Long-Term Pavement Performance 

Automated Faulting Measurement

PUBLICATION NO. FHWA-HRT-14-092 FEBRUARY 2015

Research, Development, and Technology

Turner-Fairbank Highway Research Center

6300 Georgetown Pike

McLean, VA 22101-2296
FOREWORD

This report documents the development of the Long-Term Pavement Performance (LTPP) 

automated faulting measurement (AFM) algorithm to identify transverse joint locations on 

jointed plain concrete pavements and compute faulting at these locations using the profile data 

collected by LTPP high-speed inertial profilers. The LTPP AFM algorithm is intended to replace 

traditional manual faulting surveys that entail traffic control and significant survey time from 

State departments of transportation and highway agencies. The software program developed 

based on this algorithm will serve as an automated tool for highway engineers to significantly

increase their productivity when detecting transverse joint locations with acceptable accuracy.

Jorge E. Pagán-Ortiz

Director, Office of Infrastructure

 Research and Development

Notice

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

Transportation in the interest of information exchange. The U.S. Government assumes no 

liability for the use of the information contained in this document. This report does not constitute 

a standard, specification, or regulation.

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

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

objective of the document.

Quality Assurance Statement

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

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

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

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ensure continuous quality improvement.
TECHNICAL REPORT DOCUMENTATION PAGE

1. Report No.

FHWA-HRT-14-092

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

4. Title and Subtitle

Long-Term Pavement Performance

Automated Faulting Measurement

5. Report Date 

February 2015

6. Performing Organization Code

7. Author(s) 

Mahesh Agurla and Sean Lin 

8. Performing Organization Report No.

9. Performing Organization Name and Address

Engineering & Software Consultants, Inc. 

14123 Robert Paris Court

Chantilly, VA 20151

10. Work Unit No. (TRAIS)

11. Contract or Grant No.

DTFH61-12-C-00002

12. Sponsoring Agency Name and Address

Office of Infrastructure Research and Development

Federal Highway Administration

6300 Georgetown Pike

McLean, Virginia 22101-2296

13. Type of Report and Period Covered

Research Report

14. Sponsoring Agency Code

15. Supplementary Notes

Contracting Officer’s Representative (COR): Aramis Lopez; Task Manager: Larry Wiser

HRDI LTPP Data Analysis Contract

16. Abstract

This study focused on identifying transverse joint locations on jointed plain concrete pavements using an automated 

joint detection algorithm and computing faulting at these locations using Long-Term Pavement Performance 

(LTPP) Program profile data collected by the program’s high-speed inertial profilers (HSIP). This study evaluated 

two existing American Association of State Highway and Transportation Officials R 36-12 automated faulting 

measurement (AFM) models: ProVAL (Method-A) and Florida Department of Transportation (FDOT) PaveSuite 

(Method-B). A new LTPP AFM was developed using LTPP profile data. The LTPP AFM is an automated 

algorithm to identify joint locations where faulting is also computed for each joint identified to replicate the 

manually collected faulting data using the Georgia Faultmeter (GFM), which has been used on LTPP test sections 

since the program’s inception.

The study compared the LTPP manual faulting measurements collected using the GFM with the ProVAL AFM and 

the LTPP AFM using LTPP profile data. Similarly, the FDOT GFM measurements were compared with the FDOT 

PaveSuite AFM and the LTPP AFM using the same FDOT profile data. The initial results for six LTPP test sections 

show that the LTPP AFM can identify joint locations with a joint detection rate (JDR) ranging from 95 to 

100 percent. ProVAL’s JDR range is from 58 to 99 percent for the same six LTPP test sections. Similarly, for the 

one FDOT test section available, the LTPP AFM’s and FDOT PaveSuite’s JDRs are approximately 96 percent. This 

study outlines the LTPP AFM algorithm, discusses the comparison of the three AFM results, and recommends

future research needs in this area.

17. Key Words

Automated faulting measurement, LTPP high-speed 

inertial profiler data, Georgia Faultmeter, jointed plain 

concrete pavement, joint faulting measurement, 

transverse joint location detection

18. Distribution Statement

No restrictions. This document is available to the public 

through the National Technical Information Service, 

Springfield, VA 22161.

http://www.ntis.gov

19. Security Classification (of this report)

Unclassified

20. Security Classification (of this page)

Unclassified

21. No. of Pages

36

22. Price

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

SI* (MODERN METRIC) CONVERSION FACTORS 

APPROXIMATE CONVERSIONS TO SI UNITS

Symbol When You Know Multiply By To Find Symbol 

LENGTH

in inches 25.4 millimeters mm 

ft feet 0.305 meters m 

yd yards 0.914 meters m 

mi miles 1.61 kilometers km 

AREA 

in2square inches 645.2 square millimeters mm2

ft2 square feet 0.093 square meters m

2

yd2 square yard 0.836 square meters m

2

ac acres 0.405 hectares ha

mi2square miles 2.59 square kilometers km2

VOLUME 

fl oz fluid ounces 29.57 milliliters mL 

gal gallons 3.785 liters L 

ft3 cubic feet 0.028 cubic meters m

3 

yd3 cubic yards 0.765 cubic meters m

3 

NOTE: volumes greater than 1000 L shall be shown in m3

MASS 

oz ounces 28.35 grams g

lb pounds 0.454 kilograms kg

T short tons (2000 lb) 0.907 megagrams (or "metric ton") Mg (or "t") 

TEMPERATURE (exact degrees) 

oF Fahrenheit 5 (F-32)/9 Celsius oC 

or (F-32)/1.8 

ILLUMINATION 

fc foot-candles 10.76 lux lx

fl foot-Lamberts 3.426 candela/m2 cd/m2

FORCE and PRESSURE or STRESS 

lbf poundforce 4.45 newtons N 

lbf/in2poundforce per square inch 6.89 kilopascals kPa 

APPROXIMATE CONVERSIONS FROM SI UNITS 

Symbol When You Know Multiply By To Find Symbol 

LENGTH

mm millimeters 0.039 inches in 

m meters 3.28 feet ft 

m meters 1.09 yards yd 

km kilometers 0.621 miles mi 

AREA 

mm2square millimeters 0.0016 square inches in2 

m

2

square meters 10.764 square feet ft2 

m

2

square meters 1.195 square yards yd2 

ha hectares 2.47 acres ac

km2 square kilometers 0.386 square miles mi2 

VOLUME 

mL milliliters 0.034 fluid ounces fl oz 

L liters 0.264 gallons gal 

m

3 cubic meters 35.314 cubic feet ft3 

m

3 cubic meters 1.307 cubic yards yd3 

MASS 

g grams 0.035 ounces oz

kg kilograms 2.202 pounds lb

Mg (or "t") megagrams (or "metric ton") 1.103 short tons (2000 lb) T 

TEMPERATURE (exact degrees)

oC Celsius 1.8C+32 Fahrenheit oF 

ILLUMINATION 

lx lux 0.0929 foot-candles fc 

cd/m2candela/m

2

0.2919 foot-Lamberts fl

FORCE and PRESSURE or STRESS 

N newtons 0.225 poundforce lbf 

kPa kilopascals 0.145 poundforce per square inch lbf/in2

*SI is the symbol for the International System of Units. Appropriate rounding should be made to comply with Section 4 of ASTM E380. 

