Research, Development, and Technology

Turner-Fairbank Highway Research Center

6300 Georgetown Pike

McLean, VA 22101-2296

PUBLICATION NO. FHWA-HRT-23-111 DECEMBER 2023 

Development and Use of the LTPP 

Analysis-Ready Materials Dataset
FOREWORD

Knowledge of the pavement layer structure and material properties are fundamental requirements 

for the development of meaningful pavement performance models and inputs for pavement 

design and performance modeling, among other uses. The Long-Term Pavement Performance 

(LTPP) program has a wealth of materials information and data from almost every program test 

section, as well as from a well-structured laboratory materials testing program to further classify 

and characterize pavement layers. However, understanding and using the database is not 

necessarily an easy undertaking. For starters, data are distributed among many tables, multiple 

values exist for a given layer, data are missing for some layers, and some data require further 

interpretation to provide meaningful results. 

Therefore, the program developed a process to generate LTPP Analysis-Ready Materials Dataset 

(ARMAD). This dataset summarizes the material properties for every layer in the database, 

which has the benefit of substantially reducing researchers’ time and making the data more 

accessible to all. The ARMAD dataset is now available on the LTPP InfoPaveTM web portal 

(FHWA 2022a). This report provides the objectives, rationale, process, and results of the 

development of the ARMAD dataset and is useful to transportation agencies, consultants, and 

researchers who will use ARMAD to exploit LTPP data to gain knowledge into the how and why 

of pavement performance, which is the primary goal of the LTPP program. 

Jean Nehme, Ph.D., P.E.

Director, Office of Infrastructure

Research and Development

Notice

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

(USDOT) in the interest of information exchange. The U.S. Government assumes no liability for 

the use of the information contained in this document. 

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

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

objective of the document. 

Quality Assurance Statement

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

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

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

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

ensure continuous quality improvement.
TECHNICAL REPORT DOCUMENTATION PAGE

1. Report No.

FHWA-HRT-23-111

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

4. Title and Subtitle

Development and Use of the LTPP Analysis-Ready 

Materials Dataset

5. Report Date

December 2023

6. Performing Organization Code:

7. Author(s)

Zahra (Niosha) Afsharikia (ORCID: 0000-0002-7232-4958), 

Jonathan Groeger (ORCID: 0000-0002-1307-6755), Lauren 

Gardner, Barbara Ostrom (ORCID: 0000-0002-5603-8839), 

and Gonzalo Rada (ORCID: 0000-0001-5409-0396) 

8. Performing Organization Report No.

9. Performing Organization Name and Address

Wood Environment & Infrastructure Solutions, Inc.

1747 Dorsey Road, Suite Q

Hanover, MD 20176

10. Work Unit No.

11. Contract or Grant No.

693JJ320D000025

12. Sponsoring Agency Name and Address

Office of Infrastructure

Federal Highway Administration

6300 Georgetown Pike

McLean, VA 22101-2296

13. Type of Report and Period Covered

Final Report; December 2020–June 2022

14. Sponsoring Agency Code

HRDI-30

15. Supplementary Notes

The Contracting Officer’s Representative was Jane Jiang (HRDI-30; ORCID: 0000-0003-3982-2530). 

16. Abstract

The objective of the Federal Highway Administration Long-Term Pavement Performance (LTPP) program is to 

increase pavement life by the investigation of the long-term performance of various designs of pavement 

structures and rehabilitated pavement structures, using different materials and under different loads, 

environments, subgrade soil, and maintenance practices. Information about pavement material properties is a key 

component of pavement performance and is of great interest to researchers. This report provides documentation 

regarding the objectives, scope, and approach of a study to develop an Analysis-Ready Materials Dataset

(ARMAD), which integrates the material properties of several categories of pavement layers—asphalt concrete 

materials, portland cement concrete materials, unbound granular base/subbase, stabilized base/subbase, subgrade, 

and other layers such as surface treatments and engineering fabrics. The scope of this effort was to identify an 

essential set of material properties for each type of pavement layer, assemble and process the data of the selected 

material properties, assess the percentage of missing values in the selected material properties gathered from the 

LTPP database, impute or assume missing material properties, and populate the LTPP ARMAD. In addition, a 

data source and variability indicator was assigned to each material property so users can understand the method

used to obtain the material property and hence the associated data derivation method and variability. This 

document provides details concerning how ARMAD’s material properties were selected, how the ARMAD was

populated, and what the resulting dataset contains. It could be used by researchers who will utilize the ARMAD 

dataset for their analyses. This document also provides references and resource documents to understand the 

overall LTPP materials sampling and testing program.

17. Key Words

LTPP, Long-Term Pavement Performance, 

materials, unbound materials, portland cement 

concrete, asphalt concrete, engineering fabrics, 

interlayers, General Pavement Studies, Specific 

Pavement Studies, field sampling, laboratory 

testing

18. Distribution Statement

No restrictions. This document is available to the public 

through the National Technical Information Service, 

Springfield, VA 22161.

https://www.ntis.gov

19. Security Classif. (of this report)

Unclassified

20. Security Classif. (of this page)

Unclassified

21. No. of Pages

76

22. Price

N/A

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

 

 

 

 

 

 
iii

TABLE OF CONTENTS

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

ARMAD Objective and Scope ............................................................................................... 3

LTPP Materials Testing Program Synopsis ......................................................................... 3

GPS and SPS Materials Sampling and Testing Approach ................................................. 4

GPS Test Sections............................................................................................................... 4

SPS-1, SPS-2, SPS-5, SPS-6, SPS-7, SPS-8, SPS-9, and SPS-10 Test Sections ............... 5

SPS-3 and SPS-4 Test Sections .......................................................................................... 6

Report Organization ............................................................................................................... 7

CHAPTER 2. EXAMPLE USE CASES OF LTPP ANALYSIS-READY MATERIALS 

DATASET...................................................................................................................................... 9

Use Cases.................................................................................................................................. 9

Use Case 1: Materials Study ............................................................................................... 9

Use Case 2: Materials Engineering Research Project ....................................................... 11

Considerations ....................................................................................................................... 12

Material Properties Values ................................................................................................ 12

Material Properties Do Not Exist for All Layers .............................................................. 13

Assumptions for Backcasted Values ................................................................................. 13

Computed Values .............................................................................................................. 13

Data Analysis Operations Feedback Reports.................................................................... 13

CHAPTER 3. GENERAL APPROACH TO DEVELOPMENT OF THE MATERIALS 

ANALYSIS-READY DATASET ............................................................................................... 15

Selection of Material Properties .......................................................................................... 15

Fundamental Material Properties ...................................................................................... 15

Importance to Pavement Performance Analyses .............................................................. 16

Usefulness in Computing Other Data Elements ............................................................... 16

Key Properties Availability............................................................................................... 16

Data Discovery ...................................................................................................................... 18

Deriving Representative Values........................................................................................... 19

Populating ARMAD in the PPDB ....................................................................................... 19

Primary Data Table: ANALYSIS_TST_* ........................................................................ 19

Data Support Table: ANALYSIS_TST_*_SUPPORT..................................................... 19

Data Tables ....................................................................................................................... 19

Contents of Each ARMAD Material Property Table ........................................................ 20

Contents of Each ARMAD Support Table ....................................................................... 21

Tracking Changes in Pavement Structure Over Time ...................................................... 24

ARMAD Quality Control ..................................................................................................... 24

Data Structure Checks....................................................................................................... 25

IMS-Related Checks ......................................................................................................... 25

Data Content Checks......................................................................................................... 25

Support Table Checks ....................................................................................................... 26

Disseminating Data ............................................................................................................... 27
iv

CHAPTER 4. GENERATION OF UNBOUND MATERIALS ANALYSIS-READY 

DATASET .................................................................................................................................... 29

Data Discovery ...................................................................................................................... 29

Deriving Representative Values........................................................................................... 29

Calculated Values.................................................................................................................. 32

Assumed Values .................................................................................................................... 33

Data Source and Variability ................................................................................................ 34

CHAPTER 5. GENERATION OF PCC MATERIALS ANALYSIS-READY 

DATASET.................................................................................................................................... 37

Data Discovery ...................................................................................................................... 37

Deriving Representative Values........................................................................................... 38

PCC Strength/Response Properties ................................................................................... 40

PCC Nonstrength/Response Properties............................................................................. 42

Data Source and VariabilIty ................................................................................................ 44

CHAPTER 6. GENERATION OF ASPHALT CONCRETE MATERIALS 

ANALYSIS-READY DATASET ............................................................................................... 47

Data Discovery ...................................................................................................................... 47

Determining Representative Values .................................................................................... 48

Mechanical Properties ....................................................................................................... 50

Volumetric Properties ....................................................................................................... 52

Thermal Properties ............................................................................................................ 53

Data Source and Variability ................................................................................................ 53

CHAPTER 7. GENERATION OF TREATED MATERIALS ANALYSIS-READY 

DATASET.................................................................................................................................... 55

Data Discovery ...................................................................................................................... 55

Determining Representative Values .................................................................................... 55

Material and Treatment Type ............................................................................................ 55

Strength Properties ............................................................................................................ 57

Data Source and Variability ................................................................................................ 59

CHAPTER 8. GENERATION OF OTHER MATERIALS ANALYSIS-READY 

DATASET.................................................................................................................................... 61

Data Discovery and Determining Representative Values ................................................. 61

Data Source and Variability ................................................................................................ 61

CHAPTER 9. ADDITIONAL READING ................................................................................ 63

ACKNOWLEDGMENTS .......................................................................................................... 65

REFERENCES ............................................................................................................................ 67
v 

LIST OF FIGURES

Figure 1. Illustration. Typical sampling areas for an LTPP GPS test section. ............................... 5

Figure 2. Illustration. Example unbound materials sampling areas for an LTPP SPS project. ...... 6

Figure 3. Illustration. Typical sampling areas for an LTPP SPS-3 or -4 project. ........................... 6

Figure 4. Equation. Calculation of percent air voids. ................................................................... 10

Figure 5. Illustration. Partial depiction of sampling for asphalt layers for a GPS test 

section. ................................................................................................................................. 10

Figure 6. Illustration. Partial depiction of sampling for asphalt layers for an SPS-1 project. ...... 11

Figure 7. Flowchart. Unbound materials decision tree. ................................................................ 30

Figure 8. Equation. Calculation of resilient modulus. .................................................................. 32

Figure 9. Flowchart. Decision tree for deriving PCC layers’ representative properties. .............. 39

Figure 10. Equation. Calculation of 28-d compressive strength of PCC. ..................................... 40

Figure 11. Equation. Calculation of 28-d elastic modulus of PCC............................................... 41

Figure 12. Equation. Calculation of the 28-d PCC modulus of rupture. ...................................... 41

Figure 13. Equation. Calculation of the 28-d tensile strength of PCC. ........................................ 42

Figure 14. Equation. Estimation of the PCC ultimate shrinkage. ................................................. 43

Figure 15. Equation. Prediction of PCC zero-stress temperature. ................................................ 44

Figure 16. Flowchart. Decision tree for deriving AC layers’ representative properties. .............. 49

Figure 17. Equation. Estimation of the aggregate coefficient of thermal contraction in AC. ...... 51

Figure 18. Equation. Calculation of AC VMA. ............................................................................ 52

Figure 19. Equation. Calculation of AC VFA. ............................................................................. 52
vi 

LIST OF TABLES

Table 1. Matrix of test results for a theoretical partial SPS-1 project........................................... 11

Table 2. List of key material properties ........................................................................................ 17

Table 3. List of tables created for ARMAD in the PPDB. ........................................................... 20

Table 4. Data source code descriptions......................................................................................... 21

Table 5. Classification system used for REP_CODE_SOURCE_VARIABILITY field. ............ 22

Table 6. The LTPP data table or source to extract each material property. .................................. 29

Table 7. Confining pressures for unbound granular design resilient modulus computations. ...... 33

Table 8. Unbound granular subgrade bulk stress (θ value in figure 8). ........................................ 33

Table 9. Unbound granular base/subbase bulk stress (θ value in figure 8). ................................. 33

Table 10. Assumed Poisson’s ratio and hydraulic conductivity values used in the dataset. ........ 34

