LTPP Profile Variability

FHWA Report No. FHWA-RD-00-113

June 2000

Prepared by:

ERES Consultants and Fugro-BRE

A Division of Applied Research Associates, Inc.

9030 Red Branch Road, Suite 210

Columbia, Maryland 21045

Prepared for:

Pavement Performance Division, HRDI

Federal Highway Administration

6300 Georgetown Pike

McLean, Virginia 22101-2296

 Research and Development

Turner-Fairbank Highway Research Center

U.S. Department of Transportation 6300 Georgetown Pike

Federal Highway Administration McLean, Virginia 22101-2296
FOREWORD

High quality pavement profile data are important to both the LTPP research, and highway agency

pavement management systems. This report documents a study conducted to evaluate the quality

and variability of the longitudinal profile data in the Long Term Pavement Performance (LTPP)

database. The study focuses on LTPP profiles collected between June 1989 and October 1997. 

These profiles were visually reviewed for saturation spikes, lost lock, shifted starts, wrong

location, out of study and other equipment-and-operator-related problems. Noted problems are

identified in the report. Repair, replacement, or deletion of these files was completed in

conjunction with this study. Remaining or replaced good profile data were used for analysis of

variability.

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. Trade and

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

object of the document.
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iii

TABLE OF CONTENTS

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

CHAPTER 2. DATA QUALITY REVIEW .........................................3

Saturation Spikes ...........................................................4

Lost Lock .................................................................4

Incorrect Start Location ......................................................5

Wrong Test Location ........................................................5

Miscalibrated DMI ..........................................................5

Out of Study ...............................................................6

Other Sources of Data Variability ..............................................7

Summary .................................................................7

CHAPTER 3. PROFILE DATA QUALITY UPGRADE ..............................9

Repairable Profiles ..........................................................9

Irreparable Profiles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

Pending Profiles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

Effect of Profile Data Upgrade . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

IRI Variability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

IRI Slope . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

CHAPTER 4. RUN-TO-RUN VARIABILITY . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

General Statistical Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

Design of Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

Data Transformation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16

Run-to-Run Analysis Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18

Run-to-Run Variability Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20

Effect of Region . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

Effect of Pavement Surface Type . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22

Effect of Pavement Material Structure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23

Effect of Equipment Differences . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23

Effect of Photocell Initiation Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26

Effect of Season . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27

Effect of Time of Day . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27

Effect of Roughness Level . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29

Quality Control Implications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30

Summary of Run-to-Run Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32

CHAPTER 5. VISIT-TO-VISIT VARIABILITY . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33

Inflection Point Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33

Time to Rapid IRI Increase . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33

IRI Time-History Slopes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35

IRI Roughness Level Slopes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36

Between-Visit Variability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37
iv

TABLE OF CONTENTS (Continued)

Seasonal Effects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37

Daily Change Effects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38

Between-Visit Confidence Limits . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39

Summary of Between-Visit Variability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43

CHAPTER 6. ADDITIONAL ANALYSIS RESULTS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45

Effect of Saturation Spikes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45

Effect of Shifted Profiles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46

Effect of Wheelpath Location . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47

Effect of Incorrect IRI Coefficients . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49

Effect of the Number of Runs per Visit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50

Effects of Moist and Wet Pavement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51

CHAPTER 7. CONTINUING EFFORTS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55

CHAPTER 8. RECOMMENDATIONS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57

Field Evaluation of Profiles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57

Office Review of Profile Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58

IMS Database Design and Procedures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58

Repeatability of K.J. Law Profilometer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59

Single-Visit Variability Confidence Limits . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59

Between-Visit Confidence Limits . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60

Calibration of DMI . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61

Number of Runs in the IMS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61

APPENDIX A. DESCRIPTION OF PROFILE PROBLEMS . . . . . . . . . . . . . . . . . . . . . . . . . . 63

Descriptions of Questionable Profiles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63

Saturation Spikes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63

Lost Lock in Profiles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64

Incorrect Start Location . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66

Unreported Maintenance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68

Unreported Rehabilitation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68

Testing at a Different Location . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69

DMI Miscalibrated . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70

APPENDIX B. RECOMMENDED ACTIONS FOR ADDRESSING

QUESTIONABLE PROFILE DATA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73

Saturation Spikes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73

Lost Lock . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73

Shifted Profiles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74

Miscalibrated DMI . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74

Unreported Rehabilitation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75
v

TABLE OF CONTENTS (Continued)

Unreported Maintenance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75

Wrong Testing Location . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75

APPENDIX C. PROFILE VIEWER DESCRIPTION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77

APPENDIX D. PROFILE DATA EXTRACTOR DESCRIPTION . . . . . . . . . . . . . . . . . . . . . . 79

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79

Hardware and System Requirements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79

Starting the Profile Data Extractor Application . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79

Using the Profile Data Extractor Application . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79

APPENDIX E. SUMMARY OF ALL SATURATION SPIKES . . . . . . . . . . . . . . . . . . . . . . . . 81

APPENDIX F. SUMMARY OF ALL LOST LOCK PROFILE RUNS . . . . . . . . . . . . . . . . . 103

APPENDIX G. SUMMARY OF ALL SHIFTED PROFILES . . . . . . . . . . . . . . . . . . . . . . . . 113

APPENDIX H. SUMMARY OF WRONG TESTING LOCATION DATA . . . . . . . . . . . . . . 125

APPENDIX I. SUMMARY OF OUT-OF-STUDY PROFILE DATA . . . . . . . . . . . . . . . . . . 129

APPENDIX J. SUMMARY OF MISCALIBRATED DMI PROFILE RUNS . . . . . . . . . . . . 131

APPENDIX K. SUMMARY: POSSIBLY UNREPORTED MAINTENANCE . . . . . . . . . . . 135

APPENDIX L. SUMMARY OF UNREPORTED REHABILITATION . . . . . . . . . . . . . . . . . 137

GLOSSARY . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 139

REFERENCES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 143

ADDENDUM&QUALITY REVIEW OF 1997-1999 PROFILE DATA . . . . . . . . . . . . . . . . . 145

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 145

Methods for Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 145

Profile Data Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 145

IRI Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 145

Profile Review Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 146

Saturation Spikes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 146

Lost Lock . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 147

Wrong Location . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 147

Zero IRI . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 147

Shifted Start . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 147

Variable Profiles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 148
vi

TABLE OF CONTENTS (Continued)

Unreported Maintenance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 149

Unreported Rehabilitation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 149

Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 149

Recommendations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 150

APPENDIX M. RESULTS OF SUBSEQUENT DATA REVIEW . . . . . . . . . . . . . . . . . . . . . 151
vii

LIST OF FIGURES

Figure 1. Effect of upgrade on IRI variability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

Figure 2. Effect of upgrade on IRI slopes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

Figure 3. Histogram of IRI coefficients of variation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

Figure 4. Cumulative IRI COVs for LTPP database . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

Figure 5. Regional run-to-run variability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

Figure 6. Relationship of IRI with standard deviation of IRI . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

Figure 7. Transformed GPS IRI versus standard deviation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18

Figure 8. Histogram of GPS IRI standard deviations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19

Figure 9. Histogram of GPS transformed IRI standard deviation . . . . . . . . . . . . . . . . . . . . . . . . 19

Figure 10. GPS histogram of normalized IRI standard deviation . . . . . . . . . . . . . . . . . . . . . . . . 20

Figure 11. Regional variability for GPS and SPS sections . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22

Figure 12. Variation among regional surface types . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23

Figure 13. GPS regional IRI variability for pavement material structures . . . . . . . . . . . . . . . . . 24

Figure 14. SPS regional IRI variability for pavement material structures . . . . . . . . . . . . . . . . . 24

Figure 15. Variability of regional profilers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

Figure 16. IRI variability from equipment start method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26

Figure 17. Seasonal effect on regional IRI variability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27

Figure 18. GPS time series IRI variability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28

Figure 19. SPS time series IRI variability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28

Figure 20. Regional IRI variability across roughness level . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29

Figure 21. Confidence limits for GPS transformed IRI standard deviation . . . . . . . . . . . . . . . . 31

Figure 22. Confidence limits for SPS transformed IRI standard deviation . . . . . . . . . . . . . . . . 31

Figure 23. Histogram of IRI inflection point time by surface type . . . . . . . . . . . . . . . . . . . . . . . 34

Figure 24. Histogram of IRI inflection point time by region . . . . . . . . . . . . . . . . . . . . . . . . . . . 34

Figure 25. IRI trends for 60 selected GPS sections . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35

Figure 26. IRI trends for 56 SPS sections . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36

Figure 27. Change in IRI between visits . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37

Figure 28. Seasonal effect on variability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38

Figure 29. Daily effect on IRI variability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39

Figure 30. Confidence limits for AC IRI change . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41

Figure 31. Confidence limits for PC IRI change . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41

Figure 32. Confidence limits for APC IRI change . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42

Figure 33. Effect of spike height on smoothness statistics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45

Figure 34. Effect of shifted profile start locations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46

Figure 35. Effect of wheelpath wander on AC pavement IRI . . . . . . . . . . . . . . . . . . . . . . . . . . . 48

Figure 36. Absolute error in IRI mean with runs per visit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52

Figure 37. Effect of surface moisture on average IRI . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53

Figure 38. Unnoted saturation spike in right wheelpath profile from Ontario . . . . . . . . . . . . . . 65

Figure 39. Lost lock in left wheelpath of Illinois site . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65

Figure 40. Occasionally intermittent lost lock in North Carolina profiles . . . . . . . . . . . . . . . . . 66

Figure 41. Rapidly intermittent lost lock in Maine profiles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67

Figure 42. Shifted start for 1990 profiles in Iowa . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67

Figure 43. Possible unreported maintenance at Minnesota section . . . . . . . . . . . . . . . . . . . . . . . 68
viii

LIST OF FIGURES (Continued)

Figure 44. Possible incorrectly reported rehabilitation at Indiana section . . . . . . . . . . . . . . . . . . 69

Figure 45. Incorrect testing location at Kansas test section . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70

Figure 46. Miscalibrated DMI in Kansas section . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71

Figure 47. Multi-date view from Profile Viewer software . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77

Figure 48. Multiple-run output from Profile Viewer software . . . . . . . . . . . . . . . . . . . . . . . . . . 78

Figure 49. Master table output from Profile Viewer software . . . . . . . . . . . . . . . . . . . . . . . . . . 78

Figure 50. Source list from Profile Data Extractor utility . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80

Figure 51. Selected list from Profile Data Extractor utility . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80
ix

LIST OF TABLES

Table 1. Reparable profile runs in the LTPP database .................................6

Table 2. Irreparable profile runs in the LTPP database ................................6

Table 3. Pending profile runs in the LTPP database ...................................6

Table 4. Status of runs with equipment/operator problems .............................7

Table 5. Categorization of profile problem runs ......................................9

Table 6. Effect of upgrade on average IRI slope values . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

Table 7. GPS statistical model results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

Table 8. SPS statistical model results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

Table 9. Section visits using old and new Profilometers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

Table 10. Confidence limits for transformed average IRI standard deviations . . . . . . . . . . . . . 30