(Revised March 2003)
iii

TABLE OF CONTENTS

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

PROBLEM STATEMENT...................................................................................................... 1

OBJECTIVES........................................................................................................................... 1

DATA COLLECTION............................................................................................................. 1

GFM........................................................................................................................................... 2

INERTIAL PROFILER........................................................................................................... 2

CHALLENGES IN DETECTING JPCP TRANSVERSE JOINTS .................................... 3

CHAPTER 2. LITERATURE REVIEW.................................................................................... 5

CHAPTER 3. METHODOLOGY............................................................................................... 9

USE THE PROFILER’S ERD FILE OUTPUT..................................................................... 9

IMPORT ERD FILE INTO MATLAB® ............................................................................... 9

FILTER AND NORMALIZE DATA ..................................................................................... 9

PERFORM JOINT DETECTION........................................................................................ 13

COMPUTE JOINT FAULTING........................................................................................... 17

CHAPTER 4. ANALYSIS RESULTS....................................................................................... 21

CHAPTER 5. CONCLUSIONS AND RECOMMENDATIONS ........................................... 27

RECOMMENDATIONS........................................................................................................ 27

REFERENCES............................................................................................................................ 29
iv

LIST OF FIGURES

Figure 1. Diagram. MFM using the GFM....................................................................................... 2

Figure 2. Equation. Moving average smoothing filter.................................................................. 10

Figure 3. Graph. Anti-smoothed profile with 1.25-m base length................................................ 11

Figure 4. Graph. Anti-smoothed profile with 0.3-m base length.................................................. 12

Figure 5. Equation. RMS .............................................................................................................. 13

Figure 6. Flowchart. Peakdet algorithm flow chart ...................................................................... 14

Figure 7. Graph. A 4-m moving window (0–4 m) using the Peakdet algorithm to detect the 

first transverse joint................................................................................................................. 15

Figure 8. Graph. A 4-m moving window (6.025–10.025 m) using the Peakdet algorithm to 

detect the second transverse joint ........................................................................................... 16

Figure 9. Graph. Detected true positive transverse joints identified by circles............................ 17

Figure 10. Equation. Two-point slope formula............................................................................. 17

Figure 11. Graph. The slope method to determine P1 on approach slab and P2 on leave slab .... 19

LIST OF TABLES

Table 1. LTPP AFM joint detection results using LTPP profiler data ......................................... 22

Table 2. ProVAL AFM joint detection results using LTPP profiler data..................................... 22

Table 3. FDOT and LTPP AFM joint detection results using FDOT HSIP................................. 23

Table 4. LTPP AFM faulting results (slope method) using LTPP profiler data........................... 23

Table 5. ProVAL AFM faulting results using LTPP profiler data ............................................... 24

Table 6. LTPP AFM faulting results (AASHTO Method-A) using LTPP profiler data .............. 24

Table 7. Joint faulting results using FDOT HSIP data ................................................................. 25
v

LIST OF ACRONYMS AND ABBREVIATIONS

AASHTO American Association of State Highway and Transportation Officials

AFM Automated faulting measurement

ANN Artificial neural network

CEP Current elevation point

CEPos Current elevation position

CMaE Current maximum elevation

CMaEP Current maximum elevation position

CMiE Current minimum elevation

CMiEP Current minimum elevation position

ERD Engineering Research Division

FDOT Florida Department of Transportation

GFM Georgia Faultmeter

HSIP High-speed inertial profiler

ICC International Cybernetics Corporation

Inf Infinity

IRI International Roughness Index

JDR Joint detection rate

JPCP Jointed plain concrete pavements

KDOT Kansas Department of Transportation

LTPP Long-Term Pavement Performance

LVDT Linear Variable Differential Transformer

MFM Manual faulting measurement

MLR Multivariate linear regression

NaN Not a number

PPDB Pavement performance database

RMS Root mean square

1

CHAPTER 1. INTRODUCTION

The performance of jointed concrete pavements depends to a large extent on satisfactory 

performance of joints. Most jointed concrete pavement failures can be attributed to problems at 

the joint, as opposed to inadequate structural capacity. The distresses that may result from joint 

failure include faulting, pumping, spalling, corner breaks, blowups, and mid-panel cracking.

(1)

This study focuses on faulting on jointed plain concrete pavements (JPCP). Faulting is a 

common distress type in JPCP and is defined as the difference in elevation across a transverse 

joint or crack. Faulting can result from a combination of factors such as inefficient load transfer 

at joints, slab pumping, slab settlements, curling, warping, and inadequate base support 

conditions. Faulting plays a prominent role in pavement surface roughness over time, affecting 

ride comfort and driver’s safety. Moreover, significant joint faulting has an adverse impact on 

pavement lifecycle costs for maintenance and rehabilitation as well as vehicle operating costs.

(2)

PROBLEM STATEMENT

The Long-Term Pavement Performance (LTPP) Program has been collecting longitudinal profile 

data using high-speed inertial profilers (HSIP) along the wheelpaths (left and right) on concrete 

pavements since 1989 and along the center of the lane from 1995. Profile data can be used to 

evaluate the roughness of the pavement by computing a roughness index such as the 

International Roughness Index (IRI). The change in longitudinal pavement profile over time, 

which is directly related to the change in roughness with time, is an important indicator of 

pavement performance. As previously mentioned, faulting is one of several key pavement 

performance indicators. The LTPP Program collects joint and crack faulting data on a regular 

basis at each jointed concrete pavement test site using the Georgia Faultmeter (GFM). Manual 

faulting measurement (MFM) using the GFM is time consuming and entails traffic control, lane 

closures, safety measures, personnel cost, etc. To replicate MFMs collected using the GFM, this 

study uses LTPP longitudinal profile data collected using an International Cybernetics 

Corporation (ICC) MDR 4086L3 profiler to identify joint locations and determine faulting at 

each joint on a JPCP.