Table 11. The LTPP data table or source to extract each material property—PCC dataset. ........ 37

Table 12. The LTPP data table or source to extract each material property—AC dataset. .......... 47

Table 13. The LTPP data table or source to extract each material property—treated 

materials dataset. ................................................................................................................. 55

Table 14. Soil classification for combined geological class cases. ............................................... 56

Table 15. Treatment types for combined treatment cases............................................................. 56

Table 16. Aggregate types for combined aggregate type cases. ................................................... 57

Table 17. MEPDG-recommended elastic/resilient modulus for LTPP chemically treated 

material types. ..................................................................................................................... 58

Table 18. MEPDG-recommended Poisson’s ratio values for each LTPP treated material 

type ...................................................................................................................................... 58
vii 

LIST OF ABBREVIATIONS

AASHTO American Association of State Highway and Transportation Officials

AC asphalt concrete

ARD Analysis-Ready Dataset

ARMAD Analysis-Ready Materials Dataset

ATB asphalt-treated base

BSG bulk specific gravity 

CN construction number 

COV coefficient of variation

CTB cement-treated base

DAOFR data analysis/operations feedback report 

DSR dynamic shear rheometer

EF engineering fabrics 

FHWA Federal Highway Administration

GPS General Pavement Studies

IDT indirect tensile test

IMS Information Management System

LTPP Long-Term Pavement Performance

MEPDG Mechanistic-Empirical Pavement Design Guide

PAV pressure aging vessel 

PCC portland cement concrete

PMED Pavement ME DesignTM 

PPDB Pavement Performance Database

RTFO rolling thin-film oven test

SD standard deviation

SDR Standard Data Release

SHA State highway agency

SHRP Strategic Highway Research Program

SME subject matter expert

SPS Specific Pavement Studies

VFA voids filled with asphalt

VMA voids in the mineral aggregate 

1 

CHAPTER 1. INTRODUCTION

The objective of the Federal Highway Administration (FHWA) Long-Term Pavement 

Performance (LTPP) program is to increase pavement life by the investigation of long-term 

performance of various designs of pavement structures and rehabilitated pavement structures, 

using different materials and under different loads, environments, subgrade soil, and 

maintenance practices. Specific goals for the LTPP program include the following: 

• Evaluate existing design methods. 

• Develop improved design methodologies and strategies for the rehabilitation of existing 

pavements.

• Develop improved design equations for new and reconstructed pavements. 

• Determine the effects of loading, environment, material properties and variability, 

construction quality, and maintenance levels on pavement distress and performance. 

• Determine the effects of specific design features on pavement performance.

• Establish a national long-term pavement database to support future needs. 

The LTPP program is a study of the behavior of pavement test sections located on in-service 

roadways. These in-service pavement test sections are classified in the LTPP program under 

General Pavement Studies (GPS) and Specific Pavement Studies (SPS). The GPS consists of a 

series of studies on 976 in-service pavement test sections to examine specific features of existing 

pavement. The SPS addresses specific variables involving new construction, maintenance 

treatments, and rehabilitation activities and presently comprises 1,605 pavement test sections. 

The sections are located throughout the United States and Canada (FHWA 2015).

Information about pavement material properties is a key component of all aspects of pavement 

engineering, such as design, construction, quality assurance, maintenance and rehabilitation, and 

management. The pavement layer structure and material properties are fundamental requirements 

for the development of meaningful pavement performance models and inputs for pavement 

design, among other uses. One of the uses of materials testing data is to provide the inputs 

needed for using the American Association of State Highway and Transportation Officials 

(AASHTO) AASHTOWare® Pavement ME DesignTM (PMED) software for use in local 

calibration and other studies (AASHTO 2020). 

The LTPP program has a wealth of materials information and data stored in the Pavement 

Performance Database (PPDB). Almost every test section in the program had materials sampling 

and testing conducted to ascertain the thickness and material types of the constituent layers. In 

addition, a well-structured laboratory materials testing program was undertaken to further 

classify and characterize the layers (Simpson, Schmalzer, and Rada 2007; Puccinelli et al. 2022).
2 

However, the existing database of material properties had limitations, which include the 

following: 

• Data are distributed across dozens of tables in the LTPP database, making some data 

elements hard to find, particularly for those not thoroughly familiar with the PPDB. 

• There are multiple values for a given layer, making it difficult to discern which 

representative value(s) to use.

• Materials characterization data are missing for one or more pavement layers for a number 

of LTPP test sections.

• Some data have to be further interpreted to enable meaningful results usable as inputs to 

the PMED software and other pavement design/analysis applications. 

The acquisition and interpretation of materials data required expert-level knowledge of the LTPP 

experiments’ materials sampling and testing requirements, as well as their data classification and 

characterization. This high level of required knowledge created a barrier to implementation for 

practitioners and researchers. Consequently, an LTPP user would typically need to spend 

substantial effort finding, extracting, merging, and interpreting the available data to develop a 

suitable analysis dataset.

Therefore, the program developed a process to generate the LTPP Analysis-Ready Materials 

Dataset (ARMAD) for each test section and each layer in the LTPP database. ARMAD solves 

many of the challenges listed in the preceding paragraphs, including the following: 

• Consolidating the number of tables from many to one main material properties table and 

one supporting data table for each material type. 

• Creating one representative value for each material property and providing a relative 

variability scale for these values.

• Developing representative materials characterization data for every test section and every 

layer within LTPP. 

• Interpreting the data to produce certain meaningful engineering properties for selected

materials (such as subgrade resilient modulus). 

The collective set of ARMAD tables contain more than 1 million records. ARMAD solves the 

stated challenges for the layer structure and material properties of all test sections in the LTPP 

database, except for the SPS-10 warm mix asphalt test sections. Testing for these SPS-10 test 

sections is presently ongoing, and the results will be incorporated into ARMAD upon 

completion. 

This document provides the details concerning how ARMAD material properties were selected, 

how the database was populated, and what the resulting dataset contains. It should be used by 

researchers who will utilize the ARMAD dataset for their analysis. This document also provides 

references and resource documents to understand the overall LTPP materials sampling and 
3 

testing program. Future planned Analysis-Ready Datasets (ARDs) for climatic conditions, 

traffic, and performance data will be incorporated into upcoming Standard Data Releases (SDRs) 

on the InfoPaveTM website (FHWA 2022c). 

ARMAD OBJECTIVE AND SCOPE

The objective of the ARMAD is to present a single representative value for the material 

properties of several categories of pavement layers—asphalt concrete (AC) materials, portland 

cement concrete (PCC) materials, unbound granular base/subbase, stabilized base/subbase, 

subgrade, and other layers, such as surface treatments and engineering fabrics (EF). The scope of 

this effort was to identify an essential set of material properties for each type of pavement layer, 

assemble and process the data of the selected material properties, assess the percentage of 

missing values in the selected material properties gathered from the LTPP database, impute or 

assume missing material properties, and populate the ARMAD. In addition, a variability and 

source indicator was assigned to each material property so users can understand the method used 

to obtain the material property, and hence the associated data variability. 

A group of subject matter experts (SMEs) from academia and industry provided guidance for this 

effort. Their expert opinion was integral to key decisions made regarding mining data, 

developing an approach, populating ARMAD, and reviewing the materials dataset and 

documentations from both technical and practical standpoints. 

LTPP MATERIALS TESTING PROGRAM SYNOPSIS

To better understand the foundation of the ARMAD data elements, it is important to review the 

history of LTPP’s materials sampling and testing activities. LTPP materials data were needed to 

define the properties of structural pavement layers for every test section within the program. The 

field sampling efforts were performed in accordance with the SHRP-LTPP Guide for Field 

Materials Sampling, Testing, and Handling, Operational Guide No. SHRP-LTPP-OG-006 

(Strategic Highway Research Program (SHRP) 1991). Each GPS experiment had a sampling 

plan specific to that experiment. The field guide provided each of these experiment sampling 

plans and covered the requirements associated with sample naming, labeling, identification, and 

shipping. The SPS projects differed from the GPS test sections in that each SPS project 

incorporated multiple colocated core test sections and, for selected projects, one or more some 

supplemental test sections at a location. Field sampling and laboratory testing plans were tailored 

for each specific SPS project based on the general set of sampling and testing plans for SPS 

experiments. Please see chapter 9 of this report, “Additional Reading,” for a list of reference 

documents. 

Testing for the LTPP program was typically carried out in accordance with the SHRP-LTPP 

Interim Guide for Laboratory Materials Handling and Testing (PCC, Bituminous Materials, 

Aggregates, and Soils), Operational Guide No. SHRP-LTPP-OG-004 (SHRP 1992). The guide 

was first released in November 1989, and the latest version was released in 1992. 

Beginning in 1992, under FHWA management, testing efforts were consolidated under two 

contracts. The objective of these contracts was to perform testing of the SPS projects and 

complete the resilient modulus testing for the GPS test sections. Additional testing to be 
4 

performed under these contracts included testing of any overlays constructed on the GPS test 

sections as time progressed. SPS projects that received overlays and stayed in study were 

converted to GPS test sections. However, due to a lack of funding, the testing requirements were 

reduced to the resilient modulus and supporting tests for the SPS projects and GPS test sections. 

Because of these funding limitations, the remainder of the testing requirements were taken on by 

the State departments of transportation and Provincial agencies constructing the SPS projects and 

GPS overlays. 

As part of a program assessment conducted in the mid-1990s, a review of the available materials

data indicated that there were gaps—missing material test results— in the available data. An 

effort was undertaken to fill in these gaps and improve the overall quality of the available data, 

which was termed the Materials Action Plan. 

Lastly, the SPS-10 experiment was implemented in 2014, and a laboratory contractor was 

engaged to conduct the materials sampling and testing for this experiment. At the time of writing 

of this report, the SPS-10 materials testing is ongoing, and the ARMAD dataset for SPS-10 is

planned to be delivered for the SDR in 2023. 

GPS AND SPS MATERIALS SAMPLING AND TESTING APPROACH

This section explains the general approach to materials sampling and testing for GPS and SPS 

sections. The details regarding the GPS and SPS materials sampling and testing programs can be 

reviewed through the documents in chapter 9 of this report, “Additional Reading.” For a 

description of each experiment, please see the Long-Term Pavement Performance Information 

Management System User Guide, page 4 (GPS) and page 5 (SPS), available on the home page of 

the LTPP InfoPave website (Elkins and Ostrom 2021; FHWA 2022a).

GPS Test Sections

A typical materials sampling plan for a GPS section is shown in figure 1. In general, materials 

were sampled from the approach end and the leave end. Therefore, often for each layer, at least 

two samples or specimens were taken and tested: one from the approach end and one from the 

leave end. Typically, no destructive field sampling was conducted within the section, as this 

sampling could impact the performance (and hence performance measurements) of the test 

section.

The test results for the GPS sections represent different properties at the time of sampling and 

testing and not the time of construction, as these were already in-service test sections that were 

not constructed specifically for the LTPP program. 
5 

Source: FHWA. 

Figure 1. Illustration. Typical sampling areas for an LTPP GPS test section. 

SPS-1, SPS-2, SPS-5, SPS-6, SPS-7, SPS-8, SPS-9, and SPS-10 Test Sections

For the SPS studies, which consist of multiple test sections on a given section of roadway, a 

different approach was taken. In general, for all SPS projects other than SPS-3 and SPS-4, 

several samples of the different layers and materials were extracted and tested and then used to 

represent the entire project. For example, for unbound materials, three samples (sometimes more 

or less depending on the individual project and the variability of the layers) were taken to 

represent the entire project. These samples were tied to a particular test section, as illustrated in 

figure 2 for an example SPS project. In this example, three bulk samples of the subgrade and 

dense-graded aggregate base were taken within the project adjacent to sections 1, 3 and 4, and 5 

and 6. Testing from these three locations was then tied to the other SPS test sections. For bound 

layers, a specimen was typically obtained and tested for each layer for each test section; the full 

suite of materials tests was conducted at each location. For the SPS-1, SPS-2, SPS-8, SPS-9, and 

SPS-10 projects, materials were sampled and tested during the construction process. 

