting versus IRI graphs for JPCP and JRCP,

respectively. As shown, there is a generally positive trend line between cumulative faulting and

IRI; however, the correlation is not as high as expected. One probable reason is that the effect of

the built-in initial roughness in the IRI was not considered. It is well known that the future IRI of

a pavement is highly dependent upon its initial IRI. Another possible reason could be that IRI

values were calculated using filtered longitudinal profile.

Faulting Rate Versus IRI Rate for JPCP Sections

To eliminate the effect of the built-in initial roughness in the IRI versus faulting trends, rates of

change in IRI and faulting values with time were calculated and used in the regression analysis. 

Only JPCP sections with three or more faulting and IRI surveys were considered in this analysis. 

Rates of change (slopes) in IRI and faulting values were calculated for 63 JPCP sections. A

number of sections showed negative values in either IRI or faulting slopes. One section (GPS-3

section 893002) showed a very high rate of faulting (exceeding the value of average faulting rate

plus two standard deviations) compared with the rest of the sections. These sections were

excluded from further analysis so as to examine only the typical trends. As a result, the total

number of eligible sections was narrowed to 33.
56

0

1

2

3

4

5

6

7

8

9

10

6/6/89 6/5/92 6/5/95 6/4/98

Section 55-3010, GPS-3

Average Faulting, mm

0

1

2

3

4

5

Average IRI, m/km

Joint Edge Faulting Joint Wheelpath Faulting Average IRI

0

1

2

3

4

5

6

7

8

9

10

9/1/1989 8/22/1991 8/11/1993 8/1/1995 7/21/1997

Section 49-3011, GPS-3

Average Faulting, mm

0

1

2

3

4

5

Average IRI, m/km

Joint Edge Faulting Joint W heelpath Faulting Average IRI

Figure 15. Example of "good" correlation between faulting and IRI trends.

Figure 16. Example of "poor" correlation between faulting and IRI trends.
57

Regression line:

y = 0.2099x + 1.4678

R2

 = 0.2481

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

0 1 2 3 4 5 6 7 8 9 10

Average Joint Faulting, JPCP, mm

Average IRI, m/km

Regression line:

y = 0.0707x + 1.8232

R2

 = 0.0244

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5

Average Crack and Average Joint Faulting for JRCP Sections, mm

Average IRI, m/km

Figure 17. Average faulting versus average IRI for JPCP sections (initial IRI not considered).

Figure 18. Average faulting versus average IRI for JRCP sections (initial IRI not considered).
58

Regression line:

y = 0.0065x + 1.4814

R2

 = 0.2279

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

0 50 100 150 200 250 300 350

 Cumulative W heelpath Faulting for JPCP Sections, mm

Average IRI, m/km

Regression line:

y = 0.0065x + 1.7632

R2

 = 0.1399

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

0 20 40 60 80 100 120 140 160 180

Cumulative W heelpath Faulting for JRCP Sections, mm

Average IRI, m/km

Figure 19. Cumulative faulting versus average IRI for JPCP sections (initial IRI not considered).

Figure 20. Cumulative faulting versus average IRI for JRCP sections (initial IRI not considered).
59

Two other parameters affecting IRI values were considered in the analysis: joint spacing and age. 

Joint spacing was directly included in the multiple linear regression analysis as an independent

parameter together with the rate of faulting. The effect of age on IRI rate was considered

indirectly by dividing all the sections under study into two age subgroups: sections with average

age of all observations equal to or less than 10 years and sections with average age of all

observations exceeding 10 years. All faulting observations were divided into two age groups

based on the findings from earlier LTPP studies that showed a higher rate of faulting in earlier

years than in the later years of pavement life [6]. Plots of the rate of change of IRI versus rate of

change of faulting values are given in figure 21 for both age subgroups. Both subgroups showed

appreciable correlation between faulting rate and IRI rates. Calculated R-square coefficients

were equal to 0.81 for the young sections subgroup and 0.70 for the old sections subgroup. 

Linear multiple regression analysis resulted in the following relations for young and old section

subgroups:

Young_IRI_Rate = 0.044979 + 0.525313 * Faulting_Rate - 0.01089 * Joint_Spacing

Old_IRI_Rate = 0.025055 + 0.170382 * Faulting_Rate - 0.00289 * Joint_Spacing

where Young_IRI_Rate = IRI rate of change for sections with average age of all

observations equal to or less than 10 years, m/km-year.

Old_IRI_Rate = IRI of change rate for sections with average age of all

observations exceeding 10 years, m/km-year.

Faulting_Rate = Rate of change in average faulting, mm/year.

Joint_Spacing = Joint spacing, m.

Regression statistics are summarized in tables 22 and 23. The results of regression analysis for

the 33 JPCP sections considered in the analysis indicated high correlation between faulting rate

and IRI rates. The effect of joint spacing was not found to be significant, as indicated by the high

P-values in table 23. For these 33 JPCP sections, an increase in faulting explains about 70

percent of the increase in IRI. These results are bounded and limited to the above subgroups of

sections. As more faulting data become available, more generalized algorithms can be

established. The results of this limited study indicate that the effect of build-in roughness needs

to be considered in establishing relations between faulting and IRI trends.

Table 22. Regression statistics summary.

Age 0-10 Age 10+

Multiple R 0.903 0.847

R-Square 0.816 0.718

Adjusted R-Square 0.780 0.685

Standard Error, m/km - year 0.048 0.018

Observations 13 20
60

Age: 0-10 years

Regression line:

y = 0.5345x - 0.0112

R2

 = 0.811

0.00

0.05

0.10

0.15

0.20

0.25

0.30

0.35

0.40

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8

Rate of Faulting, mm/year

Rate of IRI, m/km-year

Age: 10+ years

Regression line:

y = 0.1754x + 0.0081

R2

 = 0.7033

0.00

0.05

0.10

0.15

0.20

0.25

0.30

0.35

0.40

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8

Rate of Faulting, mm/year

Rate of IRI, m/km-year

Figure 21. Rate of change of IRI versus rate of change of average faulting values.
61

Table 23. Regression statistics summary.