OBJECTIVES

The first objective was to develop, using the LTPP profile data, a new LTPP automated faulting 

measurement (AFM) algorithm that could be used in the LTPP Program to reduce the need for 

lane closure and manual data collection using the GFM (the traditional way of measuring 

faulting at transverse joints and cracks on JPCP). The second objective was to evaluate two 

existing American Association of State Highway and Transportation Officials (AASHTO)

R 36-12 automated faulting methods: ProVAL (Method-A developed by the Transtec Group, 

Inc., using LTPP 25-mm interval profiler data as the input) and PaveSuite (Method-B developed 

by the Florida Department of Transportation (FDOT) using the 20.7-mm interval HSIP data).(3)

DATA COLLECTION

This section introduces LTPP faulting data collection using the manual GFM and longitudinal 

profile data using the LTPP profiler.
2

GFM

The GFM was designed, developed, and built by Georgia Department of Transportation Office 

of Materials and Research personnel to simplify measuring concrete joint faulting and is very 

light and easy to use. There are two versions of the GFM. The manual GFM uses a dial gage to 

determine the positive or negative difference at a joint or crack, and the automated GFM uses the 

Linear Variable Differential Transformer (LVDT) to determine positive or negative faulting at a 

joint or crack. The unit weighs approximately 3.2 kg and supplies a digital readout with the push 

of a button located on the carrying handle. It reads out directly in millimeters (e.g., a digital 

readout of 3 indicates 3 mm of faulting) and shows whether the reading is positive or negative. 

The legs of the GFM’s base are set on the slab in the direction of traffic on the leave side of the 

joint. The joint must be centered between the guidelines shown on the side of the meter. The 

measuring probe contacts the slab on the approach side. Vertical movement of this probe is 

transmitted to an LVDT to measure joint faulting. Any slab that is higher on the approach side of 

the joint registers a positive faulting number. If the slab on the leave side of the joint is higher, 

then the meter gives a negative reading.

(4) MFMs collected using the GFM may have some 

potential errors caused by, for example, vertical movement of the probe rod. If the measuring rod 

does not move freely, then the reading will also be in error. In addition, due to non-linearity of 

the LVDT, cases where approach and departure slabs are not on the same plane (i.e. slabs are at 

an angle from each other) can cause errors in the readings. In addition, weak batteries and 

improper calibration of the equipment could cause erroneous readings. Finally, three 

measurements are taken at each joint or crack, and a representative reading of the three values 

(average of the three values) is entered into the pavement performance database (PPDB). Thus, 

data entry errors ± 1 mm reading resolution could occur when recording the three measurements 

on the data sheet onsite or in the PPDB. Figure 1 shows the MFM using the GFM.

Figure 1. Diagram. MFM using the GFM.

INERTIAL PROFILER

A profiler is an instrument used to produce a series of numbers related in a well-defined way to a 

true profile. Profile data obtained by a profiler describe a two-dimensional slice of the road 
3

surface taken along an imaginary line.

(5) The longitudinal profile along the wheelpaths in a 

pavement can be used to determine ride quality as well as joint/crack faulting on jointed concrete 

pavement, which are important indicators of pavement performance. Studies have shown that a 

strong correlation exists between the rate of change in faulting values and the rate of change in 

IRI values on JPCP. Faulting is a major contributor to the increased roughness of JPCP.

(6) The 

LTPP Program currently uses an Ames Engineering profiler. The profilers previously used by the 

LTPP Program were ICC MDR 4086L3 and K. J. Law Engineers, Inc., DNC 690 and T-6600. 

This study uses the 25-mm interval longitudinal profile data collected using the ICC MDR 

4086L3 profiler to detect JPCP joint locations and to determine faulting at those locations.

CHALLENGES IN DETECTING JPCP TRANSVERSE JOINTS

This section examines the following challenges in detecting JPCP transverse joints using profile 

data:

 Varying joint spacing makes it difficult to locate a transverse joint using most joint 

detection algorithms.

 Cracks that are present on a JPCP section make it difficult for the pattern search routines 

to detect joints; moreover, false positives may be reported in the detected results.

 Spalled joints in a profile show elevation valleys (dips) similar to those of a true joint. 

The challenge is to design a joint detection algorithm to find a true joint, eliminating the 

valleys due to joint spalling.

 Joints filled with sealants or incompressible materials are very difficult to detect.

 Similar to filled joints, closed joints due to slab thermal expansion may be undetectable.

 Pattern search algorithms may have difficulty detecting skewed joints. 

 Using a sampling interval of less than 25.4 mm may not identify all the joints in a 

section. Conversely, a wider sampling interval may miss the joints in an elevation profile.

 An inertial profiler’s difficulty in getting an exact true surface profile could be one of the 

issues in accurately detecting transverse joints and computing faulting.

 Distance measuring instrument drift (especially for long profiles) may cause inaccuracies 

in location data. 

5

CHAPTER 2. LITERATURE REVIEW

An extensive literature review was conducted concerning joint detection and faulting 

computation on jointed concrete pavements. Twelve documents were reviewed, and key findings 

are listed in the following summaries.

Saghafi et al. researched “Artificial Neural Networks and Regression Analysis for Predicting 

Faulting in Jointed Concrete Pavements Considering Base Condition.”

(7) The objective of the 

research was to predict jointed concrete pavement faulting by considering base layer and other 

parameters of the LTPP database using artificial neural networks (ANN) and multivariate linear 

regression (MLR) analysis. Results showed that the ANN approach predicted JPCP faulting 

more accurately than a MLR analysis, with higher coefficient of multiple determination 

(R square) values and a very low error level.

Chang et al. studied the “Practical Implementation of Automated Fault Measurement Based on 

Pavement Profiles.”

(8) The authors developed an AFM module in the ProVAL to allow practical 

application of the AFM method for State department of transportation users. The ProVAL AFM 

method detects transverse joints and cracks on JPCP and computes joint faulting. The ProVAL 

AFM serves as the basis for AASHTO R36-12, “Standard Practice for Estimating Faulting on 

Concrete Pavements.”