For each individual SPS project site, the LTPP program developed custom tailored materials 

sampling and testing plans, which can be downloaded from 

https://infopave.fhwa.dot.gov/Reports/Library (FHWA 2022e). 
6 

Source: FHWA. 

Figure 2. Illustration. Example unbound materials sampling areas for an LTPP SPS 

project. 

SPS-3 and SPS-4 Test Sections

For SPS-3 and SPS-4 projects, there was generally no sampling or testing of the layers, as this 

was a maintenance experiment and only the surface of the pavement was of concern. However, 

each SPS-3 and SPS-4 site often had a GPS test section (which was used as a control section) 

near it (figure 3). The material properties measured for the GPS section were used for the SPS-3 

and SPS-4 test sections. 

Source: FHWA. 

Note: The distance between GPS and SPS test sections varied.

Figure 3. Illustration. Typical sampling areas for an LTPP SPS-3 or -4 project. 
7 

REPORT ORGANIZATION

The remainder of this report is divided into the following sections: 

• Chapter 2. Example Use Cases of the LTPP Analysis-Ready Materials Dataset. 

• Chapter 3. General Approach to Development of the Materials Analysis-Ready Dataset. 

• Chapter 4. Generation of Unbound Materials Analysis-Ready Dataset. 

• Chapter 5. Generation of PCC Materials Analysis-Ready Dataset. 

• Chapter 6. Generation of Asphalt Concrete Materials Analysis-Ready Dataset. 

• Chapter 7. Generation of Treated Materials Analysis-Ready Dataset. 

• Chapter 8. Generation of Other Materials Analysis-Ready Dataset. 

• Chapter 9. Additional Reading. 

• References. 

9 

CHAPTER 2. EXAMPLE USE CASES OF LTPP ANALYSIS-READY MATERIALS

DATASET

The ARMAD has many potential uses in the engineering profession. As mentioned in chapter 1, 

ARMAD is a summary of the vast amount of material property information contained in the 

LTPP database, along with inputted and calculated values. These material properties were 

selected based upon standard engineering properties needed to classify layers and properties 

needed to calibrate and run pavement performance and design software, with a focus specifically 

on the PMED software (AASHTO 2020).

Examples of the types of studies that can be performed with the ARMAD include the following: 

• Developing a materials dataset for a selection of test sections as part of a broader 

pavement research study.

• Conducting materials data studies of in-service pavements.

• Developing correlations and prediction models (regression, machine learning, etc.) 

between material properties.

• Using the values contained in the ARMAD to perform pavement materials analysis, 

including laboratory to field comparisons and advanced materials testing analysis 

(resilient modulus, dynamic modulus). 

• Using the material properties for local calibration of the PMED software 

(AASHTO 2020). 

• Evaluating the impact of material properties on pavement preservation, maintenance, and 

rehabilitation.

USE CASES

The following two example use cases illustrate some of the potential uses of ARMAD. 

Use Case 1: Materials Study

In this example, ARMAD is used in a simple materials study to evaluate air voids. Air voids 

have a significant effect on the performance of asphalt pavements. Air voids that are too low can 

cause bleeding, rutting, and shoving. High air voids, on the other hand, can lead to an increased 

potential for water infiltration, accelerated oxidation, raveling, cracking, and rutting in the 

wheelpath. 
10 

Percent air voids is calculated by comparing a test specimen’s bulk specific gravity (BSG) with 

its theoretical maximum specific gravity and assuming the difference is due to air. To determine 

the air voids of a particular section, the equation given in figure 4 is used: 

Figure 4. Equation. Calculation of percent air voids. 

Where:

Gmm = maximum specific gravity. 

Gmb = BSG.

As discussed in chapter 1, GPS test section are sampled as illustrated in figure 5. 

Source: FHWA.

Figure 5. Illustration. Partial depiction of sampling for asphalt layers for a GPS test 

section.

For this test section, 4-inch-diameter cores were drilled and obtained at the C-type locations (C1 

to C12) in the outer wheelpath (C4–C6 and C10–C12) and in the midlane (C1–C3 and C7–C9). 

Six-inch-diameter cores were taken from the A1 and A2 locations. The C-type cores were used 

to measure BSG, and the A-type cores used to measure maximum specific gravity. 

One approach to determining the air voids for the section is to combine the individual core 

results from C1 to C3 with the A1 result and the individual core results from C4 to C6 with the 

A1 result to obtain wheelpath and midlane air voids for one end of the section, which results in 

six air void values per asphalt layer tested. The results of the other end can be developed in a 

similar fashion, resulting in a total of 12 air void values per asphalt layer tested. The average of 

the ends of the section could be used to determine the wheelpath and midlane air voids for each 

asphalt layer tested in the section. 

For those not intimately familiar with the LTPP database and the materials sampling and testing 

plans, this task could be daunting—to develop air void content values for thousands of test 

sections, many with multiple asphalt layers. Accordingly, ARMAD has been developed to 

perform this task for the user. In the AC dataset, wheelpath and midlane section-level air void 

content results are presented to the user as well as statistics, such as the minimum, maximum, 

and average values used in the calculation.
11 

Use Case 2: Materials Engineering Research Project

In this use case, the first example is expanded to include other material properties. In this 

example, the user plans to perform a materials engineering study to determine the effects of 

gradation and air voids of the asphalt mix on dynamic modulus for an SPS-1 (Strategic Study of 

Structural Factors for Flexible Pavements) project (SHRP 1990a). To perform this study, the user 

must have air voids (as described in use case 1), the asphalt aggregate gradation, and a value for 

the dynamic moduli. 

As noted in chapter 1, SPS projects did not have a full suite of sampling and testing performed 

on each test section. Figure 6, which shows a partial depiction of three sections from an SPS-1 

site. In this figure, the C-type cores are 4-inch-diameter cores and the B1 is a bulk sample of 

asphalt taken from the paver. The C-type cores were used to measure BSG, and the bulk sample 

was used to measure gradation and maximum specific gravity. The dynamic modulus is 

calculated using the method from a previous study and is a project-level value (Kim et al. 2011). 

This method results in the matrix of test results presented in table 1 for this theoretical section.

Source: FHWA.

Figure 6. Illustration. Partial depiction of sampling for asphalt layers for an SPS-1 project.

Table 1. Matrix of test results for a theoretical partial SPS-1 project.

Section Test Result

Project level Dynamic modulus 

Section 1 4 BSGs 

Section 2 4 BSGs 

1 Maximum specific gravity

1 Gradation

Section 3 4 BSGs 

To arrive at the required dataset, it is necessary to calculate the air voids for each section, use the 

gradation from section 2, and use the project-level dynamic modulus. This process becomes 

significantly more complicated when the entire 12 sections of an SPS-1 project are used.
12 

In summary, the ARMAD has been developed to provide these values in an easy-to-use format. 

For example, it provides one air void value for each wheelpath and midlane, contains a 

representative set of gradation values for each test section, and provides one dynamic modulus 

value per test section. Therefore, it has decreased the necessity to individually calculate these 

values for each test section, which would have required the following: 

1. Studying the sampling and testing plan for each individual SPS-1 project. 

2. Interpreting which test results belong with each test section. 

3. Consolidating data from several tables in the LTPP database. 

4. Deriving representative values (i.e., air voids, gradation, etc., for the use case no. 2). 

5. Performing the analysis (i.e., performing the statistical analysis to evaluate the mixture 

properties correlation and their impact on the dynamic modulus). 

The ARMAD eliminates the first four steps, which will lead to more efficient and effective 

research studies.

CONSIDERATIONS

Caution should be taken when using ARMAD, and the assumptions used to populate the dataset 

should be fully understood prior to using it. The ARMAD is a compilation of many data tables 

and values, including those directly measured, calculated, expanded, imputed, derived from 

inventory data, and assumed (as described in chapter 3, table 4). The purpose of developing this 

dataset was to make LTPP data extraction and use more accessible for LTPP users, using the best 

assumptions and algorithms known to LTPP at the time. 

The following are several considerations to the use of the materials dataset that users should be 

aware of: 

1. The material properties may not be measured values.

2. Material properties do not exist for some layer types.

3. Backcasted values contain many assumptions. 

4. Other methods may exist to generate computed values. 

Issues discovered with the data should be documented with a Data Analysis/Operations 

Feedback Report (DAOFR), as described at the end of this chapter.

Material Properties Values

The user should be aware that many of the values present in the dataset are not measured 

properties. The methods used to determine the material properties (as described in chapter 3, 

table 4) include the following: 

• Measurements.

• Calculations. 

• Expansion. 

• Imputation. 

• Use of inventory values. 

• Assumed values. 
13 

As explained in chapter 3, the method used to generate each value is described in the dataset 

using the REP_CODE_SOURCE_VARIABILITY field. When using the ARMAD, it is 

important the user understand the method(s) used to populate the dataset with the layer properties

that are to be used in any analysis.

Material Properties Do Not Exist for All Layers 

Cases in which material properties do not exist for all the layers are the result of when there were 

no means to adequately use one of the methods listed previously to determine the value. 

Although every attempt was made at determining a complete set of material properties, in some 

cases this was not possible. 

Assumptions for Backcasted Values

The PCC dataset contain values that are backcasted from the measured ones. For example, the 

PMED software requires PCC compressive or flexural strength values at 28 d as an input. The 

28-d input value was not available for the LTPP experiments, with the exception of the SPS-2 

experiment (Strategic Study of Structural Factors for Rigid Pavements) (SHRP 1990b). 

Therefore, the measured value at the time of testing was used to backcast the 28-d compressive 

strength using the algorithms selected by the project SMEs. These backcasted values contain 

many assumptions and were developed using methods the project SMEs approved. 

Computed Values 

Some of the ARMAD values were calculated. One prominent example is the calculation of the 

design resilient modulus of unbound materials. In the LTPP database, design resilient moduli for 

test sections were not calculated. Therefore, an adequate method was selected to calculate the 

design resilient modulus. The details of this calculation are presented in chapter 4. However, 

users may wish to use other models to derive calculated values. It is recommended that users 

should review and understand the methodology used to derive each ARMAD value before use.

Data Analysis Operations Feedback Reports 

Users who discover potential problems with the ARMAD or the calculation of the material 

properties can file a DAOFR. The DAOFR form can be found on the LTPP InfoPave website at 

https://infopave.fhwa.dot.gov/Data/DAOFRForm (FHWA 2022b). Alternatively, users can

report issues to the LTPP Customer Support Service Center via email at LTPPInfo@dot.gov. 

15 

CHAPTER 3. GENERAL APPROACH TO DEVELOPMENT OF THE MATERIALS 

ANALYSIS-READY DATASET

Chapter 3 describes the basic process used to develop the LTPP ARMAD. This process is then 

refined in chapters 4 through 8 for each type of material under investigation (PCC, AC, etc.). 

This process may be adjusted or repeated in future years with addition of more data. The refined 

process for each material group was developed to be as automated as possible (using an

open-source programming language), with limited engineering judgment, imputation, or manual 

intervention. In addition, the process utilized four SMEs to guide the development efforts. Each 

step of the process was approved via consensus among FHWA, LTPP contractors, and SMEs 

before data release. 

The ARMAD was developed using a phased approach by material type, in the following order: 

1. Unbound materials (subgrade and unbound base/subbase). 

2. PCC layers. 

3. AC layers. 

4. Treated material (asphalt-treated and cement-treated base (CTB)/subbase). 

5. Fabrics and interlayers. 

The development process for each type of material followed the five steps listed as follows and 

discussed further in this chapter: 

1. Selecting material properties to be included in ARMAD. 

2. Data mining to identify the source table(s) housing the data in the LTPP database and 

the extraction of data for each test section and layer.

3. Deriving representative values via a decision tree. 

4. Populating the LTPP database with the material properties data.

5. Disseminating the final dataset via the LTPP InfoPave web portal.

SELECTION OF MATERIAL PROPERTIES

The starting point for developing ARMAD was identifying the key pavement layer material 

properties by layer type. ARMAD contains an essential set of material properties, as established 

by the SMEs, necessary to characterize each layer and effectively analyze pavement performance 

or calibrate pavement performance models and transfer functions, specifically (but not 

exclusively) for the PMED software. The criteria for selection of these properties are described 

in the following sections. 