Table 11. Statistically different slopes for IRI intervals . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40

Table 12. Summary of wheelpath effect analysis on site 1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48

Table 13. Summary of wheelpath effect analysis on site 2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48

Table 14. Summary of wheelpath effect analysis on site 3 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49

Table 15. Effect of incorrect coefficients on IRI . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50

Table 16. Absolute error for mean IRI . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51

Table 17. Questionable profile characteristics noted . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63

Table 18. Profile runs with saturation spikes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81

Table 19. Profile runs lost lock . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103

Table 20. Profile runs with shifted start locations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 113

Table 21. Profile runs tested in the wrong location . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 125

Table 22. Profile runs for out-of-study sections . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 129

Table 23. Profile runs with miscalibrated DMI . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 131

Table 24. Profile runs with possibly unreported maintenance . . . . . . . . . . . . . . . . . . . . . . . . . 135

Table 25. Profile runs with unreported rehabilitation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 137

Table 26. Results of the supplemental profile review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 146

Table 27. Profile runs with saturation spikes (1997-1999) . . . . . . . . . . . . . . . . . . . . . . . . . . . . 151

Table 28. Profile runs with lost lock (1997-1999) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 154

Table 29. Profile runs with shifted start locations (1997-1999) . . . . . . . . . . . . . . . . . . . . . . . . 155

Table 30. Profile runs tested in the wrong location (1997-1999) . . . . . . . . . . . . . . . . . . . . . . . 164

Table 31. Profile runs with IRI values of zero (1997-1999) . . . . . . . . . . . . . . . . . . . . . . . . . . . 165

Table 32. Profile runs with unreported maintenance or rehabilitation (1997-1999) . . . . . . . . . 166
x

LIST OF ABBREVIATIONS

AC Asphalt concrete

ANOVA Analysis of Variance

APC AC overay of PC

ASTM American Society for Testing and Materials

BWP Both wheelpaths

CN Construction number

COV Coefficient of Variation

DMI Distance Measuring Instrument

FHWA Federal Highway Administration

GPS General Pavement Studies

IMS Information Management System

IRI International Roughness Index

LTPP Long Term Pavement Performance

LWP Left wheelpath

MS Microsoft

ODBC Open Database Connectivity

PC Portland (Cement) Concrete

QC Quality Control

RCO Regional Coordination Office

RCOC Regional Coordination Office Contractor

RMSVA Root Mean Square Vertical Acceleration

RWP Right wheelpath

SD Standard Deviation

SHRP Strategic Highway Research Program

SPS Special Pavement Studies
1

CHAPTER 1. INTRODUCTION

The Long Term Pavement Performance (LTPP) program has been collecting profile and

International Roughness Index (IRI) information from more than 2,062 test sections since 1989

using K.J. Law 690DNC optical sensor Profilometers. Analysis of the IRI data has been limited,

but with the increasing distribution of the LTPP DataPave software, this data is seeing increasing

use. In an effort to confirm the quality of LTPP IRI data in the Information Management System

(IMS) database and to document its variability, LTPP initiated an analysis of IRI variability in

September 1997. This report documents the results of that study.

Before the data could be analyzed, the quality of the data needed to be confirmed, the single-visit

and multiple-visit IRI variability needed to be evaluated, and recommendations and confidence

limits needed to be developed for quality control of profile data collection. Although it was

initially conceived as a small part of the analysis, the verification and upgrade of data quality

became the largest effort in this analysis. During visual review, equipment- and processingrelated problems were noted in 14.6 percent of the IMS profiles. Repair and upgrade of 4.5

percent of the IMS data were completed. About 4 percent of the profile runs were noted as

irreparable and were deleted from the database, and nearly 2,800 runs (5.9 percent of the

database) were considered questionable and are awaiting review and reprocessing or deletion by

Regional Coordination Office Contractors (RCOC’s).

Single-visit (run-to-run) variability among the five repeated profile runs at 152.4-m test sections

has been analyzed using confirmed and upgraded IRI values (nearly 90 percent of the values in

the database). This analysis included transforming the data to remove the effect of IRI level on

variability. Several conclusions were drawn based on the study of the effects of pavement type,

testing time, season, region, equipment type, profile initiation method, and surface roughness. 

Confidence limits for the expected variability between repeated runs are proposed as a result of

this analysis.

Multiple-visit (visit-to-visit) variability between data collected on different dates was also

studied, resulting in conclusions regarding the effect of daily and seasonal changes on IRI

variability. The presence of inflection points in IRI trends was reviewed. Computed confidence

intervals for the expected yearly change in IRI resulting from equipment, seasonal, wheelpath,

and other random variation were also developed.

Included in this report is a summary of the data quality review findings and upgrade procedures. 

Analyses of the run-to-run and visit-to-visit variability are then discussed, followed by a review

of other analysis activities. Confidence limits are then provided, along with recommendations

for use of the results.
2
3

CHAPTER 2. DATA QUALITY REVIEW 

Profile data quality in the IMS database was initially reviewed using IRI variability and slope

values. This review indicated that focusing on outlier IRI values was insufficient to identify all

profile quality problems. As a result, profile plotting software&originally developed for review

of a small portion of select questionable profiles&was used to plot and visually review the more

than 47,000 profile runs in the IMS database.

The initial statistical review of the IRI data in the IMS database indicated that the run-to-run

variability in the left and right wheelpath IRI values was small. Some of these profile sets with

large run-to-run variability exhibited equipment or operator problems; many others did not. In a

review of the visit-to-visit IRI slopes for a given section, nearly 22 percent of the unrehabilitated

test sections exhibited slopes of less than zero for the linear trend of the average IRI. This trend

indicates that the pavements are becoming smoother with age (which is illogical), that there is a

problem with the profile data, or possibly that unreported maintenance is occurring.

To more easily study the reasons for these trends, the Profile Viewer software was developed for

reviewing and overlaying plots of measured LTPP pavement profiles. This software, described

further in Appendix C, allows the operator to select and plot individual left or right wheelpath

runs from single or multiple site visits. IRI values are displayed for each run, and comments

from original data collection are also displayed for review.

Initially, the Profile Viewer software was intended only for review of the outlier section profiles

identified in the IRI variability evaluation. However, after viewing profiles for 77 sections in 5

States and finding problems in 52 sections, it was determined that outlier searches of IRI

statistics did not identify all of the data collection problems revealed in the profile review. Based

on this information, the necessity of plotting and viewing all profile data was evident.

A 2.4-Gb file containing all IMS profile data was downloaded on October 7, 1997, and was split

into Microsoft (MS) Access database files for review. Initially, 9,190 General Pavement Studies

(GPS) profile plots were printed and visually reviewed. Then, 11,650 Special Pavement Studies

(SPS) profile plots were printed. Individual color profiles for the left and right wheelpaths of all

five runs from each section were visually reviewed for consistency and for the presence of

equipment- or operator-related problems. In addition, color-overlaid profiles of the left and right

wheelpaths for the first run from each section visit were reviewed to confirm the testing location

and other discrepancies. This provided the first comprehensive review of the repeatability of the

profiles in the IMS database. 

Overall, the LTPP profile database appeared to have good integrity, with profiles and IRI values

that reflect excellent equipment and good collection/processing methods. Many of the runs

completed at one site on the same day overlay each other exceptionally well, giving the

appearance of a single profile line. IRI values exhibit a steady increase in roughness between

consecutive visits for many test sections. However, during the profile review, several types of

questionable profile characteristics also became evident, including the following:

 Unnoted equipment-related saturation spikes.

 Partial and complete lost lock.
4

 Incorrect start location.

 Wrong (unknown) test location.

 Miscalibrated Distance Measuring Instrument (DMI).

Descriptions and illustrations of these questionable characteristics are provided in Appendix A. 

Many of these characteristics were described by RCOC profiler operators and data reviewers in

the comments listed in the IMS MON_PROFILE_MASTER table, but others do not appear to

have been noted during field data collection or final evaluation. 

Saturation Spikes

Saturation spikes occur as an excess of light is returned to the Profilometer optical sensors,

generally as reflections from the transverse pavement stripes used to mark the beginning and end

of each test section. This typically results in profiles that increase in amplitude from 5 to 75 mm

or more at the location of the spike. Such spikes can be flagged using the LTPP ProQual

software so that they are not included in the IRI calculations. However, the spikes in the older

version (ProQual 1.4) were still included in computation of the other smoothness indices.

All saturation spikes were identified and recorded in the visual review. These included both

those originally edited by RCOC engineers and those identified under the current review. The

presence of edited spikes was determined from an August 1998 download from the IMS

database. Saturation spikes unnoted during the original review accounted for 4.9, 6.6, 1.6, and

1.6 percent (average 3.8 percent) of the site visit data in the North Atlantic, North Central,

Southern, and Western Region profiles, respectively. Saturation spikes in section visit profiles

were 1.8 to 11.5 times (averaging 5.2 times) more common in the left sensor than in the right for

all regions. 

Lost Lock

Lost lock occurs when insufficient light is returning to the optical sensors. As a result, the

profile contains only information provided by the accelerometers. The resulting profile is

typically very smooth for compete lost lock and choppy when lost lock is intermittent. 

Significant lost lock was noted in profiles from about 3.9 percent of site visits. Rapidly

intermittent lost lock accounts for about 51 percent of the noted lost lock, and occasionally

intermittent or full lost lock makes up the rest. It is possible that the regular short wavelength

chatter noted as rapidly intermittent lost lock could, in some cases, be the result of a loose mirror

in the optical receiver or other system noise. 

Regional Profilometers varied in the sensor that exhibits the most lost lock. The North Atlantic,

North Central, and Western Region Profilometers were 1.2 to 2.9 times more likely to exhibit

lost lock characteristics on the right sensor, while the Southern Region Profilometer had 5.3

times more lost lock on the left sensor. Lost lock characteristics were most prominent in the

North Atlantic Region’s Profilometer, with 8.2 percent of its profile runs indicating significant

lost lock.
5

There appears to have been a shift in the North Atlantic Profilometer from primarily producing

lost lock in the left sensor to doing so in the right sensor around September 1994. Prior to that

date, lost lock was identified in the left sensor 5.7 times more than in the right sensor. 

Subsequent to that date, right sensor lost lock was 8.3 times more common. This shift was noted

by the RCOC, and attempts to identify and remedy the problem were taken with only limited

success. 

Incorrect Start Location

Occasionally, the profiles for individual runs or for a single date do not begin at the same

location as the remaining test dates. This has been noted as a "shifted profile start."

Profile runs where the start location was incorrect but the spatial relation to the true start location

is known are identified in the shifted start categories listed in table 3 and in Appendix G. Shifted

starts, or profile runs shifted more than 5 m, are most common in the North Central (4.9 percent)

and Western (7.2 percent) Regions. The estimated range of shifted profile distances in the

Western Region is 5 to 53 m, with an average distance of 11 m. Western Region GPS section

profile shifts averaged 33 m, and SPS sections with shifts averaged 35 m. The average GPS and

SPS profile shifts for the North Central Region, where present, are 21 and 441 m, respectively. 