(3)

Miller and Bellinger developed the Distress Identification Manual for LTPP Program (Fourth 

Revised Edition).

(4) This manual provides information required for accurate, consistent, and 

repeatable distress evaluation surveys, including measurement of faulting. It provides graphics

illustrating distresses found in three basic pavement types: asphalt concrete-surfaced, jointed 

(plain and reinforced) Portland cement concrete, and continuously reinforced concrete. These 

graphics provide a reference to assess distress type, severity, and measurement. Moreover, they

provide a method to measure faulting and instructions on how to calibrate and operate fault 

measurement devices.

Khazanovich et al. worked on LTPP Data Analysis: FAQs About Joint Faulting with Answers 

from LTPP.

(9)

(This Federal Highway Administration TechBrief was developed from the study

“Common Characteristics of Good and Poorly Performing Portland Cement Concrete (PCC)

Pavements.”) The objective was to examine the LTPP database and identify site conditions and 

design features that significantly affect transverse joint faulting. The emphasis of the study was 

to find what works and does not work to control the development of joint faulting. The document 

provides answers to questions regarding design features and site conditions that lead to “good”

(better than expected) and “poor” (worse than expected) performance of jointed concrete 

pavements with regard to joint faulting. In addition, guidelines are provided to assist highway 

agencies with what works and what does not work in the design of transverse joints to control 

joint faulting.

Sayers and Karamihas published The Little Book of Profiling.

(5) The objective of the book was to 

provide basic instructions on measuring and interpreting road profiles.

Nazef et al. worked on A Semi-Automated Faulting Measurement Approach for Rigid Pavements 

Using High Speed Inertial Profiler Data.

(10) The study objectives were to determine an 
6

appropriate profiler sampling interval to accurately locate transverse joints and to determine how 

well faulting estimated from profile elevations using an AFM algorithm compares with faulting 

measured with the GFM. The AFM algorithm accurately detects, on average, 95 percent of 

transverse joints from profile data collected at highway speed using a 17.3-mm sampling 

interval. This algorithm was also adapted to estimate faulting measured with the GFM in 

accordance with the AASHTO R36-04 protocol. Although the algorithm results are repeatable, 

the algorithm over-estimated the faulting at joints by 1.3 mm to 1.5 mm compared with faulting 

measured with the GFM.

Nazef et al. conducted a validation study, “Alternative Validation Practice of an Automated 

Faulting Measurement Method.”

(11) The objective of the study was to evaluate the accuracy and 

precision of an HSIP-based AFM system using a two-phase approach. The first phase evaluated 

the HSIP’s ability to produce reliable faulting measurements under controlled conditions. The 

second phase tested the validity of the automated method to produce repeatable and reproducible 

results under normal field conditions. The goal was to use the results from this study to support 

the implementation of the AFM system in FDOT’s Annual Pavement Condition Survey process.

Except for one HSIP, all profilers achieved a minimum profile repeatability cross-correlation of 

92 percent. Under controlled conditions, the HSIP has a faulting measurement accuracy and 

repeatability of 0.60 mm and 0.65 mm, respectively. The HSIP has a positive joint detection rate

(JDR) ranging from 80 to 94 percent. Under controlled conditions, the HSIP has accuracy, 

repeatability, and reproducibility rates of 1.2 mm, 1.1 mm, and 0.5 mm, respectively.

Selezneva et al. worked on Preliminary Evaluation and Analysis of LTPP Faulting Data—Final 

Report.”

(6) The objective was to evaluate the quality of LTPP faulting data, including 

identification of missing and questionable data. Faulting data indexes (average joint faulting for 

each visit) and related statistical parameters were developed. Subsequently, these parameters

were used to determine the impact of joint faulting and related data in identifying factors that 

affect joint faulting. Analysis indicated that doweled joints exhibit very little faulting even after 

many years of service and that the effects of design features, such as drainage, tied-concrete 

shoulder use, and joint spacing, are not as significant when doweled joints are used. For nondoweled JPCP, the following design features were found to significantly reduce faulting: 

widened lanes, an effective drainage system, a stabilized base/subbase, and narrower joint 

spacing. The effect of faulting on ride quality was also investigated on JPCP sections with three 

or more faulting and IRI surveys. A strong correlation was found between the rate of change in 

faulting values and the rate of change in IRI values for JPCP sections. The results indicate that 

faulting is a major component of increased roughness of JPCP.

Perera et al. worked on LTPP Manual for Profile Measurements and Processing.

(12) The 

objective was to provide detailed information on operational procedures for measuring 

longitudinal pavement profiles for the LTPP Program using the ICC road profiler, Face®

Dipstick®, and the rod and level. The manual also explained calibration of equipment, data 

collection, record keeping, and data processing.

Karamihas and Senn conducted a study, Curl and Warp Analysis of the LTPP SPS-2 Site in 

Arizona.

(13) The authors examined the roughness and roughness progression of 21 test sections 

over the first 16 years of the experiment to analyze slab curl and warp effects on pavement 

roughness. As part of the study, the authors conducted faulting analysis (detecting joint locations
7

from multiple profiles) using profile data to examine the effect of joint faulting on IRI values. 

All test sections except 0262 and 0265 produced average faulting of less than 1.27 mm. For 

sections 0262 and 0265, the severity of faulting grew throughout the experiment, and the 

increase in IRI with time was primarily due to faulting.

Vedula et al. published “Adaptability of AASHTO Provisional Standards for Condition Surveys 

for Roughness and Faulting in Kansas.”

(14) In this study, profile data were collected on about 

346 km of Kansas highways following AASHTO provisional standards PP-37-00 for quantifying 

roughness and PP-39-00 for faulting, and the Kansas Department of Transportation (KDOT) 

standard for condition surveys. The comparison of statistical analysis results from the algorithms 

following the KDOT Network Optimization System and the AASHTO provisional standards 

(PP-37-00) indicated that roughness measurements tended to produce statistically similar results. 

However, fault values computed from AASHTO PP-39-00 and KDOT automated faulting 

procedure were significantly different even after some modification to PP 38-00 following 

current practices in Kansas.

Watkins of the Mississippi Department of Transportation developed a joint/crack-location 

algorithm based on a brute-force method by determining the elevation difference between 

adjacent samples greater than 2.03 mm using a profile sampling interval rate of 12.7 mm.(15)

9

CHAPTER 3. METHODOLOGY

The objective of this chapter is to introduce the methodology adopted to develop the LTPP AFM

algorithm to detect JPCP transverse joint locations and to determine faulting at each of the 

detected joints. The procedure consists of the following five tasks:

 Use the profiler’s Engineering Research Division (ERD) file output.