Fundamental Material Properties

Fundamental layer structure and material properties—such as layer thickness, material type, 

gradation, volumetric properties, strength properties, and so forth—were included in the dataset. 

These properties are essential to understanding the layered composition of test sections and their 

representative material characteristics. 
16 

Importance to Pavement Performance Analyses

To help focus the effort on the most important material characteristics, the material properties 

inputs for the AASHTO Mechanistic-Empirical Pavement Design Guide (MEPDG) local 

calibration, verification, and validation were used as a yardstick (AASHTO 2020). LTPP data are 

often used for calibrating MEPDG distress transfer functions or MEPDG performance analysis. 

These material properties are grouped based on the pavement layer material categories 

mentioned previously (PCC, AC, etc.). 

Usefulness in Computing Other Data Elements 

Some of the fundamental material properties, such as AC dynamic modulus and resilient 

modulus for unbound granular materials, were not measured on every pavement section or layer, 

due to the tests not being a routine production level at the time many of the LTPP test sections 

were sampled and tested or due to limited resources for performing laboratory tests. In the 

absence of the laboratory test results, correlation equations, prediction models, or both provided 

an alternative for estimating these properties based on other material properties. ARMAD also 

contains correlated material properties, which are useful in calculating missing properties and 

developing newer correlation equations. 

Key Properties Availability

Table 2 presents the list of key material properties selected for representation in ARMAD. A 

review of the LTPP database was conducted to identify the availability of each selected material

property. The results are shown in the third column of table 2, which indicates whether a given 

material property is collected by the LTPP program. Even if a material property is collected by 

the LTPP program, the indicated material property will still be missing from some layers for a 

given class of experiment (GPS or SPS), individual experiment, or test section. 
17 

Table 2. List of key material properties

Category Material Element

Collected by the 

LTPP Program

Unbound 

base/subbase/subgrade 

materials

Layer thickness Yes

Material type Yes

Poisson’s ratio No

Soil classification (gradation and Atterberg 

limits)

Yes

Compaction characteristics (optimum moisture 

content and maximum dry density) 

Yes

Specific gravity Yes

Resilient modulus parameters Yes

Saturated hydraulic conductivity Yes

PCC mixture Layer thickness Yes

Material type Yes

Poisson’s ratio Yes

Modulus of rupture Yes

Modulus of elasticity Yes

Compressive strength Yes

Tensile strength Yes

Coefficient of thermal expansion Yes

Mixture properties (unit weight, water-tocement ratio)Yes

PCC shrinkage No

Thermal conductivity No

Heat capacity No

PCC zero-stress temperature No

Surface shortwave absorptivity No

Steel in concrete properties (diameter, depth, 

spacing, etc.) 

Yes

AC mixture and 

asphalt-treated layers

Layer thickness Yes

Material type Yes

Poisson’s ratio Yes

Asphalt binder complex shear modulus and 

phase angle—set of values at different 

frequencies and temperature to create master 

curve 

Yes

Binder type Yes

Viscosity Yes

Dynamic modulus—set of values at different 

temperatures and frequencies to build a master 

curve 

Yes

Tensile strength Yes

Creep compliance Yes
18 

Category Material Element

Collected by the 

LTPP Program 

Volumetric properties (unit weight, BSG, 

maximum specific gravity, aggregates specific 

gravity, air voids, binder content, VMA, VFA) 

Yes

Aggregate gradation Yes

Thermal conductivity No

Heat capacity No

Aggregate coefficient of thermal contraction No

Surface shortwave absorptivity No

Chemically stabilized 

materials

Layer thickness Yes

Material type Yes

Poisson’s ratio No

Aggregate type Yes

Treatment type and details Yes

Heat capacity No

Thermal conductivity No

Resilient/elastic modulus No

Compressive strength Yes

Surface treatments and 

EF

Layer thickness Yes

Material type Yes

VFA = voids filled with asphalt; VMA = voids in the mineral aggregate.

DATA DISCOVERY

The next step in the process was to determine data availability in the LTPP database by GPS and 

SPS experiment. The basis for selection of the layers to be populated was the LTPP layering 

table, TST_L05B. The TST_L05B table was selected as the base table, given the previous 

completion of a rigorous section-by-section layering reconciliation process to identify the most 

representative source of information for layer thickness, material type, and material 

classification. Layer thicknesses for ARMAD were obtained directly from this table.

The LTPP key fields used to identify the section (STATE_CODE and SHRP_ID), associated 

construction events (CONSTRUCTION_NO), layer structure (LAYER_NO), layer material type 

(LAYER_TYPE, PROJECT_LAYER_CODE, and MATL_CODE), and layer thickness 

(REPR_THICKNESS) were retrieved from the TST_L05B table. 

To extract data for an available material property from the LTPP database, the LTPP tables 

containing the indicated property were selected. The Long-Term Pavement Performance 

Information Management System User Guide was used extensively to confirm the relevant LTPP 

data tables (Elkins and Ostrom 2021).

For each material property indicated as available, a preliminary list of the appropriate LTPP 

tables was developed. Next, each property and each table were evaluated to identify the tables 

with the most representative value to include in the dataset. This selection varied based on the 

material property that was being evaluated. Finally, the table with the most representative values 

was selected along with secondary tables if they existed. 
19 

DERIVING REPRESENTATIVE VALUES

After the appropriate tables were selected, a decision tree was developed for each material type 

and data availability scenario to use as a basis to code the algorithms for developing the 

representative values. Statistical procedures were used to evaluate the data variability and select 

the most representative values or predict missing values. These decision trees are explained 

further in chapters 4 through 6 of this report.

For EF layers and other treated material layers (such as surface treatments), no laboratory testing 

was conducted, and only the representative thickness, material code, and layer description were 

populated directly from the TST_L05B table. 

POPULATING ARMAD IN THE PPDB

The final data and metadata resulting from the ARMAD effort have been stored in the LTPP 

PPDB. For each material type, two new tables were created: the primary data table, 

ANALYSIS_TST_* (where * = the material type, such as AC, PCC, etc.) and the data support 

table, ANALYSIS_TST_*_SUPPORT. The metadata contains the data dictionary, table 

dictionary, and a description of field codes. 

Primary Data Table: ANALYSIS_TST_* 

Each ANALYSIS_TST_* table contains the representative layer properties for all relevant LTPP 

GPS and SPS layers. Practitioners and researchers can use this table as the LTPP representative 

value for each data element and pavement test section. 

Data Support Table: ANALYSIS_TST_*_SUPPORT

The ANALYSIS_TST_* _SUPPORT table contains data statistics and the number of samples 

tested for each representative layer and layer property. The purpose of this table is to inform the 

user as to the variability and the method that was used to populate ARMAD. For each material 

property, the table contains the average, median, minimum, maximum, standard deviation (SD), 

coefficient of variation (COV), number of samples used in the calculation, and the source 

variability code of the value, as appropriate. 

Data Tables 

Table 3 contains a list of the actual tables created in the database. The collective set of tables 

contains more than a million records. The record count contained in ARMAD tables will 

increase with the incorporation of the SPS-10 laboratory testing data in future SDRs. 
20 

Table 3. List of tables created for ARMAD in the PPDB.

No. Table Name Description

1 ANALYSIS_TST_UNBOUND Representative properties of unbound 

base, subbase, and subgrade materials. 

2 ANALYSIS_TST_UNBOUND_SUPPORT Support information for the unbound 

base, subbase, and subgrade material 

properties.

3 ANALYSIS_TST_PCC Representative properties of PCC

materials.

4 ANALYSIS_TST_PCC_SUPPORT Support information for the PCC

material properties.

5 ANALYSIS_TST_AC Representative properties of AC

materials.

6 ANALYSIS_TST_AC_SUPPORT Support information for the AC material 

properties.

7 ANALYSIS_TST_AC_ESTAR AC dynamic modulus data generated 

from an analysis study (Kim et al. 2011). 

No support table presently exists.

8 ANALYSIS_TST_AC_CREEP_COMP AC creep compliance testing properties.

9 ANALYSIS_TST_AC_CRCOM_SUPPORT Support information for the AC creep 

compliance values.

10 ANALYSIS_TST_ACT Representative properties of ATB base 

and subbase testing materials.

11 ANALYSIS_TST_ACT_SUPPORT Support information for the ATB base 

and subbase material properties.

12 ANALYSIS_TST_PCT Representative properties of CTB base 

and subbase materials.

13 ANALYSIS_TST_PCT_SUPPORT Support information for CTB base and 

subbase materials.

14 ANALYSIS_TST_TR Representative properties of treatment 

layers.

15 ANALYSIS_TST_TR_SUPPORT Support information for treatment layer 

types.

16 ANALYSIS_TST_EF Representative properties of EF layers.

17 ANALYSIS_TST_EF_SUPPORT Support table for EF layers.

ATB = asphalt-treated base.

Contents of Each ARMAD Material Property Table 

In general, the contents of each material property table include a description of the section

location, layer location in pavement structure, layer type, and construction/maintenance history 

(CONSTRUCTION_NO). These contents also include information on all material properties for 

a given layer. Therefore, the user can determine and use the materials information derived from

ARMAD for each layer of the test section. These will likely be the tables of main interest to

researchers, as they provide the most representative value for a given material property as vetted 

by the SMEs. 
21 

Contents of Each ARMAD Support Table

In general, the contents of each material support table include a set of key fields identifying the 

layer within the section and the State or Province, a description of the layer, and the basis for the 

material property value. The support table also includes the minimum, maximum, average, 

median, SD, COV, and sample count for each material property and each layer, as applicable. 

The user can merge the support table with the ARMAD material property table using the set of 

key fields to find statistics for each data field and further filter the data.

The data element termed REP_CODE_SOURCE_VARIABILITY in the 

ANALYSIS_TST_*_SUPPORT tables is an important element, as it informs the user of the 

variability and derivation method of the individual material property. Said element is a one- or 

two-character code. The first character specifies the data source code, as detailed in table 4, 

while the second character, when present, characterizes the representative source code 

variability. If the second character is not present, the variability is unknown or not applicable. 

Table 4. Data source code descriptions.

Data Source 

Code Description

A Properties directly measured in the LTPP program by using field or 

laboratory materials sampling and testing. 

B Properties calculated by using measured parameters and assumed conditions. 

C Properties expanded to similar sections in an LTPP experiment by using the 

PROJECT_LAYER_CODE. 

D Properties estimated by using statistical methods and typical models. 

E Properties adopted from inventory or maintenance/rehabilitation data. 

F Properties assumed by basing them on the MEPDG or other reliable sources. 

Table 5 provides a schematic of the classification system used for 

REP_CODE_SOURCE_VARIABILITY. In general, as the code goes from A to B to C and so 

on, the less confidence there is in the value; however, in all cases, the best value was selected for 

a particular layer and particular material property.
22 

Table 5. Classification system used for REP_CODE_SOURCE_VARIABILITY field.

Data Source 

Group Description

Data Source 

Code Subgroups

REP_CODE_SOURCE_

VARIABILITY

Measured

Properties directly measured by

using standard testing in LTPP 

program.

A 

Variability unknown or not applicable. A 

More than one measured value with low COV. AL

More than one measured value with high COV. AH

Single value measured. AS

Calculated

Properties calculated by using 

measured parameters and 

assumed conditions.

B 

Variability unknown or not applicable. B 

Calculated from more than one measured parameter with low 

COV. BL

Calculated from more than one measured parameter with high 

COV. BH

Calculated from one measured parameter. BS

Expanded

Properties expanded to similar 

sections in an LTPP experiment 

using the 

PROJECT_LAYER_CODE.

C 

Variability unknown or not applicable. C 

More than one measured data from similar sections, low COV. CL

More than one measured data from similar sections, high COV. CH

One data point from similar sections. CS

Imputed

Properties estimated by using 

statistical methods and typical

engineering models.

D 

Variability unknown or not applicable. D 

Imputed from more than one measurement with low COV. DL

Imputed from more than one measurement with high COV. DH

Imputed from one measured value. DS

Inventory

Properties adopted from 

inventory representing agency 

typical practices.

E 

Variability unknown or not applicable. E 

Inventory median for more than one value with low COV. EL

Inventory median for more than one value with high COV. EH

Inventory single value. ES

Assumed

Properties estimated by basing 

them on the MEPDG or other 

reliable sources.