Age 0-10 Age 10+

Coefficients P-Value Coefficients P-Value

Intercept 0.044979 0.668815 0.025055 0.191485

Joint Spacing -0.01089 0.587329 -0.00289 0.348862

Wheelpath Slope 0.525313 7.78E-05 0.170382 9.34E-06

Effect of Various Design Features and Site Conditions on Faulting

Several studies have been performed to determine the design features, site conditions, and

construction practices that significantly influence JCP faulting [6, 7, 8, 9]. In this section newly

computed average faulting values were used to analyze the effect of key jointed concrete

pavement design features on faulting values. A faulting analysis was conducted using t-tests for

the following design features and site conditions:

• Use of load transfer devices (LTDs).

• Dowel bar diameter.

• Use of tied concrete shoulders.

• Use of widened lanes.

• Use of drainage features.

• Base/subbase type.

• Joint spacing for JPCP.

• Joint spacing for JRCP.

• Truck volume.

• Reinforcement amount (for JRCP).

• Climatic region.

• Joint orientation (skewed versus perpendicular).

Prior to statistical testing, all sections under investigation were divided into three separate groups

based on the pavement type: JPCP sections without dowel bars, JPCP sections with dowel bars,

and JRCP sections (all with dowel bars). Series of two-sample t-tests were carried out separately

for each group. The purpose of the t-tests was to determine whether the two sample means were

significantly different based on a 95 percent confidence interval. Discussion of the effects of

each design feature or site condition on faulting for each of three pavement types is presented

below.

Age of the pavement section is an important factor affecting faulting values. To account for this

effect, all pavement sections were divided into three age categories:

• Young (0 to10 years old at survey date). 

• Middle (10 to 20 years old).

• Old (more than 20 years old). 
62

0

5

10

15

20

25

30

35

40

45

50

0 3 6 9 13 16 19 22 25 28 31 34 More

Age, years

Number of Sections

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Cumulative Percentage

Number of Sections Cumulative %

Figure 22 shows the distribution of the age of the pavement sections at the time of the faulting

survey. Age category division was used in statistical tests when there were enough data for all

age categories. If one of the two samples of sections did not have enough data in one of the age

categories, the age categories were not considered. For these cases, only sections with

comparable ages were used in statistical analyses.

Results of t-tests for various design features and site conditions are summarized in tables 24 and

25 for JPCP sections without dowel bars and with dowel bars, respectively. Results of t-tests for

JRCP sections are summarized in table 26.

Figure 22. Frequency distribution of pavement section ages at the times of faulting surveys.

Table 24. t-test results for JPCP sections without dowels.

Comparison Pair

One-Tail

P-Value

Difference

of means

Significant

?

Feature 1 Feature 2

Feature 1 Statistics Feature 2 Statistics

Number

of

Sections 

Mean of

Avg. 

Fault

Standard

Deviation

Number

of

Sections 

Mean of

Avg. 

Fault

Standard

Deviation

Widened Lane Conventional Width 6 0.34 0.47 38 0.83 1.17 0.04420 0.49 Y

Drainage No Drainage 33 1.35 1.25 132 2.08 2.05 0.00514 0.74 Y

Tied PCC Shoulder No Tied PCC Shoulder 46 1.66 1.92 115 2.09 1.95 0.09863 0.44 N

Joint Spacing <= 4.6 m Joint Spacing > 4.6 m 70 1.57 1.70 86 2.25 2.13 0.01392 0.68 Y

Treated Base Granular Base 87 1.58 1.52 65 2.78 2.62 0.00065 1.21 Y

Dry-Freeze Zone Dry-No-Freeze Zone 35 1.56 1.70 24 1.10 0.94 0.09523 -0.46 N

Wet-Freeze Zone Wet-No-Freeze Zone 55 3.17 2.93 50 2.04 1.64 0.00801 -1.13 Y

Dry-Freeze Zone Wet-Freeze Zone 35 1.56 1.70 55 3.17 2.93 0.00072 1.61 Y

Dry-No-Freeze Zone Wet-No-Freeze Zone 24 1.10 0.94 50 2.04 1.64 0.00131 0.94 Y
63

Table 25. t-test results for doweled JPCP sections.

Comparison Pair

One-Tail

P-Value

Difference

of Means Significant?

Feature 1 Feature 2

Feature 1 Statistics Feature 2 Statistics

Number of

Sections Mean StandardDeviation

Number of

Sections Mean StandardDeviation

Widened Lane Conventional Width 59 0.17 0.24 132 0.28 0.38 0.01120 0.10 Y

Drainage No Drainage 88 0.56 0.82 166 0.36 0.55 0.02018 -0.20 Y

Tied PCC Shoulder No Tied PCC Shoulder 23 0.42 0.54 114 0.66 0.82 0.44609 0.24 N

Joint Skewed Straight Joints 41 0.51 0.75 140 0.50 1.28 0.48007 -0.01 N

Joint Spacing <= 4.6 m Joint Spacing > 4.6 m 154 0.28 0.52 94 0.68 0.79 0.00002 0.39 Y

Treated Base Granular Base 109 0.47 0.72 131 0.72 1.38 0.03561 0.25 Y

Wet-Freeze Zone Wet-No-Freeze Zone 52 0.60 0.80 68 0.64 0.73 0.40561 0.03 N

Table 26. t-test results for JRCP sections.

Comparison Pair

One-Tail

P-Value

Difference

of Means Significant?