 Import the ERD file into MATLAB®.

 Filter and normalize data.

 Perform joint detection.

 Compute faulting.

USE THE PROFILER’S ERD FILE OUTPUT

The ERD at the University of Michigan Transportation Research Institute developed a file 

format with an ERD extension to facilitate automated plotting of simulation data, experimentally 

measured data, and data from various analysis programs. The LTPP profile data are presented in

an ERD text file with profile information in two parts, the header and the data. The header 

portion of the ERD file consists of a series of conventional American Standard Code for 

Information Interchange text lines. These lines contain information used by post-processing tools 

to read the numerical data such as start and end locations of the survey, speed of the vehicle,

sampling interval, date, and time. The data portion of the ERD file contains numbers organized 

into columns and rows. There are three columns: the left and right wheelpath profiles, and the 

center of the lane profile. The LTPP regional support contractors collect a minimum of five

profile runs/files on each LTPP test section of 152.4 m in length. If more are needed, then they 

can collect up to a maximum of nine profile runs/files. Information for the five most 

representative profile runs/files that pass the ProQual quality check is included in the LTPP 

PPDB. This study used these five profile runs/files for computing JPCP joint faulting.

IMPORT ERD FILE INTO MATLAB®

This study used MATLAB® software to process and analyze LTPP profile data. The data 

portion of the profile ERD file was loaded into the MATLAB® program using MATLAB®’s 

import function. The LTPP test sections are 152.4 m in length. LTPP profile data are collected 

for the entire length. A new “distance” column was created using the profile sampling interval 

(25 mm) information from the header portion of the ERD file.

FILTER AND NORMALIZE DATA

Filtering is a process of applying a mathematical transformation to true profile data to remove 

redundant noise. It is almost always necessary to filter the sequence of numbers that makes up 

the profile and to view different types of profile features. Many filters exist. A moving average 

filter was selected, and both smoothing and anti-smoothing filters are applied in this study.

The moving average filter uses the average of several adjacent profile elevation points to replace 

each profile elevation point. In this study, a 300-mm (12 data points) moving average filter was
10

applied. For a profile p that has been sampled at interval X, a moving average smoothing filter 

is defined by the summation in figure 2.

(5)

Figure 2. Equation. Moving average smoothing filter.

Where:

Pfl is the smoothed profile (also called a low-pass filtered profile).

B is the base length of the moving average.

N is the number of samples included in the summation. 

MATLAB®’s built-in moving average filter function was used to filter the true profile. Antismoothing filtering, also called high-pass filtering, is a process of subtracting the smoothed 

profile from the true/original road profile. The choice of base length for an anti-smoothing filter 

is important to be able to show either very short-duration bumps or long deviations in the profile 

elevation. A larger anti-smoothing filter base length eliminates small deviations. To detect 

transverse joints using profile data, it is important to apply an anti-smoothing filter with a smaller 

base length. Figure 3 and figure 4 show two anti-smoothed filter profiles with different antismoothing base lengths.



Pfl

 i =

1

N

 p(j)

i+

B

2∆X

j=i−

B

2∆X
11

Figure 3. Graph. Anti-smoothed profile with 1.25-m base length.
12

Figure 4. Graph. Anti-smoothed profile with 0.3-m base length.

Figure 3 and figure 4 show that the anti-smoothed profile with a smaller base length (0.3 m) 

displays profile deviations (joints/cracks) more clearly compared with the anti-smoothed profile 

with a base length of 1.25 m. 

Once smoothing and anti-smoothing filters are applied, the next step is to normalize the antismoothed profile with the root-mean-square (RMS) value. The RMS value provides the 

magnitude of the elevation profile and facilitates an automated search for the deepest dips or 

valleys.

The RMS value of a vector, X, is defined in figure 5 with the summation performed along the 

specified dimension.
13

Figure 5. Equation. RMS.

PERFORM JOINT DETECTION

After filtering a longitudinal profile for two wheelpaths (e.g., left and right wheelpaths) and a

center of the lane profile, MATLAB®’s Peakdet algorithm, developed by Eli Billauer, is used,

along with the moving window method, to detect the valleys.

(16) The algorithm also cross-checks 

the detected valleys of one wheelpath against the other and against the center lane profile to 

detect a transverse joint (true joint on JPCP) and eliminate or minimize false positives (other 

than a true joint on JPCP). A peak is the highest point between valleys. Peakdet looks for the 

highest point around which there are points lower by (positive value, for example, of 1 or 2) 

on both sides. A similar peak and valley detection algorithm was reported by Nazef et al.(10)

Valleys in longitudinal road profile data collected on JPCP represent either potential joints or 

cracks. This study focuses on valleys that are transverse joints. Six steps describe how the 

Peakdet algorithm in MATLAB® detects peaks and valleys in a road elevation profile.

Step 1: Initialize the current elevation point (CEP) at V(i) and current elevation position (CEPos) 

at X(i) at the first point (i equals 1) in an elevation profile. For illustration, a moving window 

width of 4 m is used based on the average distance between two JPCP joints for a test section; 

the window can be adjusted as desired. The CEPos for the first moving window starts at 0 m 

(X(i) at i equals 1), and the last moving window would be the total profile survey distance 

(152.4 m) minus moving window width (4 m), which is 148.4 m, where i equals 5,936 m

(148.4/0.025 m).

Step 2: Check the valleys within the moving window (4 m) for the three profiles (left wheelpath, 

right wheelpath, and center lane). If CEPos is less than or equal to 148.4 m, then CEPos equals 0 

for the first point X(1) and the moving widow equals (CEPos + 4) m.

Step 3: Initialize current maximum elevation (CMaE) at negative infinity (Inf), current maximum 

elevation position (CMaEP) at not a number (NaN), current minimum elevation (CMiE) at Inf, 

and current minimum elevation position (CMiEP) at NaN.

Step 4: Move to the next elevation point V(i). If CEP is greater than CMaE, then make CMaE 

equal to its elevation and store its position as the new CMaEP. If CEP is less than CMiE, then 

make CMiE equal to its elevation and store its position as the new CMiEP. If CEP is less than

CMaE - , then set Peak (i) at CMaE and store the Peak_Loc (i) at CMaEP. If CEP is greater 

than CMiE + , then set Valley (i) at CMiE and store the Valley_Loc (i) at CMiEP.