F Assumed values. F 
23 

The specific value for each data source code is described the following sections. 

Measured Values—Code A

Material properties under this classification were directly measured using standard testing in the 

LTPP program. The following two primary scenarios were available:

• Layers with one specimen tested per layer: The table was populated with the single 

measured value.

• Layers with more than one specimen tested per layer: The layer was evaluated to 

determine if all tests were representative. If they were, an average or median (depending 

on data dispersion) was used. If not, then values were assessed to determine the most 

representative one(s) based on the project team’s technical expertise and logic.

For layers with a single measurement, a second character of “S” (for single) was added to the 

data source code, resulting in “AS.” Material properties with two or more measurements were 

divided into low COV and high COV based on thresholds identified from the data distributions 

and recommended by the SMEs, as appropriate for the property and material type. A second 

character of “L” (for low COV) or “H” (for high COV) was added to the data source, code 

resulting in “AL” or “AH.” If the variability was unknown or not applicable, a second character 

was not added to the data source code. Please note that the threshold value varies based on the 

layer type. The approach described in this paragraph was also used for the remaining data source 

codes (i.e., B to F). 

Calculated Values—Code B

Material properties within this classification were calculated using measured parameters and 

assumed conditions. In some cases, materials testing was conducted, but a final, representative 

value was not contained in the LTPP database. As an example, the unbound resilient modulus 

test has values for up to 15 stress sequences. The test was purposefully developed this way so 

users could derive their own resilient modulus values at a specified stress state. However, for the 

purposes of ARMAD, one value was desired, and thus it was calculated assuming typical stress 

states for different layers.

Expanded Values—Code C

Under this classification, the material properties for a given section were expanded from an 

adjacent LTPP section using the PROJECT_LAYER_CODE. Since SPS projects consist of 

multiple test sections, a project level layering structure was developed to keep track of pavement 

layering and test results from various test sections. The ultimate purpose of the project-level 

layering was to set up an accounting system to be used to link material tests for a given pavement 

layer in a particular section to other similar materials throughout the project (Simpson, 

Schmalzer, and Rada 2007). Therefore, the PROJECT_LAYER_CODE is an SPS project-level 

layer identifier and allows layers in different test sections on the same SPS project with the same 

material properties to be identified (Elkins and Ostrom 2021). In some cases, such as most SPS-3 

and SPS-4 test sections, the materials on the test sections were not themselves sampled. The 
24 

material properties may be derived from an adjacent GPS test section that was sampled and 

tested, as SPS-3 and SPS-4 test sections typically had GPS test sections nearby. 

Imputed Values—Code D

Imputed values are properties that were estimated using engineering models or statistical 

methods. For example, the gradation of unbound materials not tested was imputed by deriving 

the mean percent passing of sieves based on layers with the same material type in the LTPP 

database. The appropriateness of the gradation was then checked by comparing the imputed soil 

classification against the classification for the same material type.

Inventory Values—Code E 

When testing was not conducted, or a material test did not otherwise have a value for a given 

layer, in some cases inventory values were used. Inventory values are data gathered from agency 

records or agency specifications or from reported maintenance/rehabilitation data. These values 

were used on a case-by-case basis.

Assumed Values—Code F 

The default input values from the PMED software (NCHRP 1-37A and AASHTO 2020 MEPDG 

Manual) were used for values for which there were no data in the LTPP database

(AASHTO 2020; ARA, Inc., ERES Consultants Division 2004). This classification does not 

have a COV designation as these are assumed values, so it is a one-character value.

Tracking Changes in Pavement Structure Over Time

Each time an LTPP test section changes its characteristics due to rehabilitation treatments or the 

application of maintenance treatments, it is assigned a new construction number (CN) in the 

TST_L05B table. When a pavement section is first accepted into LTPP, it is assigned a CN of 1. 

CN is incremented by 1 for each successive maintenance or rehabilitation event. To perform the 

LTPP ARMAD process, it was necessary to assign material properties to all layers in the section 

for all CN events.

ARMAD QUALITY CONTROL

The data collected by the LTPP program are reviewed per the LTPP Information Management 

System (IMS) Quality Control Checks, available to download from the InfoPave website on the 

Library page (FHWA 2013, 2022e). In addition, the following lists of quality control checks 

were performed on the ARMAD dataset. 
25 

Data Structure Checks 

1. Confirm that the properties approved by the ARMAD SMEs were included and loaded into 

the LTPP database (aka PPDB). 

2. Ensure no reference tables contain measured values by the LTPP program for expanded, 

imputed, or missing data (i.e., fields that do not have 

REP_CODE_SOURCE_VARIABILITY = AS/AL/AH).

3. Cross-reference key fields in ARMAD with the TST_L05B table to ensure all layers are 

included (except for SPS-10 layers). 

4. Confirm that there are no duplicates in key fields per layer per CN.

5. Verify that ANALYSIS_TST_AC, ANALYSIS_TST_AC_ESTAR, 

ANALYSIS_TST_AC_CREEP_COMP, and their SUPPORT tables are properly linked 

together. 

IMS-Related Checks

1. Confirm that the existing fields in the LTPP database are named following the LTPP Data 

Dictionary (LTPPDD) nomenclature.

2. Verify that ARMAD’s existing fields in the LTPP database have the same units as in the 

LTPPDD.

3. Ensure that ARMAD’s newly created fields are added to the LTPPDD with the correct units, 

codes, and so forth. 

4. Confirm that all code types and codes exist in the LTPP database. The one exception is 

REP_CODE_SOURCE_ VARIABILITY, which is a new code type. 

Data Content Checks 

1. Check the range of property values for reasonableness: 

a. Check the distribution of values for each property. 

b. Check the distribution of REP_SOURCE_CODE_ VARIABILITY for that property 

(measured versus nonmeasured category).

c. Investigate and compare outliers and biases in data across experiments, studies, and 

States. 

2. If the ANALYSIS_TST_PCC table, if the ELASTIC_MOD_AGE > 28, then confirm the 

ELASTIC_MOD_28_DAY < ELASTIC_MOD. Similar checks apply for other strength 

properties. 

3. Verify that no property values for milled layers exist (REP_THICKNESS = 0).
26 

4. Confirm that data expansions were performed correctly: 

a. That data and corresponding REP_CODE_SOURCE_ VARIABILITY are expanded 

correctly to higher CNs. 

b. In case of missing data, that data are expanded accordingly using 

PROJECT_LAYER_CODE if present.

Support Table Checks 

1. Check that LAYER_COMMENT1, LAYER_COMMENT2, LAYER_COMMENT3, and 

COMMENT_NOTE fields in TST_L05B are translated properly into 

REP_CODE_SOURCE_ VARIABILITY for REP_THICKNESS field.

2. Confirm that PROPERTY_AVERAGE and PROPERTY_MEDIAN is equal or within 

PROPERTY_MINIMUM and PROPERTY_MAXIMUM in SUPPORT table.

3. Verify that when SAMPLE_COUNT = 1 then property’s PROPERTY_AVERAGE = 

PROPERTY_MEDIAN = PROPERTY_MINIMUM = PROPERTY_MAXIMUM.

4. Confirm that representative values in ANALYSIS_TST tables are equal to or within 

PROPERTY_MINIMUM and PROPERTY_MAXIMUM values of their corresponding 

MATL_PROPERTY in SUPPORT table.

5. Verify that SUPPORT table COEFF_VARIATION = STANDARD_DEVIATION / 

PROPERTY_AVERAGE for all records.

6. Check REP_CODE_SOURCE_VARIABILITY and confirm that: 

a. Second character of REP_CODE_SOURCE_VARIABILITY is “S” in SUPPORT table 

when SAMPLE_COUNT = 1. 

b. Second character of REP_CODE_SOURCE_VARIABILITY is not “S” in the SUPPORT 

table when SAMPLE_COUNT > 1. 

c. Second character of REP_CODE_SOURCE_VARIABILITY is “H” in the SUPPORT 

table when COEFF_VARIATION > threshold%. 

d. Second character of REP_CODE_SOURCE_VARIABILITY is “L” in the SUPPORT 

table if COEFF_VARIATION =< threshold%. 

e. Stats in SUPPORT table are null when REP_CODE_SOURCE_VARIABILITY is “F” 

(Assumed). 
27 

DISSEMINATING DATA

The LTPP program uses InfoPave to disseminate data and information to users. This data portal 

can be accessed using the web address https://infopave.fhwa.dot.gov/ (FHWA 2022a). The LTPP 

database tables explained in table 2 can be found in the Data hub under Standard Data Release 

on the LTPP InfoPave website, https://infopave.fhwa.dot.gov/Data/StandardDataRelease/

(FHWA 2022c).

The data are also available in the LTPP InfoPave portal under the Data Selection and Download 

feature at https://infopave.fhwa.dot.gov/Data/DataSelection (FHWA 2022d). This method of 

download allows users to access an easy-to-use intuitive interface to select and download the 

data of their choosing. 

Further information regarding dissemination of ARMAD, including coded database schema, 

database quality control, data dictionary, and codes, is included in the Long-Term Pavement 

Performance Information Management System User Guide (Elkins and Ostrom 2021).

29 

CHAPTER 4. GENERATION OF UNBOUND MATERIALS ANALYSIS-READY

DATASET

This chapter documents the development of the ARD for unbound granular base, subbase, and 

subgrade materials. Based on discussions within the LTPP program and input from the SMEs, 

the list of desired unbound granular material properties was presented in table 2 in this report. 

DATA DISCOVERY

Table 6 presents the LTPP database tables and data sources used to extract the selected unbound 

material properties and layer characteristics. The experiment and section information were

filtered to include unbound granular base, subbase, and subgrade material (LAYER_TYPE = 

GB, GS, and SS). 

Table 6. The LTPP data table or source to extract each material property.

Properties LTPP Data Table or Source 

Experiment and section information EXPERIMENT_SECTION

Layer thickness TST_L05B 

Resilient modulus TST_UG07_SS07_WKSHT_SUM 

Maximum dry density and optimum moisture 

content 

TST_UG05_SS05 

Gradation TST_SS01_UG01_UG02 

TST_SS02_UG03 

Soil classification TST_SS04_UG08 

Atterberg limits TST_UG04_SS03 

Specific gravity TST_UNBOUND_SPEC_GRAV

Saturated hydraulic conductivity TST_UG09, TST_SS11 

MEPDG default value 

Poisson’s ratio MEPDG default value

DERIVING REPRESENTATIVE VALUES

The decision tree shown in figure 7 summarizes the process to select the most representative 

material property for each LTPP layer. The complexities of establishing representative values 

can be observed in this decision tree. For example, the following three scenarios were available, 

depending on the data availability: 

• Layers with zero specimens tested. 

• Layers with one specimen tested. 

• Layers with more than one specimen tested. 
30 

Source: FHWA.

Figure 7. Flowchart. Unbound materials decision tree.
31 

The first scenario, layers with zero specimens tested, included cases for which an existing layer 

was not sampled and therefore not tested. For GPS test sections, this scenario generally occurred 

when the layer was too thin to obtain enough samples to test. For SPS sections, this scenario

mostly occurred when a particular test section did not have the materials sampled. For unbound 

layers in GPS sections with missing gradation or Atterberg limits data, the following procedure 

was used to impute the missing values: 

1. The MATL_CODE field was used to impute gradation and Atterberg limits. The median 

values of the properties from the same MATL_CODE for all LTPP data were assigned. 

The statistics of the properties used for imputation were included in the 

ANALYSIS_TST_UNBOUND_SUPPORT table.

2. The AASHTO classification was then generated from the imputed gradation and 

Atterberg limits; the resulting data were marked as “imputed” in the 

ANALYSIS_TST_UNBOUND_SUPPORT table.

3. The AASHTO classification was then used to impute other properties, such as optimum 

laboratory moisture content and maximum dry density. The median values for the LTPP 

materials (both SPS and GPS) with the same AASHTO classification were used. These 

fields were marked as “imputed” in the ANALYSIS_TST_UNBOUND_SUPPORT 

table. 