Shifted profiles for the remaining regions are evident in less than 1.2 percent of section visit

profile runs.

Wrong Test Location

Several profiles in the IMS database for a single date or for consecutive dates do not match those

of previous dates for the same test section. It is evident that they were collected in an unknown

location different from the true section location, and they are categorized as such. 

Profiles identified as being in the wrong location are different from those identified as having a

shifted start location because the offset from the true section location is unknown. If the

reviewers had exact knowledge of a shift in profile location, the runs were identified as being in

the shifted start category. The majority of these "wrong location" runs occurred in the North

Central Region, accounting for 1.2 percent of the IMS database. 

Miscalibrated DMI

A miscalibrated DMI affects profiles by including too little or too much profile data in a section

profile. Occasions where the DMI appears to be miscalibrated more than 1.5 m in 152.4 m have

been noted. 

Miscalibration of the DMI was only found to be a significant problem in the North Central

Region’s profiles, with 3.1 percent of the profile runs being off by 6 to 8 m. All of these runs

were completed in May 1990, April 1995, or June 1995. This indicated that an equipment

problem was experienced, the calibration period was too infrequent, or the calibration test section

was inadequate at those times.
6

Out of Study

Correspondence with RCOC personnel regarding unaccounted-for changes in section profiles

between visits has identified several profiles in the IMS database that were collected after the

section was declared "out of study." This is possible because test section removal from the LTPP

study may occur several years after section rehabilitation, after additional profile collection and

database entry have been completed. 

Summary tables listing the noted characteristics related to equipment or operator problems are

shown in Appendixes E through L of this report. Tables 1, 2, and 3 list the number of LTPP test

section profile runs exhibiting each of these critical problems. 

Table 1. Reparable profile runs in the LTPP database

Region

Total

runs

RCOC noted

spikes

Additional

spikes

Total

reparable

North Atlantic 10,011 96 486 582

North Central 14,604 185 970 1,155

Southern 10,693 0 171 171

Western 11,766 22 183 205

Total 47,074 303 1,810 2,113

Table 2. Irreparable profile runs in the LTPP database.

Region Total runs Lost lock Out of study Total irreparable

North Atlantic 10,011 821 100 921

North Central 14,604 382 70 452

Southern 10,693 542 10 552

Western 11,766 83 30 113

Total 47,074 1,828 210 2,038

Table 3. Pending profile runs in the LTPP database.

Region Total runs

Shifted

start

(>10 m)

Shifted

start

(5-10 m)

Miscalibrated

DMI

Wrong

location

Total

pending

North Atlantic 10,011 105 13 0 48 166

North Central 14,604 534 188 455 366 1,543

Southern 10,693 36 7 0 9 52

Western 11,766 287 558 0 156 1,001

Total 47,074 962 766 455 579 2,762
7

Other Sources of Data Variability

Typically, for a single date, the five profiles from a wheelpath overlaid well for all regions. Poor

profile repeatability was noted in about 10 and 30 percent of the GPS and SPS section visit

profiles, respectively. It was defined as a difference in elevation for repeated runs of more than 3

mm over at least 30 percent of the test section length. Differences in the location of the

measured wheelpath and equipment limitations can contribute to this vertical variability. 

Overall, it was identified in about 22 percent of the IMS section visit profiles. However, the

variability seems to have had very little effect on IRI values and was not included in the profile

problems slated for repair or deletion.

A significant amount of the noted vertical profile variations occurred in profiles collected using

the new T-6600 Profilometer. Visual review of profiles collected using the 690DNC and the 

T-6600 indicate that good profile repeatability is more commonly obtained using the old

690DNC system. 

Although not a profile problem, unreported rehabilitation noted in the review was confirmed with

RCOC’s in an effort to update IMS construction information. Only 115 profile runs (0.2 percent)

were identified as being on a rehabilitated pavement for which the IMS construction number had

not been incremented. 

Summary

In summary, about 14.6 percent of the IMS profile run data exhibit equipment- or

operator-related problems. Many of these profiles could be adequately repaired and replaced. 

The status of others is pending, requiring additional review of the available profile archives. 

About 4.3 percent of the IMS profile runs exhibit irreparable effects of equipment or operator

problems, most notably lost lock. Table 4 categorizes these profile problems.

Table 4. Status of runs with equipment/operator problems.

Region All problems, % Repairable, % Irreparable, % Pending, %

North Atlantic 16.6 5.8 9.2 1.7

North Central 21.4 7.9 3.1 10.6

Southern 7.2 1.6 5.2 0.5

Western 11.1 1.7 1.0 8.5

All regions 14.6 4.5 4.3 5.9

Many of these questionable data were not easily identified using the quality control software and

methods available at the time of collection. There was no ability to overlay profiles from a single

date, nor was there an option to overlay profiles from different visit dates. Without these

options, regional engineers had a limited ability to identify lost lock, spikes, early starts, and

different profiles. As a result, this review is, in essence, a supplemental quality review rather

than a judgment of regional data collection operations.
8
9

CHAPTER 3. PROFILE DATA QUALITY UPGRADE

To repair, replace, or delete the identified problem profile runs, the Action Plan summarized in

Appendix B was developed. This plan defines criteria for acceptance and rejection of profile

data. It also details the steps necessary for addressing profiles that fall within the repairable,

irreparable, and pending categories. Table 5 provides an overview of the quality upgrade process

and criteria. The methods used and the status of this upgrade are described in this chapter.

Table 5. Categorization of profile problem runs.

Profile problem Repairable Irreparable Pending

Saturation spikes All runs

Lost lock All runs

Shifted profile (>10 m) Replacement data

available

Replacement data

unavailable

Replacement

unknown

Shifted profile (5-10 m) All runs

Miscalibrated DMI (>10 m) All runs

Miscalibrated DMI (5-10 m) All runs

Wrong location Replacement data

available

Replacement data

unavailable

Replacement

unknown

Unnoted rehabilitation All runs

Repairable Profiles

Profiles that contain saturation spikes can be repaired using the ProQual Version 2.08a software

used in current LTPP profile data processing. This software allows the user to eliminate the

saturation spikes from the profiles during computation of smoothness indices. As a result,

computed IRI values from these reprocessed profiles can be very accurate, since only one or two

data points are eliminated from the profile. 

Profiles that do not begin at the correct location can also be reprocessed, using the ProQual

software, when replacement data are available. Initially, all profile runs shifted more than 10 m

were deleted from the IMS and reloaded if reprocessed profiles collected in the correct location

were available. If profiles shifted 5 to 10 m had no available replacement data, or if the DMI was

miscalibrated 5 to 10 m, the IMS profile data were extracted, comments were added, the

Regional Coordination Office (RCO) quality code was reduced to 2, and the IMS profile data

were reloaded. 

Profiles collected at an unknown, wrong location were repaired if correct replacement data were

available. For this evaluation, all such profile runs will be deleted from the analysis data set.

Unreported rehabilitation is not related to problems with the profile collection operations. 

However, since its identification was facilitated by the visual review of profiles, it is reported in

this evaluation. Repair of this problem requires that the RCO obtain the necessary information
10

and forms from a State or Province to incorporate the maintenance/rehabilitation informationinto

the IMS database. 

All profiles listed in Appendix E containing saturation spikes have been extracted from the IMS

database and reprocessed without the spikes. The Profile Extractor software, described in

Appendix D, was used to extract profile data and convert them to a form that can be used by the

ProQual Version 2.08a software. Replacement IMS upload files, archive files, and appropriate

paper files have been compiled for distribution to the RCOC’s for IMS upload.

Lists of shifted profile sections, those with miscalibrated DMI values, and profiles collected in an

unknown location were submitted to the Federal Highway Administration (FHWA) on February

27, 1999, for distribution to RCOC’s. RCOC’s were asked to replace all saturation spike profile

data and, if possible, to review and replace all shifted or unknown location profiles. The

RCOC’s have replaced profile data for many of the shifted profiles. Profiles with miscalibrated

DMI values for the North Central Region have been extracted and appropriate comments added. 

Irreparable Profiles

Because of the loss or distortion of profile data input that accompanies lost lock, most such

profiles are irreparable. It is possible that profiles that include rapidly intermittent lost lock

could be filtered to remove those data with only a small effect on IRI. However, such efforts are

not tested or have not proven to be reliable. The option of reducing the quality rating of profiles

exhibiting lost lock was considered and dismissed, and in the interest of preserving database

integrity, all profiles exhibiting lost lock effects have been deleted from the IMS database. 

Profiles that were collected in the wrong location and profiles shifted more than 10 m, for which

accurate replacement data are unavailable, are also considered irreparable. Currently, only a few

such data sets have been identified, and they have been deleted from the IMS database. 

Irreparable profiles in the IMS database and regional databases were deleted in stages. Initially,

all profile runs exhibiting lost lock and runs that were collected in the wrong location with no

available replacement data were deleted. Lists of confirmed irreparable profile runs were

distributed to RCOC’s in early March 1999 for deletion from the Regional Information

Management System (RIMS) database. As RCOC’s identified additional profile runs for which

replacement data were unavailable, these were also deleted. 

Pending Profiles

Although shifted profiles and profiles tested in the wrong location have been identified under this

research, RCOC’s were required to search their archive files to determine if accurate data were

available to replace these runs. For many GPS and all SPS sections, profiles for an entire test

site (which includes several test sections) were collected. In addition to the test section profiles,

regional archives contain profile data collected between test sections. This sometimes contained

replacement profile information for the true test site location that could be resectioned and

reprocessed to replace shifted profile data with profiles from the correct location. 
11

0

10

20

30

40

50

60

70

80

90

100

0 to 2

(LWP)

0 to 2

(RWP)

0 to 2

(BWP)

2 to 5

(LWP)

2 to 5

(RWP)

2 to 5

(BWP)

> 5

(LWP)

> 5

(RWP)

> 5

(BWP)

IRI coefficient of variation, %

Percentage of LTPP test sections

Before Upgrade After Upgrade

All profiles shifted more than 10 m were deleted from the IMS. If replacement data were

prepared by the RCOC’s for shifted profiles, they were reloaded into the IMS as they became

available. Runs with profiles shifted between 5 and 10 m, where replacement data were

unavailable, were extracted from the IMS, a comment was added, and the RCO quality code was

reduced to 2. Again, lists of pending profile runs were sent to the RCOC’s for review, and

response and replacement were completed by September 1999.

Effect of Profile Data Upgrade

Currently, the data upgrade has consisted of reprocessing and reloading to the IMS database of

2,128 runs containing equipment-related spikes and deletion of 1,110 runs with lost lock, tested

in the wrong location, or shifted more than 10 m. The effect of this upgrade on data quality can

be quantified by comparing the data variability and the data trends before and after the upgrade. 

IRI Variability

LTPP profiles are typically collected in at least five consecutive runs. The Profilometers

measure surface elevations and compute IRI for the left wheelpath (LWP) and right wheelpath

(RWP). An IRI for both wheelpaths (BWP) is computed as the average of the two wheelpaths. 