The flow chart in figure 6 explains steps 3 and 4.






14

Figure 6. Flowchart. Peakdet algorithm flow chart.

Step 5: Repeat step 4 until the valleys are detected within the moving window. For a profile,

more than one valley could be detected in one wheelpath (i.e., CMiEP and its corresponding 

CMiE) using the Peakdet algorithm within the 4-m moving window. To be certain the valley 

detected is not a false positive, the difference between the lowest valley positions (i.e., CMiEPs 

with minimum CMiEs) for at least two profiles (e.g., the left wheelpath and the center lane) 

should be less than 0.2 m. The transverse joint locations are common for the three profiles along 

the entire lane width. However, in the case of skewed joints, the transverse joint location varies 

for the three profiles and is addressed using a 0.2 m criterion. (It allows detection of the skewed 

joint, if present.)

One of the CMiEPs from two profiles that meet the 0.2 m criterion is taken as the basis to locate 

true positive joints for the left and right profiles. Once the true positive joint is detected for a 

profile, the CEPos of the next moving window would start at 2.5 m (which is 0.625 times the 

moving window width of 4 m) from the detected joint. The transverse joints on JPCP are spaced 

at least 3 m from the previous joint except in the case of full-depth patching, which causes joint 
15

spacing to be less than 3 m. If the first transverse joint detected for the right profile is at 3.525 m 

from the start point of the profile section (0 m), then the CEPos of the next moving window 

would not start from 4.025 to 8 m; rather it would start at 6.025 to 10.025 m, (i.e.,

3.525 plus 2.5 m) and would detect the next true positive joint. Thus, potential false positives

would be eliminated. In addition, the algorithm checks potential true joint locations with two

other profiles taken on the same lane to eliminate spalled joint locations.

Figure 7 and figure 8 illustrate the moving window approach to detect transverse joints using the 

Peakdet algorithm.

Figure 7. Graph. A 4-m moving window (0–4 m) using the Peakdet algorithm to detect the 

first transverse joint.
16

Figure 8. Graph. A 4-m moving window (6.025–10.025 m) using the Peakdet algorithm to

detect the second transverse joint.

Step 6: Repeat step 2 through step 5 to detect all true positive transverse joints, incrementally 

scanning through the elevation profile and sorting the detected joints. Figure 9 shows the 

detected transverse joints identified by circles on a longitudinal elevation profile.
17

Figure 9. Graph. Detected true positive transverse joints identified by circles.

COMPUTE JOINT FAULTING

The last task is to compute the JPCP transverse joint faulting. Faulting is defined as the 

difference in elevation across a transverse joint or crack. In this study, faulting is computed using 

two methods: the slope method (in-house method) and AASHTO R36-12 Method-A. The 

following steps explain faulting computed using the slope method. For faulting computation 

using AASHTO R36-12 Method-A, the reader is referred to AASHTO R36-12, “Standard 

Practice for Estimating Faulting on Concrete Pavements.”

(3)

The slope method is used to identify the point P1 on the approach slab and point P2 on the leave 

slab. P1 and P2 are data points on the approach and leave slab. Once P1 and P2 are identified, the 

faulting can be computed by taking the difference of P1 and P2. If P1 is greater than P2, then the 

result is called positive faulting. If P1 is less than P2, then the result is called negative faulting.

In figure 10, S is absolute slope, and (X1,Y1) and (X2, Y2) are coordinates of points P1 and P2.

Figure 10. Equation. Two-point slope formula.
18

To detect P1, the absolute slope is computed using two data points between the detected 

transverse joint’s CMiEP (for example, X2) and CMiE (for example, Y2) and the previous 

elevation points (for example, X1 and Y1, respectively). If the absolute slope computed is less 

than 10 (which was selected to make sure the points P1 and P2 are not taken on distressed

surface areas such as potholes), then P1 is set equal to Y1. If the absolute slope is not less than 

10, then the absolute slope is computed for the closest previous elevation point.

Similarly, to detect P2, the absolute slope is computed using two data points between the 

detected transverse joint’s CMiEP (X2) and CMiE (Y2) and the next location and elevation 

points (e.g., L1 and E1). If the absolute slope computed is less than 10, then P2 is set equal to E1. 

If the absolute slope is not less than 10, then the absolute slope is computed for the next 

elevation point.

Once the joint faulting is computed, the results are exported in Microsoft® Excel format.

Figure 11 illustrates the slope method to compute transverse joint faulting. The traverse joint 

location is first located, and then points P1 on the approach slab and P2 on the leave slab are 

established. The transverse joint location was detected at distance 3.525 m (X) and elevation 

1.977 mm (Y). The computed absolute slope between the detected transverse joint coordinates 

(X at 3.525 m and Y at 1.977 mm) and the previous profile data point (X at 3.5 m and Y at 2.759

mm) was more than 10, so the procedure was continued until P1 was reached at X equal to 3.475 

m and Y equal to 4.165 mm. Similarly, the computed absolute slope between the detected 

transverse joint location (X at 3.525 m and Y at 1.977 mm) and the next profile data point (X at

3.55 m and Y at 3.992 mm) was more than 10, so the procedure was continued until P2 was 

reached at X equal to 3.575 m and Y equal to 3.794 mm. Thus, the elevation (Y) of P1 on the 

approach slab was 4.165 mm, and the elevation of P2 on the leave slab was 3.794 mm. The

faulting computed at the joint location was therefore 0.371 mm (4.165 minus 3.794). This slope 

method approach was used to compute faulting at all detected transverse joint locations.
19

Figure 11. Graph. The slope method to determine P1 on approach slab and P2 on leave

slab.

21

CHAPTER 4. ANALYSIS RESULTS

In this chapter, the analysis and comparison results of the LTPP AFM, the ProVAL AFM, and 

the FDOT PaveSuite AFM are presented. To evaluate the LTPP AFM, six LTPP JPCP test 

sections were selected such that the manual distress survey dates matched or were close to the 

longitudinal profile survey dates. The same six LTPP test sections were also used to evaluate the 

ProVAL AFM.

Table 1 and table 2 show the evaluation results of the LTPP AFM. In table 1, the fourth column 

from the left represents the total number of transverse joints present on the 152.4-m test section. 