Feature 1 Feature 2

Feature 1 Statistics Feature 2 Statistics

Number of

Sections Mean Standard

Deviation

Number of

Sections Mean StandardDeviation

Tied PCC Shoulder No Tied PCC Shoulder 8 0.48 0.26 24 0.38 0.39 0.23465 0.09 N

Joint Skewed Straight Joints 15 0.73 0.83 15 0.58 0.28 0.24867 0.16 N

Joint Spacing <15.25

m

Joint Spacing >=15.25

m 73 0.73 0.80 58 1.33 1.94 0.01500 -0.60 Y

Steel<0.14, % Steel>=0.14, % 20 0.27 0.38 19 1.31 1.02 0.00015 -1.04 Y

Cracks Joints 70 1.08 1.33 70 1.19 1.58 0.33407 -0.11 N

Treated Base Granular Base 64 0.85 1.18 68 1.43 1.78 0.01326 -0.58 Y

Wet-Freeze Zone Wet-No-Freeze Zone 86 1.15 1.42 49 1.09 1.73 0.41925 0.06 N

The purpose of these t-tests was to examine whether the design features and site conditions lead

to significantly decreased or increased mean joint faulting values for the given sample of

sections. From a statistical point of view, the results indicate a significant difference at a 95

percent level of significance if calculated P-values are less than 0.05. These results are discussed

in the next sections.

Use of Load Transfer Devices

The effect of dowel bar use on the computed average joint faulting values was investigated for

JPCP sections. Previous research findings have indicated a major influence of dowel bar use on

the reduction of joint faulting [3, 4, 9]. T-tests conducted for three different age groups of JPCP

sections indicated that dowel bar use significantly reduced joint faulting for all pavement age

categories. As shown in table 27, faulting of JPCP sections without dowels is twice as high as

for the JPCP sections with dowel bars. To investigate a general trend of faulting development

with time for doweled and non-doweled JPCP sections, a line passing through each of three mean

faulting values calculated for each age category was plotted against age, in years, for doweled

and non-doweled sections, as shown in figure 23. Zero average faulting values were assumed at

the traffic opening date based on the results obtained from the SPS-2 experiment sites.

For both JPCP and JRCP, as indicated in figure 23, faulting development follows a similar trend

with time: average faulting values are low for the first 10 years of service
64

0

0.5

1

1.5

2

2.5

3

0 2.5 5 7.5 10 12.5 15 17.5 20 22.5 25 27.5 30

Age, years

Average Joint Faulting, mm

JPCP, No Dowels JPCP, Doweled

Table 27. Effect of dowels on JPCP faulting.

Age,

Years

No Dowels Doweled

P(T<=t)

One-tail

P(T<=t)

Two-tail

Number

of

Sections

Mean

(mm)

Standard

Deviation

(mm)

Number

of

Sections

Mean

(mm)

Standard

Deviation

(mm)

0 - 10 44 0.69 1.03 170 0.16 0.23 0.00067 0.00134

10 - 20 84 2.10 2.10 63 0.83 0.96 1.4E-06 2.7E-06

over 20 28 2.53 1.87 16 1.02 1.07 0.00073 0.00146

Figure 23. General trend of faulting development with time for

doweled and non-doweled JPCP sections.
65

(although non-doweled sections indicate a high rate of faulting development), then faulting

values increase rapidly for the next 10 years. After 20 years of service, faulting continues to

progress, but at much lower rate, especially for doweled JPCP sections. These conclusions are

based on, and limited to, the results of the very broad age group division discussed previously.

Effect of Dowel Bar Diameter

Previous studies indicated that the larger diameter dowel bars reduce faulting because of less

steel/concrete bearing stress [6]. This hypothesis was tested using computed average joint

faulting values for three different age groups of JPCP sections. The general pattern showing

reduction in average faulting values with increase in dowel bar diameter was observed for all age

categories. The results of comparisons for different dowel bar diameters are presented in table 28

and figure 24.

Table 28. Effect of dowel bar diameter on faulting of JPC pavements over time.

Age,

Years

Dowel Diameter, mm

0 19 25 28 29 31 32 35 38

0-10 0.7* 0.6 0.3 0.1 0.3 0.3 0.15

10-20 2.2 0.6 1.4 0.6 1.0 0.6 0.6 0.2

Over 20 2.6 2.1 1.7 1.1

* mean faulting in mm

Use of Tied Concrete Shoulders

The effect of tied concrete shoulders on the computed average edge faulting was investigated for

JRCP sections and doweled and non-doweled JPCP sections. Information about shoulder type

was available only for a limited number of sections. No sections beyond 20 years of age had

records indicating tied concrete shoulder use; therefore, all sections were divided into two age

categories: less than 10 years old and 10 to 20 years old. The results showed that tied concrete

shoulder use did not significantly affect the average edge faulting values for JRCP sections. For

JPCP sections, t-test results were inconsistent. The results showed that average edge faulting

values for non-doweled JPCP sections less than 10 years old were significantly reduced when

tied concrete shoulder was used, but for non-doweled JPCP sections more than 10 years old, the

use of tied concrete shoulders did not significantly affect average edge faulting values. For

doweled JPCP sections, the use of tied concrete shoulders did not significantly affect average

edge faulting values for the group of sections less than 10 years of age, but showed significant

reduction in average edge faulting values for sections greater than 10 years old.
66

0.0

0.5

1.0

1.5

2.0

2.5

3.0

0 19 25 28 29 31 32 35 38

Dowel Diameter, mm

Average Faulting, mm

Age 0-10 years Age 10-20 years Age more than 20 years

Figure 24. Effect of dowel bar diameter on faulting of JPCP.

Use of Widened Lanes

Study of the effect of widened lane on average faulting values was conducted for JPCP sections

only, because none of the JRCP sections under investigation had a widened lane. JPCP sections

were presented by three different lane widths: conventional widths of 3.66 m (12 ft) and 3.36 m

(11 ft) (only two sections) and a widened lane width of 4.27 m (14 ft). Depending on the lane

width, all sections were divided into two groups: sections with lane width less than or equal to

3.66 m and sections with lane width equal to 4.27 m. The majority of sections with widened lane

were less than 10 years old. Only two sections were 10 to 20 years old (10.7 and 11.7 years), and

none were over 20 years old. Therefore, no age effect was considered in the t-tests. Separate ttests were performed for doweled and non-doweled JPCP sections.