BWP IRI standard deviations of the five runs in the IMS database following the upgrade

decreased 7.6 percent overall. It was assumed that reduced variability resulted from elimination

of the large IRI variations associated with saturation spikes and lost lock. The coefficient of

variation (COV), or percentage ratio of standard deviation to mean, also improved. A COV of 2

percent is generally considered representative of good repeatable data. The number of sections

with COV values greater than 2 percent decreased by 2.8 percent with a corresponding increase

in sections with COV values less than 2 percent. Figure 1 shows this effect for each wheelpath. 

Figure 1. Effect of upgrade on IRI variability.
12

0

10

20

30

40

50

60

70

80

90

100

< 0

(LWP)

< 0

(RWP)

< 0

(BWP)

0 - 10

(LWP)

0 - 10

(RWP)

0 - 10

(BWP)

> 10

(LWP)

> 10

(RWP)

> 10

(BWP)

IRI slope for each wheelpath, m/km/yr

Percentage of LTPP test sections

Before Upgrade After Upgrade

IRI Slope

The slope over time of the mean IRI for each wheelpath of unrehabilitated sections was also used

to identify questionable IRI values and to quantify the effect of the data upgrade. The IRI slope

is defined as the rate of change in average IRI across all visits. A steadily increasing rate is

expected as pavement deterioration progresses. Seasonal, daily, and other testing variability can

result in this rate, or change in IRI over time, being zero or less than zero. Testing in the wrong

location, saturation spikes, lost lock, unreported rehabilitation, and shifted starts can also

significantly change the slope, making analysis of the performance trends difficult. Prior to the

data upgrade, 21.7 percent of the unrehabilitated sections having two or more visits exhibited

average IRI slopes of less than zero. Following the upgrade, the improvement resulting from the

data upgrade is evident, as only 15.8 percent of the sections have average IRI slopes of less than

zero (see figure 2). Summaries of IRI data slopes for each region are provided in table 6. The

North Atlantic Region exhibited the greatest percentage of sections with slopes of less than zero,

and the Western Region has the least. 

Table 6. Effect of upgrade on average IRI slope values.

Region

Percentage of sections with an IRI slope < 0

Before upgrade After upgrade

 North Atlantic 25.1 16.7

 North Central 21.1 15.6

 Southern 23.1 15.2

 Western 18.5 15.9

Figure 2. Effect of upgrade on IRI slopes.
13

CHAPTER 4. RUN-TO-RUN VARIABILITY

One main objective of this study is to identify confidence limits for IRI variability between

repeated runs using Profilometer data from the LTPP database. To develop these limits, the

variability of the IRI data in the database was defined. In addition, the effects of pavement type,

equipment, pavement roughness, and other variables were studied. Using this variability

information, effective IRI confidence limits were developed. 

IRI variability was determined in three steps. First, outliers were identified using run-to-run IRI

comparisons, visit-to-visit IRI trends, and visual review of profile data. If the causes of these

outliers were anything other than pavement variability, they were removed from the data set. 

Next, using the upgraded IRI data, the run-to-run variability was identified using statistical

analysis methods, data processing analysis, and field testing. Finally, confidence limits for

expected variation in future profile data collection were developed. Each of these steps is

described in this chapter, along with a summary of general statistics.

General Statistical Summary

The general statistical results of the variability analysis provide valuable information. COV

values from visits with two or more runs in the updated data set for COV ranged from 0.0 to 32.3

percent, with mean left, right, and average IRI COV’s of 2.3, 2.2, and 1.5 percent, respectively &

indicative of generally good repeatable measurements. Figure 3 indicates the distribution of

COV’s for each wheelpath. Because the IRI from both wheelpaths is the average of the left and

right wheelpath IRI’s, variability is much less for the "both wheelpaths" values. 

Shown in figure 4 are cumulative summaries of these COV values. About 60 percent of the left

and right wheelpath IRI COV’s are less than 2 percent, and more than 80 percent of the COV’s

for the IRI average of the two wheelpaths are less than the 2 percent currently recommended as a

quality check in LTPP Directive P-6. Both wheelpath IRI COV values fall below 3.5 percent in

95 percent of the database. At least 95 percent of the left wheelpath IRI COV values are less

than 5.5 percent, and about 95 percent of the right wheelpath IRI COV values are less than 5.2

percent. Figure 4 shows slightly more variation in the IRI values from the right wheelpath. This

may merely illustrate the tendency of roadways to deteriorate more rapidly near the outside

shoulders.

Some regional differences in run-to-run, single-visit IRI variability in terms of COV can be seen

in figure 5. The North Atlantic, North Central, and Southern Region IRI data exhibit very

similar ranges of COV values, with more than 80 percent of average IRI COV values being less

than 2 percent. However, additional variability in the Western Region has kept the percentage of

BWP IRI COV values that are less than 2 percent to only 66 percent of the data. This has

resulted in more section visit data exhibiting COV values greater than 2 percent. 

Visual review of the majority of the run-to-run profiles from the Western Region indicated more

vertical variation, or differences in profile traces of consecutive runs. As a result, the profile runs

from a single date do not typically overlay each other as well as in other regions, with a
14

0

5

10

15

20

25

30

35

40

45

50

1 2 3 4 5 6 7 8 9 10 >10

IRI coefficient of variation, %

Percentage of LTPP test sections

Left Wheelpath Right Wheelpath Both Wheelpaths

0

10

20

30

40

50

60

70

80

90

100

0 1 2 3 4 5 6 7 8 9 10 11 12

IRI coefficient of variation, %

Cumulative percentage of LTPP test sections

Left Wheelpath

Right Wheelpath

Both Wheelpaths

Figure 3. Histogram of IRI coefficients of variation.

Figure 4. Cumulative IRI COVs for LTPP database.
15

0

10

20

30

40

50

60

70

80

90

100

0 to 2

(LWP)

0 to 2

(RWP)

0 to 2

(BWP)

2 to 5

(LWP)

2 to 5

(RWP)

2 to 5

(BWP)

> 5

(LWP)

> 5

(RWP)

> 5

(BWP)

IRI coefficient of variation, %

Percentage of LTPP test sections

North Atlantic North Central Southern Western

Figure 5. Regional run-to-run variability.

subsequent increase in run-to-run IRI variability. This vertical variability is sometimes attributed

to a large amount of transverse pavement profile variability, variations in the collection

wheelpaths, or profile collection equipment problems. Because the variability appears to be

unrelated to the operator, and since the average roughness of the Western Region pavement

sections is not different from other regions, it is possible that slight Profilometer equipment

problems led to the additional run-to-run variability. 

Also, profile runs for the SPS sections in the Western Region are shifted longitudinally from

each other much more frequently than profiles from other regions. This shifting is reportedly

attributable to problems with the Profilometer DMI that the Western Region Contractor worked

at repairing throughout much of the data collection period.

Design of Analysis

To identify confidence limits for the collection of LTPP IRI data, a clean data set was prepared. 

The upgraded IRI database used in this analysis was modified from the original 1997 IMS

database by deleting all runs exhibiting saturation spikes and replacing them with reprocessed

data files. Also, all runs affected by lost lock, tested in the wrong location, or shifted more than

10 m from the true start location were excluded. No replacement data were available at the time

of analysis for shifted runs that had been reprocessed in the correct location. 

Observed interactions between roughness level and IRI standard deviation were then eliminated

through data transformation, and the data were converted to a normal form. Analysis of variance
16

was conducted using 10 fixed variables and 3 random effects. Several significant effects of these

variables were observed and quantified using these models. 

Data Transformation

Plots generated for the mean IRI versus the run-to-run standard deviation indicate that, as mean

IRI increased, the standard deviation increased. This trend is shown in figure 6 for the GPS data. 

An average increase in standard deviation of 0.008 m/km for each 1 m/km change in IRI was

noted for the GPS data, and an average increase of 0.013 m/km was observed for the SPS data. 

The increased variation at SPS sites is probably due to the difficulty encountered in maintaining

the correct wheelpath on rougher sections over the longer distances required for SPS data

collection. 

Profilometer operators have reported this trend and its effect on quality control operations for

many years. The past and current LTPP Manual for Profile Measurements(1-3) require

Profilometer operators to more closely check profiles when the COV is greater than 2 percent. 

As a result of this trend, the COV was used instead of standard deviation (SD) for quality control

(QC) for run-to-run IRI data. COV values exceed the 2 percent QC limit more frequently for

smooth pavements than for rougher pavements. This can result in excessive review of smooth

pavement profiles and possible insufficient review of rough pavement profiles. 

To provide Profilometer operators with a QC statistic that is not affected by pavement roughness

level, transform equations were developed. The IRI data were transformed using Box-Cox

transformations with different powers for left, right, and average wheelpath data on GPS and SPS

sections.

Iterative analysis was used to determine the powers 8a, 8l

, and 8r such that the slope of the

regression of the mean and SD of the transformed variables was as close to zero as possible. 

Variables used in the analysis were the transformed average wheelpath IRI (tr_IRI_a), the

transformed left wheelpath IRI (tr_IRI_l), and the transformed right wheelpath IRI (tr_IRI_r). 

Powers computed for the transformations are:

GPS: 8a= 0.38075; 8l = 0.24665; 8r = 0.2998

SPS: 8a= 0.11535; 8l

 = 0.08975; 8r = 0.08715

Each of the variables was transformed according to the following equations:

tr IRI a

IRI

geoIRI

a

a

a

_ _ ( ) =

−

−

λ

λ λ

1

1 (1)

tr IRI l

IRI

geoIRI

l

l

l

_ _ ( ) =

−

−

λ

λ λ

1

1 (2)
17

0.00

0.05

0.10

0.15

0.20

0.25

0.30

0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5

Average IRI, m/km

Standard deviation, m/km

Figure 6. Relationship of IRI with standard deviation of IRI.

tr IRI r

IRI

geoIRI

r

r

r

_ _ ( ) =

−

−

λ

λ λ

1

1 (3)

Where geoIRI is the geometric mean of the IRIs and N is the number of observations:

geoIRI e

IRI

N =

∑ log

(4)

Even though the section type and wheelpath standard deviations are calculated from different

Box-Cox transformations, these are designed to keep the resulting variables comparable. The

results are typified by the relationship for average IRI of GPS sections shown in figure 7. The

slope of the linear relationship for these data is 0.00005, showing no effect on standard deviation

from the roughness level.

Although these transformed data can be used for quality control, histograms of the standard

deviations of the transformed IRI values indicated a significant skew in the data. To allow for

proper analysis of variance, the transformed IRI standard deviation data were modified with a

natural log (ln) function to achieve a normal relationship between frequency and ln of the
18

0.00

0.05

0.10

0.15

0.20

0.25

0.30

0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5

Average IRI, m/km

Standard deviation of transformed IRI

Figure 7. Transformed GPS IRI versus standard deviation.

transformed IRI standard deviation. A progression of histograms for the original, transformed,

and normalized standard deviation data is shown in figures 8, 9, and 10, respectively. These

normalized standard deviation data were used in the analysis of the run-to-run and visit-to-visit

IRI data for this variability review.