For unbound layers in SPS sections, the following process was used: 

1. If the SPS project had data for the same PROJECT_LAYER_CODE, the median value 

of the provided data for the SPS project was used to populate the 

ANALYSIS_TST_UNBOUND table for a missing layer property. 

2. If the project had no data for the same PROJECT_LAYER_CODE, then: 

a. If the materials code of that layer was close to another PROJECT_LAYER_CODE’s 

MATL_CODE in the same project, then the median of those properties was used for 

imputation.

b. Otherwise, the median values for the properties with the same MATL_CODE were 

used for populating the ANALYSIS_TST_UNBOUND table. 

The resulting data were marked as “imputed” in the ANALYSIS_TST_UNBOUND_SUPPORT 

table, and the statistics of the properties used for imputation were included in the table. 

For the second scenario, layers with one specimen tested, the ANALYSIS_TST_UNBOUND 

table was populated with the single measured value, and the field was marked as “measured” in 

the ANALYSIS_TST_UNBOUND_SUPPORT table.
32 

For the third case, layers with more than one specimen tested, the following process was used: 

1. If two or more specimens tested were from the same AASHTO soil classification, then 

the average of the measured properties was provided in the 

ANALYSIS_TST_UNBOUND table and marked as “measured” in the 

ANALYSIS_TST_UNBOUND_SUPPORT table. If the specimens were not from the 

same AASHTO classification, then the sample(s) that best represented layer properties 

according to their LTPP material code were selected for the 

ANALYSIS_TST_UNBOUND table and marked as “measured” in the 

ANALYSIS_TST_UNBOUND_SUPPORT table. 

2. In those rare cases in which none of the samples’ AASHTO classification matched the 

material code, the data were reviewed manually to consider sample properties, such as 

the percentage passing sieve number 200 and Atterberg limits. These fields were marked 

as “measured” in the ANALYSIS_TST_UNBOUND_SUPPORT table.

The statistics (minimum, maximum, median, etc.) of the data used for populating the 

ANALYSIS_TST_UNBOUND table were included in the 

ANALYSIS_TST_UNBOUND_SUPPORT table.

CALCULATED VALUES

The only value calculated for ARMAD was the design resilient modulus of unbound granular 

materials. A substantial amount of resilient modulus testing was carried out by LTPP for 

unbound materials. In each test, 15 resilient modulus values were generated, which corresponded 

to the 15 stress states (different confining pressures and deviatoric stresses) specified in the 

procedure (Simpson, Schmalzer, and Rada 2007). The equation in figure 8 was used to calculate 

the design resilient modulus. The k1, k2, and k3 parameters were calculated based on the test 

results, and the SD of residuals for k1, k2, and k3 values was determined as a measure of goodness 

of fit. The confining pressures listed in table 7 and the bulk stresses provided in table 8 for 

subgrade materials and table 9 for unbound base and subbase materials were used in the design 

resilient modulus computations (Rao et al. 2012). 

Figure 8. Equation. Calculation of resilient modulus. 

Where:

Mr = laboratory resilient modulus. 

Pa = atmospheric pressure. 

θ = bulk stress = σ1 + σ2 + σ3 = σd + 3σc. 

σ1 = major principal stress. 

σ2, σ3 = minor principal stress. 

σc = confining stress. 

σd = deviatoric stress = σ1 − σ3 = θ − 3σc. 

ζoct = octahedral shear stress = (√2/3) σd. 
33 

Table 7. Confining pressures for unbound granular design resilient modulus computations. 

Total Pavement Structural Thickness 

Above the Layer (inches) Confining Pressure (psi)

<12 2.0

12–23.9 2.5

24–32 3.0

>32 3.5

Table 8. Unbound granular subgrade bulk stress (θ value in figure 8). 

Pavement Type Total Bulk Stress (psi)

PCC 9

AC < 9 inches 9

AC > 9 inches 11

Table 9. Unbound granular base/subbase bulk stress (θ value in figure 8). 

Pavement Type Total Bulk Stress (psi)

PCC 10

AC < 3 inches 60

AC 3–5 inches 30

AC 5–9 inches 15

AC > 9 inches 10

If a given unbound granular layer was sampled and tested in the laboratory, the design resilient 

modulus was computed as detailed in this section. However, when the layer was not sampled, the 

k1, k2, and k3 regression parameters from layers with similar MATL_CODE or 

PROJECT_LAYER_CODE were used to calculate the design resilient modulus.

ASSUMED VALUES

The default values from the PMED software were used for the Poisson’s ratio and hydraulic 

conductivity when missing from the LTPP database. Table 10 shows the values that were used 

for a variety of materials.
34 

Table 10. Assumed Poisson’s ratio and hydraulic conductivity values used in the dataset.

LTPP Material Class Poisson’s Ratio Hydraulic Conductivity

River-run gravel 0.35 5.054e-02 

Permeable aggregate 0.35 5.054e-02 

Crushed stone 0.35 5.054e-02 

Crushed gravel 0.35 5.054e-02 

A-1-a 0.35 5.054e-02 

A-1-b 0.35 2.303e-03 

A-2-4 0.35 5.854e-04 

A-2-5 0.35 4.64e-07 

A-2-6 0.35 7.651e-06 

A-2-7 0.35 6.832e-06 

A-3 0.35 3.777e-03 

A-4 0.35 8.325e-06 

A-5 0.35 9.256e-07 

A-6 0.35 1.95e-05 

A-7-5 0.35 4.281e-06 

A-7-6 0.35 8.946e-06 

These values were input into ARMAD with the assumed label (code of “F”) for the 

REP_CODE_SOURCE_VARIABILITY in the ANALYSIS_TST_UNBOUND_SUPPORT 

table.

DATA SOURCE AND VARIABILITY

The REP_CODE_SOURCE_VARIABILITY in the ANALYSIS_TST_UNOUND_SUPPORT 

table was assigned as follows:

• Measured—labeled as “A.” For this classification, material properties having two or 

more measurements that were combined to generate a single value were divided into 

low-COV and high-COV thresholds based on a COV of 25 percent.

1 These properties 

were labeled as “AL” for low COV and “AH” for high COV. In cases in which only one 

measurement was available, an “S” was added to the variability code, resulting in “AS.” 

Finally, for measurements with unknown COV, such as representative layer thickness

(REPR_THICKNESS), a second character was not added to the variability code, leaving

a single character “A.” 

• Calculated—labeled as “B.” This classification was only used for resilient modulus data. 

The data were divided into low- and high-COV values depending on the COV of the k1, 

k2, and k3 regression parameters used to calculate the resilient modulus. If two or more 

laboratory test results were combined to generate the resilient modulus, then a character 

of “L” for low (less than 25 percent) COVs was added to the variability code, resulting in 

1

The COV of 25 percent was determined as the threshold based on a review of the overall LTPP data statistics 

and the recommendations of the ARMAD SMEs. 
35 

“BL.” If the COV was high (greater than 25 percent), then a character of “H” was added 

to the variability code, resulting in “BH.” If the value was calculated from a single 

measurement, the character of “S” for single was added to the variability code, resulting 

in “BS.” 

• Expanded—labeled as “C.” This classification was used for SPS sections’ properties that 

were expanded to similar sections in an LTPP experiment using the 

PROJECT_LAYER_CODE. Material properties based on two or more measurements 

from similar sections were divided into low and high COV based on a COV threshold of 

25 percent, which resulted in “CL” for low COV and “CH” for high COV. In cases in 

which the property was based on only one measurement, an “S” was added to the 

variability code, resulting in “CS.” For cases in which the COV was unknown, such as 

representative layer thickness, a second character was not added to the variability code, 

resulting in the single character of “C” only. 

• Imputed—labeled as “D.” Generally, the data were averaged based on the material 

properties available for all test sections having the same layer and MATL_CODE. Like 

previous classifications, a threshold COV of 25 percent was used to divide the imputed 

values into “DL” for low COV and “DH” for high COV. 

• Inventory—labeled as “E.” This code only applied to the representative layer thickness of 

unbound materials, in which values were obtained from the respective agencies. For 

properties where the value was derived from data from multiple agencies, the 

classification was divided into low- (“EL”) and high-COV (“EH”) values based on a 

COV threshold of 25 percent. If the value was calculated from a single inventory value, it 

was labeled as “ES.” 

• Assumed—labeled as “F.” This classification was used for Poisson’s ratio and hydraulic 

conductivity data, as values for these two properties were not generally available in the 

LTPP database. This classification does not have a COV designation, as they are assumed 

values; i.e., only the single character of “F” is used. 

37 

CHAPTER 5. GENERATION OF PCC MATERIALS ANALYSIS-READY DATASET

This chapter documents the development of the ARD for PCC materials. The list of key PCC 

material properties selected for inclusion in ARMAD was presented earlier in the report in

table 2. 

DATA DISCOVERY

Table 11 presents the LTPP database tables and data sources used to extract the selected PCC 

material properties and layer characteristics. The following LTPP experiments have sections with 

PCC layer type that were included in ARMAD: GPS-3, GPS-4, GPS-5, GPS-7, GPS-9, SPS-1, 

SPS-2, SPS-4, SPS-6, SPS-7, SPS-8, and SPS-9. Please refer to the InfoPave website for more 

information regarding LTPP experiment types (FHWA 2022a). The referenced experiment and 

section information was filtered to include PCC layers (LAYER_TYPE = PC) and cementitious

material types (MATL_CODE = 4, 5, 6, 90, and 334). 

Table 11. The LTPP data table or source to extract each material property—PCC dataset. 

Property Type Properties LTPP Tables

LTPP layer 

information

Layer information, thickness, 

and material type

EXPERIMENT_SECTION

TST_L05B

Construction date/placement 

date

INV_AGE

PROJECT_HIST_AGE

SPS2_PCC_PLACEMENT_DATA

SPS6_OVERLAY

SPS7_PCC_OVERLAY

SPS8_PCC_PLACEMENT_DATA

TST_FRESH_PCC

Strength properties Modulus of elasticity TST_PC04

INV_PCC_STRENGTH

Modulus of rupture (flexural 

strength) 

TST_PC09 (for SPS sections only) 

INV_PCC_STRENGTH

Tensile strength TST_PC02

INV_PCC_STRENGTH

Compressive strength TST_PC01

INV_PCC_STRENGTH

RHB_PCCO_STRENGTH

Mixture properties 

(nonstrength) 

Unit weight TST_PC05

Air content TST_PC05, TST_PC08

INV_PCC_MIXTURE

RHB_PCCO_MIXTURE 

Poisson’s ratio TST_PC04

Water-to-cement ratio INV_PCC_MIXTURE

SPS2_PCC_MIXTURE_DATA

SPS8_PCC_MIXTURE_DATA

SPS7_PCC_OVERLAY
38 

Property Type Properties LTPP Tables

Thermal properties 

(nonstrength) 

Coefficient of thermal 

expansion 

TST_PC03

Surface shortwave absorptivity MEPDG default value 

Thermal conductivity MEPDG default value 

Heat capacity MEPDG default value 

PCC zero-stress temperature MEPDG prediction equation 

Construction 

properties 

(nonstrength) 

Curing method INV_PCC_MIXTURE

SPS2_PCC_PLACEMENT_DATA

SPS8_PCC_PLACEMENT_DATA

SPS7_PCC_OVERLAY

RHB_PCCO_CONSTRUCTION

Ultimate shrinkage MEPDG prediction equation 

Reversible shrinkage MEPDG default value 

Time to develop 50 percent of 

ultimate shrinkage

MEPDG default value 

Reinforcement steel in concrete 

(diameter, depth, spacing, etc.) 

INV_PCC_STEEL

SPS2_PCC_STEEL

RHB_PCCO_STEEL

DERIVING REPRESENTATIVE VALUES

The properties that table 11 lists are categorized into two main types: strength/response 

properties (modulus of elasticity, modulus of rupture, tensile strength, and compressive strength) 

and nonstrength properties (PCC mixture properties, PCC construction properties, and thermal 

characteristics). Figure 9 illustrates the decision tree used to derive the PCC layer-level 

representative properties. Challenges with populating these data in the ARMAD are discussed in 

this section.
39 

Source: FHWA.