The results of the t-tests showed a significant decrease in faulting values along the edge for the

non-doweled JPCP sections with widened lane. For doweled sections, results of the t-tests (Pvalues) also indicated statistically significant reduction in faulting values for widened lane

sections.
67

Use of Drainage Features

The effect of drainage use on faulting values was studied for JPCP and JRCP sections. All

sections within each pavement type were divided into two groups: those sections with some sort

of positive drainage system and those without. Because of unequal sample sizes in age groups,

no age category division was implemented in this analysis. Separate t-tests were performed for

doweled and non-doweled JPCP sections. The results of the t-tests did not show a significant

effect of drainage use on faulting values for JRCP sections. For JPCP non-doweled sections, use

of drainage significantly reduced faulting for both wheelpath and edge observations. This

supports the findings of the NCHRP 1-34 study on the effect of subsurface drainage on the

reduction of faulting in non-doweled JPC pavements [9]. Results for doweled JPCP sections

were inconclusive, mainly because of the very low level of faulting.

Base/Subbase Type

The effect of stabilized base/subbase type versus untreated aggregate base on faulting values was

tested for JPCP and JRCP sections. The results of t-tests showed a significant reduction in

faulting values for non-doweled JPCP sections with stabilized bases/subbases compared with the

values of non-doweled JPCP sections with granular bases/subbases. For doweled JPCP and

JRCP sections, the absolute difference between average faulting values was too small for

practical consideration.

Joint Spacing

Before any analysis of the effects of joint spacing on faulting values was performed, plots of joint

spacing frequency distribution were developed for the following groups of pavements: JPCP/no

dowels, JPCP/doweled, and JRCP, as shown in figure 25. Based on the distribution plots, JPCP

doweled and non-doweled sections were divided into two groups each: sections with joint

spacing 4.6 m or less and sections with joint spacing more than 4.6 m. This division permits

testing of groups of similar sizes. Similarly, JRCP sections were divided into two groups:

sections with joint spacing less than 15.25 m and sections with joint spacing greater than or equal

to 15.25 m. Separate t-tests were carried out for each pavement group pair. The results of the ttests showed that joint spacing significantly affects faulting for all pavement categories under

study. Shorter joint spacings show smaller faulting values.

Joint Orientation (Skewed Versus Perpendicular)

The effect of joint skewness on faulting values was studied for doweled JPCP and JRCP

sections. There was not enough information on joint orientation for non-doweled sections in the

IMS database. The results of the tests did not show a significant effect of joint skewness for

JRCP sections. For JPCP doweled sections, the results were not consistent. There is some

evidence that doweled JPCP sections with skewed joints more than 10 years old show an

increase in faulting; however, sample sizes were small, and other design features may have

affected the faulting values. Thus, for doweled joints, skewed joints did not show significant

faulting differences.
68

JPCP, No Dowels

0

10

20

30

40

50

60

70

3.43.53.73.84.04.14.34.44.64.74.95.05.25.35.55.65.85.96.1

More

Average Contraction Joint Spacing, m

Frequency

0%

20%

40%

60%

80%

100%

120%

Cumulative

JRCP

0

5

10

15

20

25

30

4.66.17.69.2

10.712.213.715.316.818.319.821.422.924.425.927.529.030.5

Average Contraction Joint Spacing, m

Frequency

0%

20%

40%

60%

80%

100%

120%

Cumulative

Frequency Cumulative %

JPCP, Doweled

0

50

100

150

4.04.34.64.95.25.55.86.16.46.77.07.37.67.98.28.58.89.2

Average Contraction Joint Spacing, m

Frequency

0%

20%

40%

60%

80%

100%

120%

Cumulative

Figure 25. Joint spacing frequency distribution for different types of JCP.
69

T0 ' Tc

(1 % 0.02)n&1

CESAL ' T0 ( (1 % 0.02)m &1

0.02

% 0.98T0 ( days

Truck Volume

Traffic data are one of the most important factors affecting joint faulting [3, 4]. Good quality

traffic data over the whole pavement life is very important for the study of the effect of

cumulative traffic on faulting. Available traffic data were obtained from the IMS table

TRF_MONITOR_BASIC_INFO for the sections under study. An analysis of monitoring traffic

data revealed that data were missing for a number of sections, and the available ESAL data

represented only a few years in pavement life, with large differences in values between different

years. Some of the sections with missing monitoring information had estimated information

available. Comparison of the ESAL values for the sections that have both monitoring and

estimated information showed large differences between the two.

Since the traffic data obtained from the LTPP IMS database were available only for a few years,

the traffic data for the remaining years were backcasted to the year when the pavement was

opened to traffic and forecasted to the year of the latest faulting survey in order to estimate the

cumulative traffic. Traffic data for the latest monitored year were assumed to be most accurate

and, therefore, were used in backcasting and forecasting procedures. In this study, a constant

growth factor of 2 percent was assumed for all the sections. The use of the 2 percent growth

factor was considered conservative, as it results in a high level of cumulative traffic loading. The

following equation incorporating the 2 percent growth factor was used to calculate the estimated

traffic at the beginning year for the test section:

where: T0 = Estimated annual ESALs for the first full year (Year of T0 ) since traffic

opening date.

Tc = Annual ESALs for the last available year (Year of Tc )in traffic record.

n = Year of Tc - Year of T0.

To be consistent with the backcasting approach, a constant growth rate of 2 percent was again

assumed to calculate the cumulative traffic at the time of the distress survey. The following

equation was used to compute the cumulative traffic:

where: CESAL= Cumulative ESALs since traffic opening date to the time of

faulting survey.

days = Number of days since traffic opening date to the end of the traffic

opening year, converted to a fraction of the year.

m = Full Date of Faulting Survey - Year of T0.
70

0

5

10

15

20

25

30

35

40

45

50

0 5 10 15 20 25 30 35 40

Age, years

CESAL, million

Figure 26 shows the distribution of CESALs with age. There is a general trend of higher

CESALs for older pavement sections; however, no strong correlation can be established. This

can be explained by different road functionalities that result in lower or higher ESALs per year. 

Attempts were made to correlate faulting values with CESALs, but no meaningful relations were

achieved.