The transverse joint locations and faulting measurements were collected using manual distress 

surveys and were stored in LTPP PPDB table MON_DIS_JPCC_FAULT. Profile runs in ERD 

format from the MON_PROFILE_MASTER table for the LTPP test section that passed the 

ProQual quality check were used in the analysis. The column “TP” stands for “true positive,”

whereas column “FP” stands for “false positive.” True positive means the true transverse joint 

was detected, whereas false positive means something other than the true transverse joint, such 

as a crack or pothole, was detected.

According to the data in table 1, for Georgia test section 133019 (STATE_CODE of 13 and 

SHRP_ID of 3019), the LTPP AFM detected all of the 25 true transverse joints with 0 false 

positives using the 5 ERD files collected from that section. For New York test section 364018, 

the true positive JDR was 95 percent, i.e., the LTPP AFM missed one true positive joint from 

ERD files 3 and 4. The JDR from the LTPP AFM for the six selected sections ranged from 95 to 

100 percent.

Similarly, the same six LTPP test sections and five ERD files were used to evaluate the ProVAL 

AFM. According to the data in table 2, New York test section 364018 had a JDR of 67.5 percent, 

whereas the LTPP AFM JDR was 95 percent (table 1). The total number of false positives 

detected for the New York test section using the ProVAL AFM for the five profile ERD files 

(i.e., ERD1 to ERD5) was 33 (5 + 7 + 10 + 6 + 5). The total false positives detected using the 

LTPP AFM (table 1) for the same test section (364018) for five profile files was four. The JDR

from ProVAL AFM for the six selected sections ranged from 58 to 99.4 percent.
22 

Table 1. LTPP AFM joint detection results using LTPP profiler data.

State 

Code

SHRP 

ID

Survey 

Date

Total No. 

of

Transverse

Joints

ERD 

File 1

ERD 

File 2

ERD 

File 3

ERD 

File 4

ERD 

File 5

Average

True 

Positives 

DetectedTP FP TP FP TP FP TP FP TP FP

13 3019 11/27/2007 25 25 0 25 0 25 0 25 0 25 0 25 

31 3018 12/18/2003 32 32 0 32 0 32 0 32 0 32 0 32 

36 4018 4/13/2010 8 8 0 8 1 7 1 7 1 8 1 7.6 

37 201 9/19/2002 33 32 0 33 0 33 0 33 0 33 0 32.8 

42 1606 10/15/2003 10 9 2 10 0 10 0 10 1 9 1 9.6 

49 3011 10/9/2007 34 34 0 34 0 34 0 34 0 34 0 34 

ERD = Engineering Research Division 

FP = False Positive 

JDR = Joint Detection Rate 

SHRP = Strategic Highway Research Program 

TP = True Positive 

Table 2. ProVAL AFM joint detection results using LTPP profiler data.

State 

Code

SHRP 

ID

Survey 

Date

Total No. of

Transverse

Joints

ERD 

File 1

ERD 

File 2

ERD 

File 3

ERD 

File 4

ERD 

File 5

Average

True 

Positives 

DetectedTP FP TP FP TP FP TP FP TP FP

13 3019 11/27/2007 25 22 1 21 1 23 0 22 1 23 0 22.2 

31 3018 12/18/2003 32 28 0 29 0 29 0 29 0 30 0 29 

36 4018 4/13/2010 8 7 5 4 7 3 10 7 6 6 5 5.4 

37 201 9/19/2002 33 31 0 31 0 30 0 31 0 30 0 30.6 

42 1606 10/15/2003 10 6 5 6 4 6 7 5 9 6 8 5.8 

49 3011 10/9/2007 34 34 0 33 1 34 0 34 0 34 0 33.8 

ERD = Engineering Research Division 

FP = False Positive 

JDR = Joint Detection Rate 

SHRP = Strategic Highway Research Program 

TP = True Positive
23

Table 3 shows the joint detection comparison results between the LTPP AFM and the FDOT 

PaveSuite AFM methods, using FDOT HSIP data collected on State Road 24 in Waldo, FL. 

FDOT’s 609.6-m test section included a 152.4-m lead-in and lead-out, and a 304.8-m effective 

test length spanning 50 slab joints. The slabs were typically 6.1 m long by 3.66 m wide with a 

relatively smooth surface finish. Both AFM methods detected 48 true positives with a JDR of 

96 percent. However, according to the data in table 3, the FDOT PaveSuite AFM detected eight 

false positives, whereas the LTPP AFM detected zero false positives. 

Table 3. FDOT and LTPP AFM joint detection results using FDOT HSIP.

AFM Method

Total No. 

of

Transverse

Joints

FDOT HSIP Profiler

TP FP JDR (percent)

FDOT AFM

50

48 8 96

LTPP AFM 48 0 96

AFM = Automated faulting measurement

FDOT = Florida Department of Transportation

HSIP = High-speed intertial profiler

JDR = Joint detection rate

LTPP = Long-Term Pavement Performance

Table 4 through table 7 show the analysis results for AFMs computed using both the ProVAL 

and the LTPP AFM methods. As discussed in the methodology, the LTPP AFM uses two 

methods, the slope method (in-house method) and the AASHTO R 36-12 Method-A, to compute 

faulting measurements. The ProVAL AFM uses AASHTO R 36-12 Method-A. Table 4 shows 

the average faulting for the entire test section (152.4 m) for all five profile runs computed using

the LTPP AFM slope method. The average of absolute differences in faulting estimated by the 

LTPP AFM and faulting measured by the manual GFM on the six LTPP JPCP test sections are

shown in the “Average section |Bias|” column. Test sections 133019, 370201, and 493011 have 

less than 1-mm bias/error as required by the AASHTO R 36-04 standard. However, test sections 

313018, 364018, and 421606 have a bias/error greater than 1 mm.

Table 4. LTPP AFM faulting results (slope method) using LTPP profiler data.

State 

Code SHRP ID Survey Date

GFM Average

Section 

Faulting (mm)

Average Section 

Faulting for All 

Five Runs (mm)

Average Section 

|Bias| for All 

Five Runs (mm)

13 3019 11/27/2007 0.84 0.56 0.80

31 3018 12/18/2003 4.41 3.28 3.72

36 4018 4/13/2010 1.75 -3.05 5.07

37 201 9/19/2002 0.15 0.37 0.44

42 1606 10/15/2003 3.30 0.39 2.98

49 3011 10/9/2007 3.32 3.48 0.95

GFM = Georgia Faultmeter

SHRP = Strategic Highway Research Program

The average faultings for the entire test section (152.4 m) for all five runs computed using the

ProVAL AFM are shown in table 5. Also, the average of the absolute difference in faulting 

estimated by the ProVAL AFM and faulting measured by the manual GFM on the six LTPP 
24

JPCP test sections are shown in the “Average section |Bias|” column of table 5. Sections 133019 

and 313018 have less than 1-mm bias/error as required by the AASHTO R 36-04 standard. 