Figure 9. Flowchart. Decision tree for deriving PCC layers’ representative properties.
40 

PCC Strength/Response Properties

PCC strength/response properties include compressive strength, tensile strength, modulus of 

rupture (flexural strength), and modulus of elasticity. The 28-d PCC strength properties are 

typically required as inputs for the PMED software. However, LTPP PCC layers, particularly for 

GPS experiments, were tested at a later time than 28 d after the construction, in many cases years 

after placement of the PCC layer.

Consequently, 28-d strength properties were estimated based on the growth models used in the 

MEPDG (AASHTO 2020; ARA, Inc., ERES Consultants Division 2004), as detailed in this 

section. The distributions of the resulting backcasted strength values were compared with the 

distribution of LTPP sections with actual measured 28-d strength properties to check the validity 

of each estimation. Since the distributions characteristics were similar, these estimations were 

included in the ARMAD, and are identified by a “* _28_DAY” extension in their field name—

for example, “ELASTIC_MOD_28_DAY,” “COMP_STRENGTH_28_DAY,” 

“MODULUS_OF_RUPTURE_28_DAY,” and “TENSILE_STRENGTH_28_DAY.” 

28-d Compressive Strength Calculation

The 28-d compressive strength was estimated using the F_STRRATIO ratio of modulus of 

rupture and its relationship with the compressive strength using the MEPDG growth function 

model (AASHTO 2020). Figure 10 provides the specific equations used to estimate the 28-d 

compressive strength. 

Considering: 

And, 

Then, 

Figure 10. Equation. Calculation of 28-d compressive strength of PCC.
41 

Where:

= compressive strength. 

MR(t) = modulus of rupture at time t. 

MR(28) = 28-d modulus of rupture. 

F_STRRATIO = modulus of rupture at a given age to modulus of rupture at 28 d (single-point 

estimate). 

(28) = 28-d compressive strength. 

28-d Elastic Modulus Calculation

The 28-d elastic modulus, Ec(28), was calculated using the F_STRRATIO ratio using the 

MEPDG growth model (AASHTO 2020). The specific equation used is provided in figure 11. 

Figure 11. Equation. Calculation of 28-d elastic modulus of PCC.

Where:

Ec(28) = 28-d elastic modulus. 

Ec(t) = elastic modulus at time t. 

28-d Modulus of Rupture Calculation

Similarly, the 28-d modulus of rupture, MR(28), was estimated using the equation provided in

figure 12, which is based on the MEPDG growth model (AASHTO 2020). 

And, 

Therefore,

Figure 12. Equation. Calculation of the 28-d PCC modulus of rupture.

28-d Tensile Strength Calculation

The 28-d tensile strength, ft(28), was estimated using the T_STRRATIO_3 ratio using the 

equation provided in figure 13, which is based on the MEPDG growth function model 

(AASHTO 2020). 
42 

And, 

Then, 

Figure 13. Equation. Calculation of the 28-d tensile strength of PCC.

Where:

T_STRRATIO_3 = level 3 ratio of tensile strength at a given age to tensile strength at 28 d. 

ft(t) = tensile strength at time t. 

ft(28) = 28-d tensile strength. 

PCC Nonstrength/Response Properties

PCC Mixture Properties

The AASHTO MEPDG requires multiple volumetric and mixture property inputs to characterize 

PCC layers (AASHTO 2020). Following is the list of PCC mixture properties that are included in 

ARMAD: 

• Water-to-cement ratio (WATER_CEMENT_RATIO).

• Unit weight (UNIT_WT).

• Poisson’s ratio (POISSON_RATIO).

The LTPP program acquired part of these data from State highway agency (SHA) construction 

documentation and stored them in the LTPP database’s inventory module (INV). PCC unit 

weight and Poisson’s ratio were measured on some sections as a part of the testing program. 

The general approach for identifying the representative layer properties discussed in chapter 3 

was used to obtain mixture properties depending on the data availability in the LTPP database. 

PCC Construction Properties

The LTPP database stores information on PCC curing method and reinforcement steel properties 

(DEPTH_TO_REINFORCEMENT, LONG_BAR_DIAMETER, LONG_BAR_SPACING, 

TRANSVERSE_BAR_DIAMETER, and TRANSVERSE_BAR_SPACING), which was 

obtained from SHA construction documentation and is stored in the LTPP database inventory 

module. The following properties are considered in the PMED software and have been included 

in the ARMAD: 

• Ultimate shrinkage (ULT_SHRINKAGE): The equation provided in figure 14, obtained 

from the PMED software source code (Bazant and Murphy 1995), was used to estimate 

the ultimate shrinkage in ARMAD: 
43 

Figure 14. Equation. Estimation of the PCC ultimate shrinkage. 

Where: 

εult–shrinkage = ultimate shrinkage microstrain.

C1 = cement type factor for:

o cement Type I = 1.0.

o cement Type II = 0.85.

o cement Type III = 1.1.

C2 = curing method for: 

o wet curing = 1.0. 

o curing compound = 1.2. 

w = water content calculated using cement content and water/cement ratio (lb/ft3

).

• Reversible shrinkage (REVERS_SHRINKAGE): The MEDPG default value of 

50 percent was used (AASHTO 2020).

• Time to develop 50 percent of ultimate shrinkage (T_HALF_ULT_SHRINKAGE): The

MEPDG default value of 35 d was used (AASHTO 2020). 

Thermal Characteristics

The following PCC thermal characteristics were included in ARMAD: 

• Coefficient of thermal expansion (COEF_THERMAL_EXPANSION), which was 

obtained from one of the following two methods: 

o Representative LTPP measurements.

o The MEPDG default value of 4.9 × 10-6/°F based on the NCHRP 20-07 report 

(AASHTO 2020). 

• Surface shortwave absorptivity (SUR_SHORTWAVE_ABSORP): The MEPDG default 

value of 0.85 was used as the recommended input in the PMED software. 

• Thermal conductivity (THERMAL_CONDUCTIVITY): Typical values for PCC range 

from 0.2 to 2.0 BTU/ft/hr/°F. The MEPDG default value of 1.25 BTU/ft/hr/°F was used 

to populate ARMAD (AASHTO 2020). 

• Heat capacity (HEAT_CAPACITY): Typical values for PCC heat capacity range from 

0.1 to 0.50 BTU/lb/°F. The MEPDG default value of 0.28 BTU/lb/°F was used for 

ARMAD (AASHTO 2020). 

• PCC zero-stress temperature (ZERO_STRESS_TEMP): The PCC zero-stress temperature 

or set temperature, Tz, was calculated for ARMAD using the MEPDG equation contained 

in figure 15 (AASHTO 2020). 
44 

Considering: 

Then, 

Figure 15. Equation. Prediction of PCC zero-stress temperature.

Where:

H = computed parameter as a function of mean monthly temperature construction 

month. 

MMT = mean monthly temperature for month of construction, °F.

Tz = PCC zero-stress temperature (allowable range is 70–212 °F).

CC = cementitious content (lb/yd3

).

DATA SOURCE AND VARIABILITY

The REP_CODE_SOURCE_VARIABILITY in the ANALYSIS_TST_PCC_SUPPORT table 

was assigned as follows: 

• Measured—labeled as “A.” Under this classification, mixture and as-measured strength 

properties with two or more measurements that were combined to generate a single value 

were divided into low or high COV based on a threshold of 10 percent.

1 These properties 

were labeled as “AL” for low COV and “AH” for high COV. Properties with a single 

measurement were included in this classification and labeled as “AS.” For properties with 

unknown COV, such as representative layer thickness (REPR_THICKNESS), the values 

are labeled as a single character “A.” 

• Calculated—labeled as “B.” This classification was used for calculated 28-d strength 

properties. For properties where the value was derived from multiple measurements, the 

classification was divided into low- (“BL”) and high-COV (“BH”) values based on a 

COV threshold of 10 percent. If the value was calculated from a single measurement, it 

was labeled as “BS.” 

• Expanded—labeled as “C.” This classification was used for the strength and nonstrength 

properties of SPS test sections that were expanded to other similar SPS test sections using 

the PROJECT_LAYER_CODE. Properties that were determined based on two or more 

measurements from similar sections were labeled as “CL” for low COV and “CH” for 

high COV, based on a COV threshold of 10 percent. If the COV was unknown, such as 

representative layer thickness, values were labeled by the single character “C.” 

1

The COV of 10 percent was determined as the threshold based on a review of the overall LTPP PCC data 

statistics and the recommendations of the ARMAD SMEs. 
45 

• Imputed—labeled as “D.” Modulus of rupture values of PCC materials were imputed 

based on the measured or expanded compressive strength. The properties were labeled as 

“DL” for low COV or “DH” for high COV based on a COV threshold of 10 percent. 

• Inventory—labeled as “E.” This classification was applied to the PCC construction and 

mixture properties, where values were obtained from the respective agencies. For 

properties where the value was derived from multiple agency data, the classification was 

divided into low- (“EL”) and high-COV (“EH”) values based on a COV threshold of 

10 percent. If the value was calculated from a single inventory value, it was labeled as 

“ES.” 

• Assumed—labeled as “F.” This classification was used for representative values adopted 

from the MEPDG or default PMED software inputs, such as thermal characteristics and 

PCC shrinkage properties. This classification does not have a COV designation as these 

are assumed values. 

47 

CHAPTER 6. GENERATION OF ASPHALT CONCRETE MATERIALS 

ANALYSIS-READY DATASET

This chapter documents the development of the ARD for AC materials. The key AC material 

properties selected for inclusion in ARMAD were presented earlier in table 2. 

DATA DISCOVERY

Table 12 presents the LTPP database tables and data sources used to extract the selected AC 

material properties and layer characteristics. The following LTPP experiments have sections with 

an AC layer type that were included in ARMAD: GPS-1, GPS-2, GPS-3, GPS-5, GPS-6, GPS-7, 

GPS-9, SPS-1, SPS-2, SPS-3, SPS-5, SPS-6, SPS-7, SPS-8, and SPS-9 (refer to FHWA 2022a 

for more information regarding LTPP experiments). Materials testing of the AC layers of the

SPS-10 experiment is ongoing; therefore, it is not included in ARMAD in SDR 36. The 

referenced experiment and section information was filtered to include AC layer and asphalt 

treated base/subbase/subgrade for the following layer types and material codes: 

• LAYER_TYPE = “AC” and MATL_CODE = 1, 2, 9, 13, 14, 15, 16, and 319. 

• LAYER_TYPE = “TS” and MATL_CODE = 319, 320, 321, and 325. 

• LAYER_TYPE = “TB” and MATL_CODE = 322, 323, 324, 326, 327, and 328. 

Table 12. The LTPP data table or source to extract each material property—AC dataset. 

Property Type Properties LTPP Table

LTPP layer 

information

Layer thickness and material 

type 

TST_L05B

Section information EXPERIMENT_SECTION

Construction date INV_AGE

Mechanical

properties 

AC Poisson’s ratio TST_AC07_V2_IDT_SUM 

Asphalt binder complex shear 

modulus and phase angle 

AC_DSR_EXP

Binder type AC_BINDER_EXP

Binder viscosity TST_AE05

Dynamic modulus TST_ESTAR_MASTER

TST_ESTAR_MODULUS

Tensile strength TST_AC07_V2_IDT_SUM 

Creep compliance TST_AC07_V2_CREEP_COMP_SUM

Aggregate coefficient of thermal 

contraction 

MEPDG default value 

Volumetric 

properties 

Maximum specific gravity TST_AC03

Binder content percent total by 

weight 

TST_AC04

Air voids TST_AIR_VOIDS_SECT

Aggregate gradation TST_AG04

BSG TST_AC02

Thermal properties Heat capacity MEPDG default value 

Thermal conductivity MEPDG default value

Surface shortwave absorptivity MEPDG default value
48 

DETERMINING REPRESENTATIVE VALUES

The properties listed in table 12 were classified into the following three types: mechanical

properties, volumetric properties, and thermal properties. Figure 16 exhibits the decision tree 

used to derive the AC layer-level representative mechanical and volumetric properties. The 

recommended default values provided in the MEPDG (AASHTO 2020) were used to establish 

the ARMAD AC thermal properties.
49 

Source: FHWA. 