The quality of the traffic data used is very questionable, as was addressed in chapter 2 of this

report. There is a strong need for a systematic procedure/guideline for traffic backcasting

applicable to all LTPP sections. This procedure should account for differences in traffic stream

(vehicle distribution by class) and growth rates specific to different road functional classes and

geographical regions. Available historical and monitoring data need to undergo QC analysis to

resolve conflicts between historical and monitoring traffic trends.

Figure 26. Distribution of CESALs with age.

Reinforcement Amount (for JRCP)

An attempt was made to relate reinforcement amount with crack faulting values. No conclusive

relations were established because of the overpowering effect of joint spacing on crack faulting

and the interdependent relationship between reinforcement amount and joint spacing.
71

Climatic Region

The difference between faulting values for different climatic regions was tested. Most of the

available sections with faulting status 1 were located in wet-freeze and wet-no-freeze zones, as

indicated in table 29. No JRCP sections with faulting status 1 and only three JPCP doweled

sections were found in dry-freeze and dry-no-freeze zones. JPCP sections without dowels had

enough sections for the comparative analysis in all four climatic zones.

Table 29. Number of available observations with faulting status 1.

DF Region DNF Region WF Region WNF Region

JPCP, 

No Dowels

Wheelpath 34 24 53 44

Edge 35 24 55 50

JPCP,

Doweled

Wheelpath 3 3 52 63

Edge 3 3 52 68

JRCP Wheelpath 0 0 93 38

Edge 0 0 96 49

Results of the t-tests for JPCP sections without dowels showed no significant difference between

joint faulting for sections located in dry-freeze and dry-no-freeze zones. There was a significant

increase in joint faulting for sections located in the wet-freeze zone (mean faulting 3.2 mm)

compared with the wet-no-freeze zone (mean faulting 2.0 mm), as well as for sections located in

the wet-freeze zone (mean faulting 3.2 mm) compared with the dry-freeze zone (mean faulting

1.6 mm), and for sections located in the wet-no-freeze zone (mean faulting 2.0 mm) compared

with the dry-no-freeze zone (mean faulting 1.1 mm).

For doweled JPCP sections, the results of the t-test did not show a significant difference between

joint faulting for sections in the wet-freeze zone and the wet-no-freeze zone. For JRCP sections,

the results of the t-test showed statistically higher joint faulting values for sections in the wetfreeze zone (mean faulting 1.1 mm) compared with the wet-no-freeze zone (mean faulting 1.0

mm). However, these differences for JRCP sections have no practical significance because of

the very low values of faulting that exist for JRCP sections.

A comparison of the mean faulting values obtained for JPCP and JRCP sections in different

climatic zones is presented in figure 27. It should be noted that non-doweled JPCP sections in

wet-freeze zones exhibited the worst mean faulting among all the categories—five times higher

than the mean faulting for doweled JPCP sections in the same climatic zone.

Summary of Faulting Trend Analysis

In this chapter, newly computed faulting indices and summary statistics were used to investigate

trends in faulting with time for sections with two or more surveys. The results of time-series

faulting trend analyses indicated that most of the sections exhibited a reasonable
72

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

Dry-Freeze Dry No-Freeze W et-Freeze W et No-Freeze

JPCP Sections, No Dowels

Mean Faulting, mm

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

Doweled WetFreezeDoweled Wet NoFreezeNo Dowels WetFreezeNo Dowels Wet NoFreeze

All JPCP Sections

Mean Faulting, mm

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

Doweled W et-Freeze Doweled W et No-Freeze

All JRCP Sections

Mean Faulting, mm

Figure 27. Comparison of mean faulting values for JPCP and JRCP sections

located in different climatic zones.
73

trend: average faulting values were increasing or remaining stable with age. Few sections with

questionable trends were identified. The reasons for these trends were investigated at the

regional offices and, as it was found, zero values were entered by default in the database when

null values should have been used instead. This change has been completed.

An assumption of zero average faulting values at the start of pavement in-service life was tested

using computed average faulting values for SPS-2 experiment sites. Ninety-seven percent of the

computed average edge faulting values and 99 percent of the computed average wheelpath

faulting values were less than 1 mm. Since the precision of the Georgia Faultmeter is ±1 mm, all

the values more than -1 mm and less than +1 mm can be reasonably assumed to be equal to zero. 

Therefore, an assumption of initial zero faulting is correct.

The effect of faulting on ride quality was investigated using JPCP sections with three or more

faulting and IRI surveys conducted no more than 1 year apart from each other. A strong

correlation was found between rate of change in faulting values and rate of change in IRI values.

An analysis was carried out to determine the usefulness of joint faulting and other related LTPP

data in identifying factors that affect joint faulting. The results of the data analysis indicated that

the following factors affect faulting:

• Use of load transfer devices has the greatest effect on the amount of joint faulting. Use of

dowel bars reduces joint faulting of JPCP sections by the factor of two.

• Use of larger diameter dowels results in lower faulting values for JPCP sections. 

• Use of widened lanes results in reduced edge faulting values, especially for JPCP

sections.

• Use of drainage features significantly reduces faulting, especially for JPCP non-doweled

sections.

• Use of stabilized base/subbase significantly reduces faulting, especially for JPCP nondoweled sections.

• Shorter joint spacing significantly reduces faulting in all pavement categories.

• Use of skewed joints did not show significant difference in faulting values for doweled

joints.

• Non-doweled JPCP sections located in wet-freeze zones exhibited the worst faulting

among all sections.

• Doweled joints exhibit very little faulting even after many years of service. The effect of

design features such as drainage, tied-concrete shoulder use, and joint spacing is not as

significant when doweled joints are used.
74

The results obtained in this chapter are limited by the resolution of the Georgia Faultmeter of ±1

mm. This resolution seems to be too large, since average faulting measurements are on the order

of 3 mm. The other limitation of the analysis was the difficulty of establishing a full set of

variables because of the large amount of missing complementary data. 
75

5. SUMMARY

The primary objectives of the study reported here were to examine the quality of the joint and

crack faulting data, provide recommendations for resolving questionable data, and develop

representative faulting indices and statistics for each jointed concrete pavement test section in the

LTPP program. In addition, preliminary analysis of the faulting data was conducted to identify

practical trends in faulting development.