However, test sections 364018, 370201, 421606, and 493011 have a bias/error greater than 

1 mm.

Table 5. ProVAL AFM faulting results using LTPP profiler data.

State 

Code SHRP ID Survey Date

GFM Average

Section 

Faulting (mm)

Average Section 

Faulting for All 

Five Runs (mm)

Average Section |Bias| 

for All Five Runs (mm)

31 3018 12/18/2003 4.41 5.04 0.88

36 4018 4/13/2010 1.75 -6.58 8.75

37 201 9/19/2002 0.15 1.08 1.02

42 1606 10/15/2003 3.30 1.35 2.46

49 3011 10/9/2007 3.32 4.71 1.46

GFM = Georgia Faultmeter

SHRP = Strategic Highway Research Program

Table 6 shows the LTPP AFM joint faulting using AASHTO R 36-12 Method-A. Only four test 

sections were analyzed using AASHTO R 36-12 Method-A. Test sections 133019 and 370201 

have less than a 2-mm bias/error. However, sections 364018 and 421606 have a bias/error of 

13.81 and 3.68 mm, respectively.

Table 6. LTPP AFM faulting results (AASHTO Method-A) using LTPP profiler data.

State 

Code SHRP ID Survey Date

GFM Average

Section 

Faulting (mm)

Average Section 

Faulting for all 

Five Runs (mm)

Average Section |Bias| 

for All Five Runs (mm)

36 4018 4/13/2010 1.75 -12.06 13.81

37 201 9/19/2002 0.15 1.66 1.59

42 1606 10/15/2003 3.30 -0.38 3.68

GFM = Georgia Faultmeter

SHRP = Strategic Highway Research Program

Comparison results of the FDOT PaveSuite AFM and the LTPP AFM using the FDOT HSIP 

data are shown in table 7. The average faulting measured from the FDOT PaveSuite AFM was 

1.69 mm, while the LTPP AFM, using the slope method for the same data, was 1.62 mm. (For 

FDOT HSIP data, only the in-house method, i.e., the slope method, was used to compute joint 

faulting; AASHTO R 36-12 Method-A was not used.) Also, the average of absolute differences 

in faulting estimated by the FDOT PaveSuite AFM and the LTPP AFM slope method with the 

measured manual GFM were 1.05 and 1.14 mm, respectively. Both AFM results using the FDOT 

data were similar.
25

Table 7. Joint faulting results using FDOT HSIP data.

Method

GFM Average

Section Faulting 

(mm)

Average Section 

Faulting (mm)

Average Section |Bias| 

(mm)

FDOT AFM

1.81

1.69 1.05

LTPP AFM (Slope Method) 1.62 1.14

AFM = Automated faulting measurement

FDOT = Florida Department of Transportation

GFM = Georgia Faultmeter

LTPP = Long-Term Pavement Performance

27

CHAPTER 5. CONCLUSIONS AND RECOMMENDATIONS

This study was conducted to develop a new LTPP AFM algorithm to detect transverse joint 

locations and compute joint faulting and to compare the new method with the existing AASHTO 

R 36-12 AFM methods (including the ProVAL AFM (AASHTO Method-A) and the FDOT 

PaveSuite AFM (AASHTO Method-B)). LTPP profiler longitudinal elevation profiles at 25-mm 

sampling intervals and the FDOT profiler at 20.7-mm sampling intervals were used. The joint 

detection results from the six selected LTPP sections show that the LTPP AFM algorithm was 

more effective than the ProVAL AFM routine. The JDRs from the ProVAL AFM for the six 

selected sections ranged from 58 to 99.4 percent, whereas the JDRs for the same six LTPP test 

sections using the LTPP AFM ranged from 95 to 100 percent. The average section biases

computed for the ProVAL AFM and the manual GFM for test sections 364018, 370201, 421606,

and 493011 were greater than 1 mm, as were the average section biases for the LTPP AFM and 

the manual GFM for test sections 313018, 364018, and 421606. These results could be because 

the joint faulting measurement surveys using the GFM and the LTPP profilers were not

conducted on the same wheelpaths, at the same time of the day, or under the same temperature 

conditions.

The average of absolute differences between faulting estimated by the LTPP AFM and the 

manual GFM (average section bias) for the six LTPP sections ranged from 0.44 to 5.07 mm. 

Similarly, the average of absolute differences between faulting estimated by the ProVAL AFM 

and the manual GFM (average section bias) for the same six LTPP sections ranged from 0.88 to 

8.75 mm. FDOT used an HSIP profiler to collect elevation profile data with a 20.7-mm sampling 

interval on the same wheelpaths (left and right) where manual GFM measurements were 

collected, at same time of the day, and under the same temperature conditions. The JDR for the 

one FDOT test section using both the LTPP AFM and the FDOT AFM was 96 percent. The 

averages of absolute differences in faulting estimated by the FDOT PaveSuite AFM and the 

LTPP AFM with the measured manual GFM were 1.05 and 1.14 mm, respectively. From this 

study, it appears that the newly developed LTPP AFM is relatively reliable in detecting 

transverse joints and computing joint faulting.

RECOMMENDATIONS

 A profiler sampling interval of less than 25.4 mm may yield better results for both 

transverse joint detection and faulting measurements.

 Faulting measurements surveys conducted using the manual GFM and the LTPP profiler 

on the same wheelpaths, at the same time of the day, and under the same temperature 

conditions may generate better AFM results.

 Further research is needed to understand the bias of the AFMs and manual GFM 

measurements.

29

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Highway Administration, McLean, VA, November 30, 1990.

2. U.S. Department of Transportation, Integrated Materials and Construction Practices 

for Concrete Pavement: A State-of-the Practice Manual, FHWA Publication No. HIF07-004, Federal Highway Administration, McLean, VA, December 2006.

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American Association of State Highway and Transportation Officials, Washington, 

DC, 2012.

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VA, November 2008.
30

13. Karamihas, S.M. and Senn, K., Curl and Warp Analysis of the LTPP SPS-2 Site in 

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HRDI-30/02-15(500)E Recycled

Recyclable