Run-to-Run Analysis Models

The analysis of the normalized transformed IRI standard deviation data from the upgraded data

set was conducted using the SAS PROC mixed utility. Random variables used in the analysis

included the State, Strategic Highway Research Program (SHRP) ID, and profiler driver. Fixed

variables initially included in the analysis are:

 Pavement type (AC [asphalt concrete], PC [portland cement concrete], APC [AC overlay of

PC]).

 Pavement structure (AC, AC/AC, AC/PC, PC, PC/PC).

 Testing time (3 a.m. to 8 a.m., 8 a.m. to 6 p.m., and 6 p.m. to 3 a.m.).

 Season (winter, spring, summer, fall).

 Region (North Atlantic, North Central, Southern, Western).

 Equipment type (690DNC, T-6600).

 Start method (photocell, manual pendant).

 Roughness level, m/km (low [0 to 1.11], medium [1.11 to 1.56], high [>1.56]).
19

0

200

400

600

800

1000

1200

0-0.5

0.5-1.0

1.0-1.5

1.5-2.0

2.0-2.5

2.5-3.0

3.0-3.5

3.5-4.04.0-4.54.5-5.0

5.0-5.5

5.5-6.0

6.0-6.5

6.5-7.0

7.0-7.5

More

IRI standard deviation, 0.01 m/km

Frequency

0

200

400

600

800

1000

1200

0-0.5

0.5-1.0

1.0-1.5

1.5-2.0

2.0-2.5

2.5-3.0

3.0-3.5

3.5-4.0

4.0-4.5

4.5-5.0

5.0-5.5

5.5-6.0

6.0-6.5

6.5-7.0

7.0-7.5

More

Transformed IRI standard deviation, x 0.01

Frequency

Figure 8. Histogram of GPS IRI standard deviations.

Figure 9. Histogram of GPS transformed IRI standard deviation.
20

0

100

200

300

400

500

600

700

-2.7-2.6-2.5

-2.4

-2.3-2.2-2.1

-2

-1.9-1.8-1.7-1.6

-1.5-1.4-1.3

-1.2

-1.1

-1

More

Normalized IRI standard deviation

Frequency

Figure 10. GPS histogram of normalized IRI standard deviation.

 Visit number (1 through 21).

 Construction number (1 through 3).

The statistical models were run initially using main effects and two- and three-factor interactions. 

The models were then simplified in stages, where appropriate. The resulting final models

differed for GPS and SPS road segments. Results of the statistical analysis of GPS and SPS

fixed effects are shown in tables 7 and 8, along with the degrees of freedom (DF) and the

probability that the hypothesis of equality is true (Pr > F). The Pr > F values indicate, with 

95-percent confidence, that there are significant differences in the first four GPS variables and

the first eight SPS variables. 

Run-to-Run Variability Results

The SPS and GPS model results indicate that differences in standard deviation for several

independent variables are significant. In addition, interactions between many of these variables

are significant. These independent variables include the pavement type and equipment types in

GPS sections and the region, pavement structure, start method, and season for SPS sections.

Significant interactions are evident between region and start method, pavement type and

roughness level, region and pavement type, and season and roughness level. (All significance

statements in this report, unless otherwise noted, were developed using the normalized standard

deviation of the IRI for the average of the left and right wheelpaths at a 95-percent confidence

level.) 
21

Table 7. GPS statistical model results.

Main variable Interaction with DF Pr > F

Pavement type 2812 0.0001

Pavement type Roughness level 2812 0.0072

Region Start method 2812 0.0123

Equipment type in each region 2812 0.0340

Region Roughness level 2812 0.1641

Region 57 0.1877

Testing time 2812 0.1899

Construction number 2812 0.3169

Season 2812 0.5047

Roughness level 2812 0.6475

Region Construction number (CN) 2812 0.8189

Start method 2812 0.8557

Table 8. SPS statistical model results.

Main effect Interaction with DF Pr > F

Pavement structure Pavement type * CN 3026 0.0001

Season 3026 0.0003

Region Start method 3026 0.0024

Pavement type Roughness level 3026 0.0061

Region 40 0.0144

Start method 3026 0.0222

Region Pavement type 3026 0.0226

Pavement structure Region * Pavement type 3026 0.0373

Season Roughness level 3026 0.0470

Region Season 3026 0.0496

Region Roughness level 3026 0.0511

Pavement type Season 3026 0.1318

Pavement type 3026 0.1498

Roughness level 3026 0.2750

Equipment type in each region 3026 0.2781

Testing time 3026 0.6428

Effect of Region

For SPS sections, there is a significant difference between regional standard deviations; however,

no significant regional difference was found for GPS sections in the overall model. Summaries

of the GPS and SPS transformed and normalized standard deviations for each region are shown

in figure 11. According to a one-way analysis of variance (ANOVA) with least-squares means, 
22

0.000

0.005

0.010

0.015

0.020

0.025

0.030

0.035

North Atlantic North Central Southern Western

LTPP Region

Standard deviation, m/km

-4.5

-4.4

-4.3

-4.2

-4.1

-4.0

-3.9

-3.8

-3.7

-3.6

-3.5

Normalized standard deviation

GPS (original) SPS (original)

GPS (normalized) SPS (normalized)

Figure 11. Regional variability for GPS and SPS sections.

the variance of Western Region SPS average IRI values is statistically greater than that of the

other regions. The ANOVA is a procedure for using the variation of the components of a set of

data to judge whether differences in the sample means are statistically significant. This increase

in Western Region IRI values is probably the result of the problems experienced with the DMI

on the Western Region’s original profiler. Statistically, the least variability was found in the

North Central and Southern Regions. Other effects and interactions explain the insignificance of

similar trends in the GPS sections.

Effect of Pavement Surface Type

For this analysis, the LTPP pavement sections were divided by surface type into three

categories&AC, APC, and PC. Figure 12 shows the variability associated with these surface

types in each LTPP region. In GPS sections, the variability associated with AC and APC

surfaces is statistically greater than that associated with PC surfaces. Variability for SPS sections

is numerically, but not statistically, greater for the AC sections than for the PC sections. 

The GPS and SPS interactions between pavement type and roughness level are indicative of the

lowest variability in each roughness group occurring in AC pavements. Variances were similar

for APC and PC in the low (0 < IRI < 1.11) and moderate (1.11 < IRI # 1.56) roughness groups,

but were different for the high (IRI > 1.56) roughness group.
23

0.000

0.005

0.010

0.015

0.020

0.025

0.030

0.035

NA (AC)

NA (APC)

NA (PC)

NC (AC)

NC (APC)

NC (PC)

S (AC)

S (APC)

S (PC)

W (AC)

W (APC)

W (PC)

Region (pavement type)

Standard deviation, m/km

-5.0

-4.5

-4.0

-3.5

-3.0

-2.5

-2.0

-1.5

-1.0

-0.5

0.0

Normalized standard deviation

GPS (original) SPS (original)

GPS (normalized) SPS (normalized)

Effect of Pavement Material Structure

Information in the IMS database allows each pavement section to also be grouped by pavement

material structure into five categories&AC, AC/AC, AC/PC, PC, and PC/PC. In both GPS and

SPS sections, no statistical differences in variability were noted for pavement sections in these

categories. The reason is illustrated in figures 13 and 14, which show no clear relationship

between pavement structure and variability. Across regions for both GPS and SPS sections, the

PC structures generally have the lowest IRI variability. Unrehabilitated AC sections have the

greatest run-to-run variability in SPS sections, and AC overlays of PC pavements show the most

GPS variability. 

The SPS interactions noted between pavement material structure and the interaction of region

and pavement type are caused by the close relationship between the pavement surface material

and pavement material structure variables. Its significance, as well as that of pavement material

structure with the interaction of pavement type and construction number, is anticipated, but is not

useful for the analysis.

Effect of Equipment Differences

Each RCOC collected profiles from June 1988 to October 1996 using K.J. Law 690DNC

optical-sensor Profilometers. In the fall of 1996, these profilers were replaced by K.J. Law

T-6600 Profilometers with infrared sensors. The number of GPS and SPS section visits

Figure 12. Variation among regional surface types.
24

0.000

0.005

0.010

0.015

0.020

0.025

0.030

0.035

0.040

0.045

AC AC_AC AC_PCC PCC PCC_PCC 

Pavement structure

Standard deviation, m/km

-5.0

-4.0

-3.0

-2.0

-1.0

0.0

Normalized standard deviation

NA (original) NC (original) 

S (original) W (original)

NA (normalized) NC (normalized)

S (normalized) W (normalized)

0.000

0.005

0.010

0.015

0.020

0.025

0.030

0.035

0.040

AC AC_AC AC_PCC PCC PCC_PCC 

Pavement structure

Standard deviation, m/km

-5.0

-4.0

-3.0

-2.0

-1.0

0.0

Normalized standard deviation

NA (original) NC (original) 

S (original) W (original)

NA (normalized) NC (normalized)

S (normalized) W (normalized)

Figure 13. GPS regional IRI variability for pavement material structures.

Figure 14. SPS regional IRI variability for pavement material structures.
25

0.000

0.005

0.010

0.015

0.020

0.025

0.030

0.035

0.040

AC AC_AC AC_PCC PCC PCC_PCC 

Pavement structure

Standard deviation, m/km

-5.0

-4.0

-3.0

-2.0

-1.0

0.0

Normalized standard deviation

NA (original) NC (original) 

S (original) W (original)

NA (normalized) NC (normalized)

S (normalized) W (normalized)

conducted by each region is shown in table 9. Insufficient data had been collected using the

T-6600 profilers in the North Central and Southern Regions to allow for statistical comparison.

Table 9. Section visits using old and new Profilometers.

Region 690DNC visits T-6600 visits

 North Atlantic 1,640 110

 North Central 2,673 2

 Southern 1,948 8

 Western 2,022 121

Figure 15 shows the IRI variability of each piece of equipment from the four regions. The

Western Region variability associated with the 78 T-6600 visits at SPS sections was significantly

less than that of the 690DNC. This reduction can be related to the improved DMI repeatability

in the new profiler. No difference was noted in the North Atlantic Region between equipment

types based on 36 T-6600 GPS visits and 74 T-6600 SPS visits.

This equipment comparison was not conducted on results from the tests made using each type of

equipment on the same site at the same time. Typically, 1 to 8 years had passed between testing

with the 690DNC and the T-6600. Over this time, transverse pavement variability can change at

a section, disrupting the direct comparison of variability between equipment type. Acceptance

testing of the new T-6600 Profilometers has confirmed the insignificance of the difference in

Figure 15. Variability of regional profilers.
26

0.00

0.01

0.02

0.03

0.04

0.05

NA (Photo)

NA (Manual)

NC (Photo)

NC (Manual)

S (Photo)

S (Manual)

W (Photo)

W (Manual)

Region (start method)

Standard deviation, m/km

-5.5

-4.5

-3.5

-2.5

-1.5

-0.5

GPS (original) SPS (original)

Normalized standard deviation

GPS (normalized) SPS (normalized)

IRI output from the two profilers on the same day at the same site. However, additional study of

this comparison can be made in future analyses using the additional data collected by the T-6600

to further study similarities or differences in IRI variability. 