The following observations and conclusions regarding faulting data availability, quality, and

relationships between faulting data and other pavement design features or characteristics were

derived from this study:

Faulting Data Availability

Data for 422 JCP sections were available in the IMS database at the time of the study. Out of

422 sections, only 307 sections had records in the faulting data table MON_DIS_JPCC_FAULT,

for a total of 24,108 records. The number of faulting surveys for these sections ranged from one

survey to nine, with 31 percent of sections having only one survey in the database. This

magnitude of missing data is considered very serious because faulting is one of the key distress

types associated with jointed concrete pavements. Future efforts should be focused on ensuring

that faulting data are collected as required.

Faulting Data Quality

The available faulting data were evaluated in terms of missing and questionable data. 

• Missing Faulting Data – For the faulting data studied, many sections have missing

faulting measurements at some joint and crack locations along the section. In some cases,

faulting measurement locations for joints from different surveys do not correspond to

each other. In other cases, different total numbers of joints were reported on different

surveys. Furthermore, the total number of joints counted from joint faulting tables did

not always agree with either the number of joints computed from the joint spacing data in

the inventory table or with the number of joints counted from PASCO distress maps. 

This problem was reported in LTPP Feedback Reports.

• Negative Faulting Data – Negative faulting values are present in 4 percent of all faulting

observations. The majority of negative faulting records were equal to -1 mm (73 percent

of all negative faulting cases). In most cases, the negative faulting values were random

occurrences, with a few repeated at the same joint/crack locations. In several instances,

negative faulting profiles were mirror images of the positive faulting profiles measured

on a different survey date. All cases of negative faulting were reported to FHWA.

• Reasons for Negative Faulting – The reasons for excessive negative faulting values were

investigated at the Regional Centers, and a few causes were identified. Negative mirror

image faulting resulted from the fact that the faultmeter was turned in the wrong direction
76

during data collection. Other negative faulting values were attributed to measurements

over patched or sealed joints.

• Negative Faulting of -1 mm – The possible reasons for negative faulting were investigated

using SPS-2 sites. It was found that 40 percent of SPS-2 sections had at least one joint

that exhibited a negative faulting value on the first faulting survey after construction. 

Most negative faulting values were equal to -1 mm. This fact can be attributed to random

positive and negative measurement variation taken on joints with zero faulting, as would

be expected for new construction (because of built-in surface texture and the precision of

the Georgia Faultmeter being ±1 mm).

• The overall quality of the faulting data reported in the IMS database is acceptable for the

development of faulting indices and summary statistics, in terms of data availability. 

Assessment of the available data indicated that up to 95 percent of faulting surveys could

be used for the development of representative indices and summary statistics. Only 1

percent of surveys were dismissed because of a large number of points with excessive

negative faulting (more than 25 percent of measurements per survey with less than -2

mm), and 4 percent were dismissed because of a large number of points with missing

faulting observations (more than 25 percent of measurements per survey).

• Precision of Georgia Faultmeter – The faulting data quality issue addressed in this study

was affected by the precision of the Georgia Faultmeter, which is the standard faulting

measurement equipment used in LTPP studies. A review of numerous faulting records

indicated that accuracy of ±1 mm is inadequate because representative maximum faulting

values, obtained as an average of all maximum faulting values for all sections and

surveys, were about 5 mm for non-doweled sections and 3 mm for doweled sections. It is

recommended that the Georgia Faultmeter be modified to read to 0.1 mm. Use of a more

precise device would significantly improve the quality of future faulting data collection

and benefit future pavement performance analysis, especially for the SPS-2 and SMP

sections that are still in the early stages of pavement service life.

• Available faulting data were also evaluated in terms of usefulness for faulting trend

analysis. It was found that less than 45 percent of sections had faulting data available

from three or more surveys. Therefore, trend analysis reported in this report is to be

viewed as “limited” or “preliminary.” It is recommended that more extensive trend

analysis be conducted as more data become available. The lack of faulting measurements

over time must be corrected in the future if the LTPP program is to provide significant

findings on ways to reduce faulting.
77

Computed Faulting Indices and Summary Statistics

• Representative Faulting Values for Each Survey – Mean faulting values were computed

for each section and each survey date where 75 percent or more measurements were

present and valid. Standard deviations, minimum and maximum faulting values, and

other related quantities were also computed for these cases. Computed faulting indices

and summary statistics can be found in a new LTPP database table,

MON_DIS_JPCC_FAULT_SECT.

• Outliers – The computed faulting statistics for each section survey were used to

determine the outlier faulting observations for each survey. About 30 percent of all the

surveys contain at least one outlier point along the section. The average percentage of

outlier points (for surveys that contain outliers) is about 5 percent.

Faulting Trend Analysis

• Faulting Data Time History – Time history plots of mean faulting data were generated

and examined for all the sections. Most of the sections show a reasonable trend over

time, with average faulting values increasing or remaining stable with age. Few sections

exhibited questionable time trends of average faulting—either decreasing or fluctuating

with time.

• Reasons for Questionable Faulting Trends – Sections with questionable trends were

reported in LTPP Feedback Reports. Most of the questionable trends resulted from zero

faulting reported on a survey when non-zero faulting was reported on the previous or on

the following surveys. As was found through the response from the Regional Centers,

zero values were entered by default in the database when null (resulting in an empty table

cell) values should have been used instead.

• Initial Faulting – An assumption of zero average faulting values at the start of pavement

in-service life was tested using computed average faulting values for SPS-2 experiment

sites. The mean faulting measurements for all SPS-2 sections was 0.2 mm for both

wheelpath and edge on the first survey since construction. Also, the distributions indicate

that 97 percent of the computed average edge faulting values and 99 percent of the

computed average wheelpath faulting values are less than 1 mm. These results indicate

that the mean faulting of newly constructed SPS-2 joints is very close to zero, as would

be expected.

Faulting Rate Versus IRI Rate for JPCP Sections

• The effect of faulting on ride quality was investigated using JPCP sections with three or

more faulting and IRI surveys conducted no more than 1 year apart from each other. A

strong correlation was found between rate of change in faulting values and rate of change

in IRI values for JPCP sections. Thus, faulting was found to be a major component of

increased roughness of JCP.
78

Effects of Various Design Features and Site Conditions on Faulting 

Computed faulting values were compared for key JCP design features, such as dowel bars, joint

spacing, drainage, and traffic, for different pavement age groups. The results of the data analysis

indicated that the following factors affect faulting:

• Use of load transfer devices has the greatest effect on the amount of joint faulting. Use of

dowel bars reduces joint faulting of JPCP sections by a factor of two.

• Use of larger diameter dowels results in lower faulting values for JPCP sections. 

• Use of widened lanes results in significant reduction of edge faulting values, especially

for JPCP sections.

• Use of drainage features significantly reduces faulting, especially for JPCP non-doweled

sections.

• Use of stabilized base/subbase significantly reduces faulting, especially for JPCP nondoweled sections.

• Shorter joint spacing significantly reduces faulting in all pavement categories.

• Use of skewed joints did not show a significant difference in faulting values for doweled

joints.

• Non-doweled JPCP sections located in a wet-freeze climatic zone exhibited the worst

faulting among all sections.

• Doweled joints exhibit very little faulting even after many years of service. The effects of

design features such as drainage, tied-concrete shoulder use, joint spacing, and climatic

zone are not as significant when doweled joints are used.

The results obtained in this study are affected by the resolution of the Georgia Faultmeter (±1

mm). This resolution seems to be too large, because average faulting measurements are on the

order of 3 mm. 
79

6. RECOMMENDATIONS FOR FUTURE RESEARCH

To improve the reliability of the collected faulting data, the following recommendations are

made:

• The measurement of faulting needs to be given a high priority. A large amount of data is

missing, and time-series data are scarce.

• More accurate faulting measurements should be obtained. It is recommended that the

Georgia Faultmeter be modified or an alternative device be used to read to 0.1 mm. Use

of the more precise device would significantly improve the quality of future faulting data

collection and benefit future pavement performance analysis, especially for the SPS-2 and

SMP sections that are still in the early stages of pavement service life.

• Use of automated profilometer data to detect faulting and slab curvature should be

investigated as a means of improving the quality of faulting data.

• Whenever negative faulting values are recorded at multiple points along the section, or if

faulting records for the section do not contain any positive readings (possibility of mirror

image), the Faultmeter should be calibrated and measurements should be repeated and the

reasons for negative faulting should be commented upon.

• To avoid inconsistency in records of joint/crack locations currently found in faulting data,

it is recommended that the Regional Offices use a template for each section to record the

joint faulting data. This way, the same joint and crack locations will be used in every

survey, and joint/crack location data will be more consistent.

• Recording the time of faulting measurement during the day will allow analysts to account

for the effect of temperature gradient through the slab on joint faulting.

• Joint and crack load transfer efficiency at each joint and crack, loss of support data, and

slab curling data should be utilized in future analyses. 

These recommendations for improvement of reliability of the collected faulting data have been

submitted to FHWA as an LTPP Data Analysis Feedback Report numbered ERES_BW_70.
80
81

REFERENCES

1. Stone, J., Georgia Digital Faultmeter, Report FHWA-GA-91-SP9010, Federal Highway

Administration, Washington, DC, 1991.

2. Wells, G.K., and W.A. Nokes, Synthesize PCCP Design Parameters Researched by

Caltrans and Others, Caltrans Publication No. 65328-637391-31111, F91TL01, June 10,

1991.

3. Yu, H.T., M.I. Darter, K.D. Smith, J. Jiang, and L. Khazanovich, Performance of

Concrete Pavements: Volume III-Improving Concrete Pavement Performance, FHWARD-95-11, Federal Highway Administration, Washington, DC, 1996.

4. Wu, C.L., J.W. Mack, P.A. Okamoto, and R.G. Packard, Prediction of Faulting of Joints

in Concrete Pavements. Volume 2, Proceedings of the International Conference on

Concrete Pavement Design, Purdue University, Lafayette, IN, 1993.

5. ASTM, ASTM Standards on Precision and Bias for Various Applications, 5th Edition,

West Conshohocken, PA, 1997.

6. Khazanovich, L., M. Darter, R. Bartlett, Common Characteristics of Good and Poorly

Performing PCC Pavements, Federal Highway Administration, Washington, DC, 1997.

7. Titus-Glover, L., E. Owusu-Antwi, and M.I. Darter, Design and Construction of PCC

Pavements, Volume III: Improved PCC Performance, Report No. FHWA-RD-98-113, 

Federal Highway Administration, Washington, DC, January 1999.

8. Simpson, A.L., et al., Sensitivity Analyses for Selected Pavement Distress, Strategic

Highway Research Program, Report Number SHRP-P-393, Washington, DC, 1994.

9. Yu, H.T., et al., Guidelines for Subsurface Drainage, National Cooperative Highway

Research Program, Project 1-34, Final Report, 1998.
82
83

APPENDIX A % COMPUTATION AND STORAGE OF

LTPP PCC JOINT FAULTING SUMMARY STATISTICS

Joint faulting is one of the key distress types that leads to decline in ride quality of jointed rigid

pavements. Transverse joint faulting is being monitored regularly at the jointed concrete

pavement test sections under the Long-Term Pavement Performance (LTPP) program. Faulting

data are available for the GPS-3, GPS-4, GPS-9, SPS-2, SPS-4, SPS-6, and SPS-8 experiments. 

These sections represent the following pavement types: 

 Jointed plain concrete pavement (JPCP).

 Jointed reinforced concrete pavement (JRCP).

 JPCP over JRCP.

 JPCP over JPCP.

 JRCP over JPCP.

 JRCP over JRCP.

 JPCP over continuously reinforced concrete pavement (CRCP).

 JRCP over CRCP.

Joint faulting is measured at each joint at two locations, the edge (corner) and at the outside

wheelpath, using the Georgia Faultmeter. Faulting is also measured at some transverse crack

locations.