Effect of Photocell Initiation Method

Both the 690DNC and the T-6600 were fitted with photocells for electronic initiation of data

collection, and manual initiation of data collection is not currently allowed. However, between

1989 and 1991, operators collected 41 GPS and 19 SPS data sets in the North Atlantic (0 GPS, 1

SPS), North Central (29 GPS, 3 SPS), and Western (12 GPS, 15 SPS) Regions while the

photocells were inoperative. Operators manually triggered the start of data collection for those

sections, sometimes resulting in start location differences of more than 6 m between runs. The

effect on IRI variability was significant for both GPS and SPS sections for the Western Region

and for the GPS sections in the North Central Region. 

Figure 16 illustrates these trends. However, because so many more visits were performed using

the photocell, these data sets are quite unbalanced and not appropriate for analysis. Western

Region SPS sections show the greatest increase in variability related to manual initiation, and

GPS sections in the Western Region show the next greatest increase. North Central Region GPS

sections show the only other significant variability increase resulting from manual profile

initiation. Trends for SPS sections in the North Atlantic and North Central Regions are invalid

because only one visit was collected manually in the North Atlantic Region and three were

collected in the North Central Region.

Figure 16. IRI variability from equipment start method.
27

0.000

0.005

0.010

0.015

0.020

0.025

0.030

0.035

0.040

NA (W)

NA (Sp)

NA (Su)

NA (F)

NC (W)

NC (Sp)

NC (Su)

NC (F)

S (W)

S (Sp)

S (Su)

S (F)

W (W)

W (Sp)

W (Su)

W (F)

Region (season)

Standard deviation, m/km

-5.0

-4.5

-4.0

-3.5

-3.0

-2.5

-2.0

-1.5

-1.0

-0.5

0.0

Normalized standard deviation

GPS (original) SPS (original)

GPS (normalized) SPS (normalized)

Effect of Season

Especially in freezing and moist climates, the season of testing could have an important effect on

variability because of the effects of pavement swelling and frost heave. Analysis of the seasonal

effect was completed by dividing the year into four 3-month seasons. Winter included

December, January, and February. Spring included March, April, and May, and so on.

Shown in figure 17 is a summary of the regional variability within each season. Statistically,

there is no seasonal difference for GPS and SPS sections in the Southern Region. This is

probably the result of not having freezing effects on pavements and base materials. The North

Central Region exhibited significant differences between the spring season and the remaining

seasons for SPS IRI variability. Spring provided statistically more SPS variability in the North

Atlantic Region, and the winter season was more variable than the other seasons for SPS sections

in the Western Region.

Overall, the variability was greater in the spring for sections with the highest level of roughness. 

This trend is possibly the result of a greater effect from frost heave and soil swelling on rougher

pavement sections.

Effect of Time of Day

Figures 18 and 19 show the GPS and SPS variability for 10 equal time periods, beginning and

ending at midnight. Although there appears to be more variability in the early morning and

evening hours, this is the result of having large amounts of data in the mid-day fields and very 

Figure 17. Seasonal effect on regional IRI variability. 
28

0.000

0.005

0.010

0.015

0.020

0.025

0.030

0.035

0:00 2:24 4:48 7:12 9:36 12:00 14:24 16:48 19:12 21:36

Military time

Standard deviation, m/km

-5.0

-4.5

-4.0

-3.5

-3.0

-2.5

-2.0

-1.5

-1.0

-0.5

0.0

Normalized standard deviation

NA (original) NC (original) 

S (original) W (original)

NA (normalized) NC (normalized)

S (normalized) W (normalized)

0.000

0.010

0.020

0.030

0.040

0.050

0.060

0:00 2:24 4:48 7:12 9:36 12:00 14:24 16:48 19:12 21:36

Military time

Standard deviation, m/km

-5.0

-4.5

-4.0

-3.5

-3.0

-2.5

-2.0

-1.5

-1.0

-0.5

0.0

Normalized standard deviation

NA (original) NC (original) 

S (original) W (original)

NA (normalized) NC (normalized)

S (normalized) W (normalized)

Figure 18. GPS time series IRI variability.

Figure 19. SPS time series IRI variability.
29

0.000

0.005

0.010

0.015

0.020

0.025

0.030

0.035

NA

(1)

NA

(2)

NA

(3)

NC

(1)

NC

(2)

NC

(3)

S 

(1)

S 

(2)

S 

(3)

W

(1)

W

(2)

W

(3)

Region (roughness level)

Standard deviation, m/km

-5.0

-4.5

-4.0

-3.5

-3.0

-2.5

-2.0

-1.5

-1.0

-0.5

0.0

Normalized standard deviation

GPS (original) SPS (original)

GPS (normalized) SPS (normalized)

little data in the other fields. Analysis of this data was conducted on data from the three time

periods representing different variability & 3:00 a.m. to 8:00 a.m., 8:00 a.m. to 6:00 p.m., 6:00

p.m. to 3:00 a.m. Time of day for testing had no significant effect on the variability associated

with IRI data collection.

Effect of Roughness Level 

To analyze the effect of pavement roughness level on variability, the GPS and SPS sections were

divided into three evenly distributed categories (0 to 1.11, 1.11 to 1.56, and > 1.56 m/km). Since

the IRI means for GPS and SPS sections were transformed to ensure that the level of roughness

did not affect the standard deviation, no significant effect was noted as a result of roughness

level. However, significant interactions were noted with pavement type and season. Also,

significant, with 90-percent confidence, was an SPS interaction of roughness level with region. 

The reason for this last interaction is shown in figure 20. Variability was greatest in the high

roughness level for the North Atlantic and Western Regions, but variability was least for the

highest roughness level in the Southern Region. 

The SPS interaction of roughness level with pavement type is significant in that variability is

highest for the AC sections at the highest roughness level. However, at the lowest roughness

level, variability is least for the AC sections. Interaction between roughness level and season

was noted because the SPS variability was greater in the spring for the highest roughness level,

but least in the spring for the lowest roughness level.

Figure 20. Regional IRI variability across roughness level.
30

Quality Control Implications 

Currently, Profilometer operators are using a COV of 2.0 percent or greater from run-to-run as a

flag for possible data problems. Operators are asked to review any profile data more closely if

the limit is exceeded. This analysis has defined the run-to-run variability for SPS and GPS

sections in the IMS database. An application of that information is in defining levels of

variability that are within a normal limit. 

Since the standard deviation of IRI data is affected by the mean IRI level, the required current

practice of using a COV tends to flag very smooth pavements too frequently, and it is less

stringent on rougher pavements. This analysis used a Box-Cox transform to eliminate the effect

of the IRI level on standard deviation. Thus, it is possible to define a standard deviation limit

that is normal across all roughness levels within the program. 

Box-Cox IRI transforms can be approximated for GPS sections using the equations (r2

 = 0.999):

Left or Right Wheelpaths: IRIT = 0.0463 IRI3 % 0.4472 IRI2

 + 1.9819 IRI % 1.5899 (5)

Average Wheelpath: IRIT = 0.0463 IRI3 % 0.4220 IRI2 + 1.8972 IRI % 1.5272 (6)

For SPS sections, the transformed IRI values can be estimated using the equations (r2

 = 0.999):

Left or Right Wheelpaths: IRIT = 0.0559 IRI3 % 0.5515 IRI2

 + 2.2047 IRI % 1.7238 (7)

Average Wheelpath: IRIT = 0.0570 IRI3 % 0.5488 IRI2 + 2.2011 IRI % 1.7209 (8)

For each of these wheelpaths and section types, the 75-, 90-, 95-, and 99-percent confidence

limits on transformed IRI standard deviations are shown in table 10. Estimated untransformed

standard deviations are also shown in table 10; however, the linear relationship used in this

estimate has an r2 of 0.898. To simplify the quality checks, operators can be instructed to check

all data more closely if the standard deviation of the transformed IRI data exceeds the

recommended confidence limits. Figures 21 and 22 show the standard deviation of transformed

GPS and SPS IRI data and several typical confidence limits. A confidence limit of 90 percent is

recommended. This limit corresponds to an estimated untransformed COV for GPS sections of

2.2 percent and for SPS sections of 3.0 percent. These limits are based on the actual variability

within the LTPP profile database, and they are not affected by the level of section roughness.

Table 10. Confidence limits for transformed average IRI standard deviations.

GPS confidence limits SPS confidence limits

Transformed SD Estimated SD Transformed SD Estimated SD

Mean 0.017 0.018 0.021 0.022

75% level 0.022 0.022 0.026 0.027

90% level 0.032 0.033 0.040 0.042

95% level 0.040 0.042 0.053 0.058

99% level 0.061 0.068 0.087 0.101
31

0.00

0.02

0.04

0.06

0.08

0.10

0.12

0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5

Average IRI, m/km

Standard deviation of transformed IRI

99% Limit

95% Limit

90% Limit

Mean

0.00

0.02

0.04

0.06

0.08

0.10

0.12

0.14

0.16

0.18

0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0

Average IRI, m/km

Standard deviation of transformed IRI

99% Limit

95% Limit

90% Limit

Mean

Figure 21. Confidence limits for GPS transformed IRI standard deviation.

Figure 22. Confidence limits for SPS transformed IRI standard deviation.
32

Summary of Run-to-Run Analysis

Analysis of run-to-run variability was completed on IRI data collected between 1989 and 1997. 

IRI values were recomputed for runs that contained equipment-related spikes. Also, profiles that

exhibited lost lock or that were shifted more than 10 m were excluded from the data set. The

revised data set was transformed to eliminate the interaction between standard deviation and IRI

level. In addition, the transformed IRI data were normalized using a natural log function to

improve analysis accuracy. 

Using main effects and two- and three-factor interactions with ANOVA analysis of the average

IRI values, several key findings were identified:

 The average wheelpath run-to-run IRI variances for SPS data collected in the Western Region

are significantly greater than in the other regions. This is probably related to the DMI

problems reported by that region. 

 Variances for the IRI of the GPS sections are not significantly different between regions. 

SPS data collected in the Southern and North Central Regions have the least variability. The

variability associated with GPS AC and APC sections is significantly greater than that of PC

sections. This may be because rutting in the wheelpaths of AC and APC sections causes

differences in longitudinal profiles measured a few centimeters apart.

 SPS sections exhibit no significant variability difference between AC, APC, and PC sections,

although more variability is found in the AC sections than in the PC sections. 

 SPS and GPS variability of the 690DNC and the T-6600 Profilometers in the North Atlantic

Region is not significantly different. The T-6600 variability in the Western Region is

significantly less than that of the 690DNC. However, less than 6 percent of the data from

that region was collected using the T-6600. Insufficient data are available to compare the

variability in the North Central and Southern Regions’ Profilometers. 

 Manual triggering of data collection significantly increases the run-to-run IRI variability in

all regions where a sufficient amount of data is available for analysis.

 Overall run-to-run variability is greatest in the spring for SPS sections in the North Central

and North Atlantic Regions. This is possibly a result of the effect of additional moisture or

thawing. Variability for SPS sections in the Western Region is statistically greatest in the

winter. 