In addition to the joint-by-joint faulting data currently available in the Information Management

System (IMS), it is preferable to have available representative faulting values and companion

statistics for each site for each measurement cycle (site visit). The availability of computed

representative values of edge and wheelpath faulting would minimize duplication of effort in

future analysis work and provide a consistent set of data to be used for study of joint and crack

faulting. The representative faulting values for each test section can be used for the investigation

of time-series trends and proof testing of the faulting data. Analysts can make use of the

representative faulting indices and statistics for the development of mechanistic-based prediction

models for joint faulting. To provide LTPP users with representative faulting indices and

statistics, a new computed parameters table is proposed for inclusion in the IMS database.

Structure of Table MON_DIS_JPCC_FAULT_SECT

The new table, MON_DIS_JPCC_FAULT_SECT, resides in the monitoring module within the

IMS database. The data source for table MON_DIS_JPCC_FAULT_SECT is the IMS table

MON_DIS_JPCC_FAULT. The new table includes calculated statistical values developed

separately for joint faulting and for transverse crack faulting for each site for each measurement

cycle (site visit). Therefore, each test section may contain up to two records per site visit: one for

joint faulting statistics and one for transverse crack faulting statistics. A specially coded column

is used to indicate whether it is a joint or crack record. Each record contains separately

calculated statistical indices for wheelpath and for edge faulting. Calculated statistical faulting

indices include average, minimum, and maximum faulting, standard deviation, number of

observations used in developing statistics, number of missing observations, number of
84

observations with negative faulting, and total number of observations per visit. Comment fields

indicate the reasons that faulting statistics are not provided for certain survey dates.

Table 30 contains the schema and field definitions for the new table

MON_DIS_JPCC_FAULT_SECT. Table 31 contains the description of codes used to define

availability of data for computation of faulting indices and summary statistics. The description

of computational algorithm to produce faulting indices and summary statistics can be found in

chapter 3 of the report entitled Assessment of LTPP Faulting Data.
85

Table 30. Schema and field definition for table MON_DIS_JPCC_FAULT_SECT.

Field Name Unit Field Type Codes Data Dictionary Description

STATE_CODE NUMBER(2,0) Code identifying the State or

Province

SHRP_ID VARCHAR2(4) SHRP section identification

SURVEY_DATE DATE The date the survey was

performed

CRACK_OR_JOINT VARCHAR2(1) see desc A code indicating whether the

faulting is at cracks (C) or

joints (J)

CONSTRUCTION_NO NUMBER(1,0) Event number indicating

pavement layer changes in a

section. Set to 1 when a

section is chosen for inclusion

in the LTPP study and

incremented after each

pavement layer change. It is in

all tables that relate to a

section at a specific time

RECORD_STATUS VARCHAR2(1) Status code related to level of

QC, set to Level A initially

NO_TOTAL_POINT_LOC NUMBER(3,0) Total number of points

available for wheelpath or

edge faulting measurements

AVG_EDGE_FAULT mm NUMBER(3,0) Average edge faulting

calculated per site per survey

MIN_EDGE_FAULT mm NUMBER(3,0) Minimum edge faulting per

site per survey

MAX_EDGE_FAULT mm NUMBER(3,0) Maximum edge faulting per

site per survey

STD_EDGE_FAULT mm NUMBER(3,0) Standard deviation for edge

faulting calculated per site per

survey

NO_VALID_EDGE_FAULT NUMBER(3,0) Number of edge faulting

observations per survey with

values greater than %1 mm

NO_NULL_EDGE_FAULT NUMBER(3,0) Number of missing edge

faulting observations per

survey

NO_NEG2_EDGE NUMBER(3,0) Number of negative edge

faulting observations per

survey with values less than %2

mm

EDGE_FAULT_STATUS NUMBER(1,0) see 1 A code describing the

availability of data to compute

edge faulting indices

 AVG_WHEELPATH_FAULT mm NUMBER(3,0) Average wheelpath faulting

calculated per site per survey

MIN_WHEELPATH_FAULT mm NUMBER(3,0) Minimum wheelpath faulting

per site per survey
Field Name Unit Field Type Codes Data Dictionary Description

86

MAX_WHEELPATH_FAULT mm NUMBER(3,0) Maximum wheelpath faulting

per site per survey

STD_WHEELPATH_FAULT mm NUMBER(3,0) Standard deviation for

wheelpath faulting calculated

per site per survey

NO_VALID_WHEELPATH_FAULT NUMBER(3,0) Number of wheelpath faulting

observations per survey with

values greater than %1 mm 

NO_ NULL_WHEELPATH_FAULT NUMBER(3,0) Number of missing wheelpath

faulting observations per

survey

NO_ NEG2_WHEELPATH NUMBER(3,0) Number of negative wheelpath

faulting observations per

survey with values less than %2

mm 

WHEELPATH_FAULT_STATUS NUMBER(1,0) see 1 A code describing the

availability of data to compute

wheelpath faulting indices

Note: 1

 see table 31 for a list of codes for fields WHEELPATH_FAULT_STATUS and

EDGE_FAULT_STATUS.

Table 31. Code list for fields WHEELPATH_FAULT_STATUS and EDGE_FAULT_STATUS.

Code Name Code Description

1 Faulting statistics are calculated since more than 75% of points have faulting

values equal to or above zero.

2 Faulting statistics were not calculated because of a large number of points

with missing faulting observations (25% or more).

3 Faulting statistics were not calculated because of a large number of points

with negative faulting values in excess of 1 mm (25% or more).

4 Faulting statistics were not calculated because of a large number of points

with either missing and negative faulting values in excess of 1 mm (25% or

more combined).
87

APPENDIX B % AVERAGE FAULTING AND AVERAGE IRI

TIME HISTORY PLOTS

Appendix B contains time history plots for 307 sections (121 GPS-3 sections, 52 GPS-4 sections,

18 GPS-9 sections, 65 SPS-2 sections, 43 SPS-4 sections, 6 SPS-6 sections, and 2 SPS-8

sections). These sections are presented sequentially by Experiment Type, State ID, and Section

ID.