 The time of day had no effect on variability for GPS or SPS sections. 

 Run-to-run variation in IRI for a typical section is less than 2 percent COV. This represents

very good test repeatability.

 A 90-percent confidence limit on the transformed IRI standard deviation is recommended for

field quality control on the IRI average of both wheelpaths. For GPS sections, this

corresponds to a transformed standard deviation value of 0.32; for SPS sections, the value is

0.40.
33

CHAPTER 5. VISIT-TO-VISIT VARIABILITY

Visit-to-visit IRI variability is associated with the effects of collecting profiles over time, while

run-to-run variability is associated with the five profiles collected during a single site visit. Over

time, the effects of daily temperature changes, seasonal changes, pavement deterioration, and

other factors add additional variability to the run-to-run variability. One objective of the analysis

was to define a time or roughness level inflection point where rapidly increasing pavement

deterioration makes it necessary to increase the data collection frequency. The two main

objectives of the visit-to-visit variability study were to define the effects of the above-stated

factors and to identify confidence limits for expected change in average IRI between profile

collection visits. 

Inflection Point Analysis

To achieve the first objective, three approaches were taken. First, the rate at which the IRI

increases with the time since construction or last rehabilitation was studied for various surface

types and regions. In addition, the research team attempted to identify an inflection point in the

IRI time history where the rate of yearly change significantly increases. Following on this

approach, an inflection point associated with the level of roughness where more rapid IRI

increases occurred was also sought. Each of these approaches failed because of the lack of an

apparent inflection point or general trend toward more rapid IRI increases at higher roughness

levels or pavement ages. This result indicated that, in general, the LTPP IRI data change linearly

with time, inhibiting the identification of inflection point trends. However, this linear

relationship helped to make it possible to achieve the two main objectives.

Time to Rapid IRI Increase

The objective of the first inflection point approach was to identify a pavement age associated

with a rapid increase in the rate of IRI changes. This rate increase is important because it could

indicate impending pavement failure and could be used to drive data collection scheduling or

repair planning. Initially, visual analysis of the IRI data indicated that only about 99 (5 percent)

of the 2,035 sections in the upgraded data set show any specific positive inflection point in the

IRI time-history trend. In reality, it is debatable whether several of the sections included in this

list show true inflection points. When the times between last construction or rehabilitation and

these inflection points were compared, the range in time from initial reconstruction to the

inflection points for each pavement type was 7 to 25 years. This is shown in figure 23. The

same large range is seen in the regional data of figure 24, where the range in time from

construction or last rehabilitation to the estimated inflection points was 12 to 25 years. 

Even with this reduced data set, the variability in the time to rapid IRI increases is still extremely

large. Thus, it is not possible to determine well-defined guidelines to trigger increasing site visit

frequency or pavement maintenance based on time from last construction or rehabilitation.
34

0

5

10

15

20

25

30

35

40

45

50

0 5 10 15 20 25 30

Time to IRI inflection point, years

Percent Frequency

All Pavements

AC

PC

AC/PC

Figure 23. Histogram of IRI inflection point time by surface type.

Figure 24. Histogram of IRI inflection point time by region.

0

5

10

15

20

25

30

35

40

45

50

0 5 10 15 20 25 30

Time to IRI inflection point, years

Percent Frequency

All Regions

North Atlantic Region

North Central Region

Southern Region

Western Region
35

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

4.5

1/1/89 1/1/91 12/31/92 12/31/94 12/30/96 12/30/98

IRI, m/km

IRI Time-History Slopes

Another attempted approach to finding conditions where increased testing frequency may be

necessary was to identify specific overall differences in the IRI versus time slope before and after

any perceived inflection point. Initially, this procedure was run on the 99 selected test sections

with promising results. However, further statistical review of all sections indicated that their use

was inappropriate because of the lack of statistically identifiable inflection points in the most

promising data.

In the statistical review, only sites having more than three visits within a construction period and

only the greatest 16 percent of IRI versus time slopes from simple linear regression were used. 

The response variable was the mean of the transformed IRI values. Then a quadratic polynomial

was fitted for each of the identified 187 sites. Those with fewer than 4 visits prior to

rehabilitation were omitted, cutting the number of sites to 116. Trends for these sections are

shown in figures 25 and 26. There is obviously an increasing trend for IRI with time for most of

these sections. However, there is little indication of inflection points where the rate of IRI

change increases rapidly. Of these equations, 48 had negative coefficients for the second power

term and 59 equations had negative coefficients for the first power term. Negative coefficients

indicate a decreasing rate of change, rather than a more rapid increase.

Because of the lack of a trend indicating that the IRI increased more rapidly following a certain

time in a pavement’s life, this approach was abandoned. However, it is possible that such a trend

does exist in pavement performance, but that the sections included in the study have not reached

a deterioration level that would allow it to be identified. Another possible reason for the lack of

Figure 25. IRI trends for 60 selected GPS sections.
36

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

1/1/89 1/1/91 12/31/92 12/31/94 12/30/96 12/30/98

IRI, m/km

Figure 26. IRI trends for 56 SPS sections.

inflection points is that maintenance efforts have reduced the rapid increase sometimes

associated with increased pavement deterioration. 

IRI Roughness Level Slopes

The third approach used to identify trigger values for increased testing frequency was to find an

IRI roughness level where the yearly change in IRI is significantly greater than that of lower IRI

levels. This is based on the assumption that pavements deteriorate more rapidly when they get

rougher.(4) To begin this analysis, the yearly change in IRI was computed using all adjacent

average IRI values for sections that had more than one visit within a construction period. Figure

27 shows the resulting relationship. Because of the significant effect of short site visit intervals

on an IRI rate of change, section visits that were tested less than 6 months apart were not

included in the analysis. Also, 16 visits where the projected IRI was less than 0.5 m/km were

eliminated because of the unreasonableness of the data. The data show a linear trend with a

slope of 5.8 percent per year and a correlation coefficient (r2) of 0.07. 

These results do not indicate a significant increase in IRI at a certain level of roughness,

rendering this third approach inappropriate. Although inflection points were not apparent, the

linear trend between IRI level and yearly change in IRI simplified the identification of

between-visit confidence limits. This trend, and the lack of change between regions and

experiment types, allows for the development of confidence intervals for quality control.
37

-1.0

-0.8

-0.6

-0.4

-0.2

0.0

0.2

0.4

0.6

0.8

1.0

0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0

Average BWP IRI, m/km

IRI change from previous visit, m/km/yr

Figure 27. Change in IRI between visits.

Between-Visit Variability

Differentiating the variability associated with factors such as seasonal effects and daily

temperature changes is difficult when the variability associated with wheelpath location within

the data is unknown. However, seasonal and daily change effects can be isolated somewhat and

evaluated within the 66 LTPP seasonal sections. These PC and AC sections were tested three or

four times each year between 1992 and 1997. In many cases, the PC sections were also tested in

the early morning, mid-morning, and late afternoon to identify the effects of curling on

roughness. IRI values from the five runs at each of these seasonal visits provided a good

estimate of these effects.

Seasonal Effects

Sufficient data was available from the 97 PC seasonal site visits and the 170 AC visits to provide

19 PC and 44 AC segments of 3 or 4 seasons within a year’s time. Fifty-one percent of these

seasonal groupings include data from four seasons. The average yearly IRI change for these

groupings is 0.03 m/km, indicating that the IRI changes across the 5 to 12 months that these

seasonal groups extended was not greatly related to pavement deterioration. This allowed for

analysis of the effect of seasonal variability on IRI.

Within this subset of the LTPP database, the average IRI standard deviation for each visit was

0.014 m/km. This compares well with the standard deviation of the entire data set shown in
38

0

0.05

0.1

0.15

0.2

0.25

0 10 20 30 40 50 60 70 80 90 100

Percentile

Standard deviation, m/km

3- to 4-Season Variability

1-Visit Variability

0.089

0.014

0.028

0.160

1 SD2 SD

figure 17. About 95 percent of these seasonal sections are from GPS sections and 80 percent of

these sections are from the North Atlantic, North Central, and Southern Regions. The seasonal

standard deviation compares well with the overall standard deviation of 0.013 m/km for GPS

sections in these three regions, indicating that the seasonal data are representative of all data in

this larger data set.

Standard deviations for the collection of the three and four seasonal visits in these yearly data 

sets give an indication of the effect of seasonal changes on expected IRI variability. Shown in

figure 28 are the percentile rankings for the single-season and across-season standard deviations. 

They indicate that about 68 percent of the single-visit standard deviations are less than 0.014

m/km. IRI standard deviations between the seasons are about six times greater (0.089 m/km). 

The conclusion can be drawn that testing within the same season of the year should reduce the

seasonally related variability by up to six times.

Daily Change Effects

FHWA has asked RCOC’s to collect profiles during seasonal PC section data collection two or

three times in a single day&in the early morning, mid-morning, and late afternoon. This is

intended to capture the effect of slab curling on pavement roughness. The variability within a

single time period versus that of an entire day provides insight into the effect of curling on IRI

variability.

Data used in this comparison include IRI values from 12 seasonal GPS PC sites collected on 32

days. Multiple sets of profile testing were completed on all of these days. When the hours were

Figure 28. Seasonal effect on variability.
39

0

0.05

0.1

0.15

0.2

0.25

0 10 20 30 40 50 60 70 80 90 100

Percentile

Standard deviation, m/km

Daily Variability

1-Visit Variability

1 SD2 SD

0.044

0.022

0.096

0.045

divided into three representative time groups (4 a.m. to 10 a.m., 10 a.m. to 3 p.m., and 3 p.m. to

10 p.m.), there remained 20 days in which two time groups were tested and 7 days when three

time groups were profiled. On 5 days, the multiple sets were collected in the same time group. 

If only two groups were tested, one of the groups was the early morning time. 

Standard deviations for the individual visits and the daily IRI values are shown in figure 29. For

GPS PC sections in the entire IMS database, the average standard deviation is 0.017 m/km. In

this seasonal data subset, the standard deviation is 0.019 m/km. The standard deviation for all

runs collected at different times of the day is 0.047, compared to 0.019 m/km for the individual

runs collected at a single time. This indicates that an increase in variability of up to 2.5 times is

added to GPS PC sections when tested at different times of the day. Based on this trend, it is

evident that striving to collect profiles from PC sections at the same time of day will reduce IRI

variability, although not as much as by testing during the same season of the year. 

Between-Visit Confidence Limits

 The linear trend in IRI slope data allows, with additional analysis, the development of

confidence limits for the expected change in average IRI values in consecutive visits. 

Differences in this change for SPS and GPS sections and for regional contractors were not

statistically significant. The mean IRI slopes of the AC, PC, and APC pavement types were

significantly different, requiring that confidence limit models be developed for each pavement

type.

A review of the IRI slope data indicated 124 visits where the negative change in IRI between

visits was greater than 0.180 m/km. These visits (2.4 percent of the data set) are outside the 90-

Figure 29. Daily effect on IRI variability.
40

percent confidence interval for yearly IRI standard deviation in the seasonal data set. Because

this seasonal set includes an entire range of variability between runs, at different times of the day,

and across seasons, it seemed reasonable to exclude these visits from the analysis.

For each pavement type, the IRI slopes within 0.5-m/km intervals from 0.5 to > 3.0 m/km were

compared statistically. The slopes for each pavement type fell into three statistically distinct

grouping intervals (according to the Duncan multiple-comparison procedure), shown in table 11. 

Mean and standard deviation slope values for each interval provide sufficient information to

develop confidence limits for each roughness level. Data points, mean slopes, and confidence

limits for AC, PC, and APC pavements are shown in figures 30, 31, and 32, respectively. These

were developed using a 95-percent confidence level.

Table 11. Statistically different slopes for IRI intervals.

Grouping1 IRI range, m/km

AC AC/PC PC

A 0.5 to 1.5 0.5 to 1.5 0.5 to 1.5

B 1.5 to 2.5 1.5 to 2.5 1.5 to 2.5

C >2.5 >2.5 >2.5

1

Statistically different at 95-percent confidence level.

Formulas that can be used in quality control software or in manual quality checks are listed

below for each pavement type. These models are considered accurate for AC pavements within

the IRI range of 0.5 to 4.0 m/km, for PC between 0.5 and 3.5 m/km, and for APC between 0.5

and 3.0 m/km. Because of the small denominator, they are not recommended for use when the

time between section tests is less than 6 months. 

AC Pavements:

Upper Limit: IRIE = IRIP + )T (0.1984 IRIP % 0.0273) (9)

Lower Limit: IRIE = IRIP + )T (-0.0282 IRIP % 0.0995) (10)

PC Pavements:

Upper Limit: IRIE = IRIP + )T (0.1532 IRIP + 0.0094) (11)

Lower Limit: IRIE = IRIP + )T (-0.1158 IRIP % 0.0686) (12)

AC/PC Pavements:

Upper Limit: IRIE = IRIP + )T (0.3244 IRIP % 0.1538) (13)

Lower Limit: IRIE = IRIP + )T (-0.1158 IRIP % 0.0006) (14)
41

-0.6

-0.4

-0.2

0.0

0.2

0.4

0.6

0.8

1.0

1.2

0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5

Mean IRI, m/km

Change in IRI, m/km/yr

Mean

90% Limit

95% Limit

90% Limit

95% Limit

-0.6

-0.4

-0.2

0.0

0.2

0.4

0.6

0.8

1.0

0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5

Mean IRI, m/km

Change in IRI, m/km/yr

Mean

95% Limit

90% Limit

90% Limit

95% Limit

Figure 30. Confidence limits for AC IRI change.

Figure 31. Confidence limits for PC IRI change.
42

-0.6

-0.4

-0.2

0.0

0.2

0.4

0.6

0.8

1.0

0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5

Mean IRI, m/km

Change in IRI, m/km/yr

95% Limit

95% Limit

90% Limit

90% Limit

Mean

 

Figure 32. Confidence limits for APC IRI change.

Where:

IRIE = Expected IRI, m/km

IRIP = Previous IRI, m/km

)T = Time since previous visit, yrs

In contrast to the ±10 percent limit currently used to check for expected IRI values, these

confidence limits adjust for the different variability and IRI changes associated with different

roughness levels. Between IRI levels of 0.75 and 3.5 m/km, the AC confidence limits

correspond to average allowable yearly reductions of 8.6 percent and allowable increases of 18.2

percent. For PC sections, the average allowable reduction and increase are 10.7 and 15.9

percent, respectively. Finally, the allowable yearly reduction for APC sections is 11.6 percent,

with an allowable increase of 23.5 percent. 

These limits are intended to provide guidelines for future profile data collection. IRI changes

outside the upper limits are not unreasonable; however, changes below the lower limits may

indicate problems in data collection or unreported maintenance or rehabilitation. These

confidence levels should be used as a quality control guide. If limits are exceeded, it is

recommended that the newly collected profiles be reviewed and compared with profiles from

previous visits. Also, the pavement section should be visually surveyed to identify changes in its

condition.
43

Summary of Between-Visit Variability

Through this analysis, researchers have drawn several conclusions that can be applied to the

future collection of profile data. Several of the previously discussed key findings are provided

below:

 Linear trends in the LTPP IRI data currently prohibit the identification of inflection points for

development of incremented collection frequency guidelines.

 IRI standard deviations associated with seasonal effects are about 0.089 m/km. This is about

six times the variability of the single-visit standard deviations for the same data.

 Daily cycling of IRI roughness on PC sections results in an average standard deviation of

0.047 m/km for the PC GPS seasonal sections in the LTPP database. The individual standard

deviation for these sections at each time of day is 0.019 m/km. This indicates that the

increased standard deviation typically associated with daily cycling of PC pavement sections

is up to 0.028 m/km.

 Confidence limits for AC, PC, and APC pavements have been developed using the data from

the LTPP database. These limits can be used as quality control checks for future profile data

collection.
44
45

0

5

10

15

20

25

30

0 10 20 30 40 50 60 70 80

Start spike height, mm

Percent change in IRI, RMSVA, and MO

0

100

200

300

400

500

600

Percent change in Slope Variance

Average IRI

RMSVA 4

RMSVA 16

Mays Output

Slope Variance

CHAPTER 6. ADDITIONAL ANALYSIS RESULTS

In addition to the visual review and statistical analysis, data processing analysis and field testing

have been conducted. Since unreported saturation spikes have been identified on 1,810 test site

profile runs, the effect of unedited saturation spikes on IRI using spikes of various amplitude was

analyzed. 

The shifting of profiles more than 5 m from the true 152.4-m location was also noted on 1,728

test site profile runs. Using PC and AC pavement sections ranging in IRI from 0.85 to 2.34

m/km, the effect on IRI of profile location shifts up to 15 m was also evaluated. 

Variation of wheelpath location was also reviewed. Since no information is available regarding

the effect of transverse vehicle location on IRI, field testing was completed on three sections with

an IRI ranging from 1.18 to 2.87 m/km. The location of the profile sensors was shifted 0.3 m to

the left and right of the wheelpath location, and IRI variability was summarized. The results of

these and other analyses are described in this chapter.

Effect of Saturation Spikes

 The effect of the unreported saturation spikes (noted in profiles from about 5 percent of the

section profile runs) on IRI and other smoothness indices is unknown. To document this effect,

spikes of various amplitudes were inserted at the start of the profile for Illinois section 5217

collected on June 15, 1991. The IRI average for both wheelpaths when no spikes were present

was 2.4 m/km. Figure 33 shows the effect of spikes with amplitudes of 5 to 75 mm.

Figure 33. Effect of spike height on smoothness statistics.
46

-15

-10

-5

0

5

10

15

20

25

30

-20 -15 -10 -5 0 5 10

Shift from true start location, m

Percent difference from true IRI, %

Mean and Standard Deviation 

Range Shown

The relationship between spike amplitude and IRI is nearly linear, with a 1.8-percent increase in

average IRI for every 5-mm increase in spike amplitude. Average effects on slope variance

values are much more dramatic, with a 24-percent increase for every 5-mm spike height increase. 

Other smoothness statistics are less sensitive to the presence and amplitude of spikes. For each

5-mm increase in spike height, the average increases in Root Mean Squared Vertical

Acceleration (RMSVA) with a 1.2-m (4-ft) baselength (RMSVA 4), RMSVA 16 (4.9-m

baselength), and Mays Output were 1.0, 0.5, and 1.0 percent, respectively. 

This evaluation shows that the effects of spikes in the typical 12- to 75-mm range on IRI and

other smoothness statistics can be dramatic, and it reinforces the importance of not including

saturation spikes greater than 5 mm in profile smoothness statistics computation from profiles in

the LTPP database.

Effect of Shifted Profiles

The problem of shifted profile start locations is particularly important given that about 3.6

percent of the LTPP section profile runs are shifted more than 5 m. IRI values ranging from 0.5

to 4.5 m/km were computed for 25 AC and PC SPS sections. The start and end locations of each

profile were shifted backward 15, 12, 9, 6, and 3 m, and the IRI values were recomputed on the

shifted profiles. Comparing the IRI values for the shifted profiles with those of the original

profile, it was noted that the difference from the true IRI value ranged from 0 to 28 percent. 

Figure 34 shows the results of this analysis. The importance of testing a section at the same

location is evident. For profiles shifted 12 m, normalized statistics indicate that about 70 percent

of the shifted IRI values were within 5 percent of the true IRI.

Figure 34. Effect of shifted profile start locations.
47

For profiles shifted 15, 9, 6, and 3 m from the original location, about 70 percent were within

7.0, 3.8, 3.0, and 1.9 percent, respectively. 

However, the average effect on IRI from shifted profiles is negligible because the profile sections

added or deleted from the beginning and end of the true profile location can be smoother or

rougher than the true section. A random selection results in no average IRI change. 

For quality control, the data indicate that profiles shifted up to 3 m can be expected to be within

3.7 percent of the true IRI values in 95 percent of the data. This is well within the effect of

seasons on IRI variability. However, profiles shifted more than 3 m can be expected to greatly

affect the IRI values and should be avoided. 

Effect of Wheelpath Location

No definitive evaluation of the effect of the transverse location (i.e., wheelpath location) of

testing longitudinal profiles has been completed by FHWA. It has been FHWA policy to allow

the LTPP Profilometer operators to visually define the wheelpath location to be tested at each

visit. This has raised questions about the effect on IRI if different operators in different years

measure the profiles in a slightly different wheelpath location. 

To help study this effect, in February 1998, the Texas Department of Transportation assisted in

the location and marking of three AC test sites of different roughness levels. Sites 1 and 2 are

located on westbound SH 71, approximately 31 km east of Interstate 35 in Austin, Texas. The

third site is on Pope Bend South Road, a service road adjacent to sites 1 and 2 in the westbound

direction. Air temperatures were between 22 and 26°C, and the skies were clear to partly cloudy. 

Five repeat runs were made at each site for the center of the wheelpaths and 300 mm left and

right of the center using the Southern Region’s K.J. Law T-6600 Profilometer. 

Figure 35 shows the average of five runs for each site and wheelpath location. Shifting

transverse location left or right 300 mm for smooth, medium, and rough AC pavement sites

resulted in average left and right wheelpath IRI changes of 7.1, 15.3, and 4.0 percent,

respectively. Tables 12, 13, and 14 list the results of ANOVA and Tukey multiple comparison of

average values analysis. The ANOVA indicated that, for each site and wheelpath, there is a

significant difference between one or more of the shifted profile IRI values. P-values listed on

these tables all indicate good statistical results. Mean values for each location are shown in the

tables, along with the Tukey significance-level groupings defining data sets that are significantly

different. Based on the mean and variability of the five runs left, right, and center of the true

wheelpath, these significance-level groupings identify locations where the IRI values are

statistically the same. For example, in the left wheelpath of site 1, there is no significant

difference at the 95-percent confidence level between runs taken at the center of the wheelpath

and 300 mm to the right of center. However, the IRI values measured 300 mm left of center are

significantly greater.