Figure 16. Flowchart. Decision tree for deriving AC layers’ representative properties.
50 

Mechanical Properties

AC Poisson’s Ratio 

In the PMED software, there are two methods for entering Poisson’s ratio to predict distresses: 

using a single value entered by the designer that is assigned to that layer for all temperatures 

calculated by the software and having the software’s internal regression equation calculate 

Poisson’s ratio as a function of layer temperature. The designer is unable to change the 

coefficients of the regression equation in the software, so a single value for Poisson’s ratio was 

included in ARMAD. The AC Poisson’s ratio (IDT_POISSON_USED) is measured in the LTPP 

Program during the indirect tensile test (IDT) at 25 °C. For those AC layers where Poisson’s 

ratio is unavailable (i.e., missing values), the recommended MEPDG default value of 0.35 at 

25 °C was used to populate ARMAD (AASHTO 2020).

Asphalt Binder Complex Shear Modulus and Phase Angle 

In the LTPP program, the binder complex shear modulus and phase angle were measured using 

the dynamic shear rheometer (DSR) test for original binder from the tank, aged binder using 

rolling thin-film oven (RTFO), and pressure aging vessel (PAV) per Long-Term Pavement 

Performance Project Laboratory Materials Testing and Handling Guide (Simpson, Schmalzer, 

and Rada 2007).

The DSR_COMPLEX_MODULUS_TANK, DSR_COMPLEX_PHASE_ANGLE_TANK, 

DSR_COMPLEX_MODULUS_RTFO, DSR_COMPLEX_PHASE_ANGLE_RTFO, 

DSR_COMPLEX_MODULUS_PAV, and DSR_COMPLEX_PHASE_ANGLE_PAV fields 

were determined following the general approach detailed in chapter 3, which depended on data 

availability.

Binder Viscosity

Representative values of binder absolute viscosity at 140 °F (ABSOLUTE_VISC_140_F) and 

binder kinematic viscosity at 275 °F (KINEMATIC_VISC_275_F) were measured via laboratory 

testing on bulk samples or binder extracted from cores, depending on the projects. 

Representative values were established following the general approach detailed in chapter 3, 

which depended on data availability. The viscosity and other rheology properties measured on 

extracted binder from field cores represent the time of sampling. The input properties required by 

the PMED software need to represent the short-term aged binder at construction. These rheology 

properties on the extracted binder were not backcast to the time of construction. 

Dynamic Modulus 

FHWA performed a study titled LTPP Computed Parameter: Dynamic Modulus, which made 

use of available AC material properties in the LTPP database to predict dynamic modulus when 

these values were not directly measured (Kim et al. 2011). In this study, several artificial neural 

network models were developed and compared, and the data were incorporated in the LTPP 

database. These data in ANALYSIS_TST_AC_ESTAR can be merged with the 

ANALYSIS_TST_AC table using the ESTAR_LINK_GROUP field for each layer.
51 

Tensile Strength

Two IDT strengths were measured and reported within the LTPP program. The first IDT strength 

was measured at 25 °C at a loading rate of 25 mm/min in accordance with AASHTO TP 09 

(AASHTO 1996). In contrast, the second IDT strength was measured at much lower 

temperatures and much slower loading rates in accordance with AASHTO T 322 (AASHTO 

2007). Please note that the PMED software’s input is IDT at −10 °C per AASHTO T 322

specification and with a different loading rate. The IDT_AVERAGE, 

IDT_TEST_TEMPERATURE, and IDT_LOADING_RATE fields were populated following the 

general approach detailed in chapter 3, which depended on data availability. 

Creep Compliance 

LTPP AC creep compliance testing was performed at temperatures of 14, 41, and 77 °F and 1-, 

2-, 5-, 10-, 20-, 50-, 100-s frequencies. Again, the testing temperatures and loading rates used in 

the LTPP testing were different than those used as inputs to the PMED software. The PMED 

software requires inputs for creep compliance in accordance with AASHTO T 322 (AASHTO 

2007). These data are provided in a separate table (ANALYSIS_TST_AC_CREEP_COMP) and 

linked to the main AC table (ANALYSIS_TST_AC) via the CREEP_COMP_LINK field for 

each layer.

Aggregate Coefficient of Thermal Contraction 

The coefficient of thermal contraction (COEF_THERMAL_CONTRACTION) for the aggregate 

in AC mixtures was estimated using the MEPDG equation provided in figure 17

(AASHTO 2020).

Figure 17. Equation. Estimation of the aggregate coefficient of thermal contraction in AC.

Where:

VMA = volume of voids in the mineral aggregate (VMA) (percentage).

Vagg = volume of aggregate in the mixture (percentage).

VTOTAL = 100 percent.

LMIX = 2.2 to 3.4×10−5

/°C (linear). 

Bac = 3.5 to 4.3×10−4/°C (cubic). 

Bagg = 21 to 37×10−6/°C (cubic). 
52 

Volumetric Properties

The AC mixture volumetric properties included in ARMAD were derived as follows: 

1. The following properties were determined from LTPP laboratory testing carried out in 

accordance with the Long-Term Pavement Performance Project Laboratory Materials 

Testing and Handling Guide (Simpson, Schmalzer, and Rada 2007): 

o Maximum specific gravity (MAX_SPEC_GRAVITY). 

o Binder content (ASPHALT_CONTENT_MEAN). 

o Air voids (AIR_VOIDS_AVG_WP and AIR_VOIDS_AVG_NWP); WP represent 

wheelpath and NWP non-wheelpath.

o Aggregate gradation (ONE_AND_HALF_PASSING, ONE_PASSING, 

THREE_FOURTHS_PASSING, ONE_HALF_PASSING, 

THREE_EIGHTHS_PASSING, NO_4_PASSING, NO_10_PASSING, 

NO_40_PASSING, NO_80_PASSING, and NO_200_PASSING). 

o Bulk specific gravity (BSG).

2. To calculate VMA and VFA, binder and aggregate specific gravities were assumed to be 

typical values. The equations presented in figure 18 and figure 19 were used to calculate 

the VMA and VFA, respectively: 

o Binder specific gravity (BINDER_SPEC_GRAVITY). 

o Aggregate specific gravity (AGGR_SPEC_GRAVITY). 

o VMA (VOIDS_MINERAL_AGGR). 

o VFA (VOIDS_FILLED_ASPHALT). 

Figure 18. Equation. Calculation of AC VMA.

Figure 19. Equation. Calculation of AC VFA.

The general approach described in chapter 3 was used to obtain representative AC volumetric

properties, which depended on the layer-level data availability.
53 

Thermal Properties

The following AC thermal properties were included in ARMAD: 

• Heat capacity (HEAT_CAPACITY): The MEPDG recommended default value of 

0.28 BTU/lb/°F was used (AASHTO 2020). 

• Thermal conductivity (THERMAL_CONDUCTIVITY): The MEPDG default value of 

1.25 BTU/ft/hr/°F was used (AASHTO 2020). 

• Surface shortwave absorptivity (SUR_SHORTWAVE_ABSORP): The MEPDG default 

value of 0.85 was used (AASHTO 2020). 

DATA SOURCE AND VARIABILITY

The REP_CODE_SOURCE_VARIABILITY in the ANALYSIS_TST_AC_SUPPORT table was 

assigned as follows: 

• Measured—labeled as “A.” For this classification, material properties with two or more 

measurements that were combined to generate a single representative value and were

divided into low-COV (“AL”) and high-COV (“AH”) thresholds based on a threshold 

COV of 10 percent.

1 Material properties with only a single (“S”) measurement were 

included in this classification under the label of “AS.” For measurements with unknown 

COV, such as measured representative layer thickness (REPR_THICKNESS), only the 

classification code was used; i.e., “A.” 

• Calculated—labeled as “B.” This classification was used for calculated VMA, VFA, and 

air voids. The classification was divided into material properties with low (“BL”) and 

high (“BH”) COV based on a threshold COV of 10 percent. If the value was calculated 

from a single (“S”) measurement, it was labeled as “BS.” 

• Expanded—labeled as “C.” This classification was applied to the material properties of 

SPS sections that were expanded to similar sections using the 

PROJECT_LAYER_CODE. Material properties established based on two or more 

measurements from similar sections were labeled as “CL” for low COV or “CH” for high 

COV based on a threshold COV of 10 percent. Material properties established based on 

one measurement from a similar section were labeled as “CS.” For expanded values with 

an unknown COV, such as representative layer thickness, the classification was labeled 

as the single character “C.” 

1

The COV of 10 percent was determined as the threshold based on a review of the overall LTPP AC data 

statistics and the recommendations from the ARMAD SMEs. 
54 

• Inventory—labeled as “E.” This classification was applied to the AC viscosity and DSR 

values, which were obtained from SHA-provided information. For properties where the 

value was derived from data from multiple agencies, the classification was divided into 

low- (“EL”) and high-COV (“EH”) values based on a COV threshold of 10 percent. If the 

value was calculated from a single inventory value, it was labeled as “ES.” 

• Assumed—labeled as “F.” This classification was used for representative values adopted 

from the MEPDG or default PMED software inputs, such as the AC thermal 

characteristics (AASHTO 2020). This classification does not have a COV designation as 

the values were assumed. 
55 

CHAPTER 7. GENERATION OF TREATED MATERIALS ANALYSIS-READY

DATASET

This chapter documents the development of the ARD for treated or stabilized materials. The list 

of key material properties selected for inclusion in ARMAD was provided in table 1 earlier in the 

report. 

DATA DISCOVERY

Table 13 presents the LTPP database tables and data sources used to extract the selected material 

properties and layer characteristics. The experiment and section information was filtered to 

include treated subgrade and base layers for the following layer types and material codes:

• LAYER_TYPE = “TS” and MATL_CODE = 309, 331, 333, 338, 339, 340, and 360. 

• LAYER_TYPE = “TB” and MATL_CODE = 332, 334, and 350. 

Table 13. The LTPP data table or source to extract each material property—treated 

materials dataset. 

Material Properties LTPP Tables/Source

Layer thickness and material type TST_L05B

Section information EXPERIMENT_SECTION

Soil and aggregate type TST_TB01

Treatment type TST_TB01

Compressive strength TST_TB02

TST_SS10

Resilient modulus MEPDG estimation equation/default value

Elastic modulus MEPDG estimation equation/default value

Poisson’s ratio MEPDG default value 

Unit weight MEPDG default value 

Thermal conductivity MEPDG default value 

Heat capacity MEPDG default value 

DETERMINING REPRESENTATIVE VALUES

The procedure used to determine layer-level representative values for ARMAD’s 

ANALYSIS_TST_TR and ANALYSIS_TST_TR_SUPPORT tables is described in this section.

Material and Treatment Type

Representative properties for soil geological classification (GEOL_CLASS), treatment type 

(TREAT_TYPE), and aggregate type (AGGR_TYPE) were identified for each treated layer in 

the LTPP database. For treated layers with two or more measurements available, the dominant 

material and treatment type were identified as the representative values. Table 14 to table 16

summarize these cases; the “combined code” column presents the representative value selected.
56 

Table 14. Soil classification for combined geological class cases. 

GEOL_CLASS

_1 

GEOL_CLASS

_1_DETAIL 

GEOL_CLASS

_2 

GEOL_CLASS

_2_DETAIL Combined Code

32 Boulder clay 43 Alluvial clays 

and/or peat 

43 

32 Boulder clay 36 Ground moraine 36 

30 Other rock type 

(specify if possible 

or state unknown) 

47 Wind-blown sand 47 

1 Granite 10 Dolomite 10 

41 River gravels 60 Other limerock 

materials

41 

9 Limestone 60 Other limerock 

materials

9 

41 River gravels 47 Windblown sand 41 

9 Limestone 41 River gravels 41 

30 Other rock type 

(specify if possible 

or state unknown) 

52 Residual soils 

derived from 

granites, gneisses, 

and schists 

52 

9 Limestone 33 Glacial sands and 

gravels

9 

9 Limestone 41/60 River gravels/other 

limerock materials

9 

Table 15. Treatment types for combined treatment cases.

TREAT_

TYPE_1

TREAT_TYPE

_1_DETAIL 

TREAT_

TYPE_2

TREAT_TYPE_2

_DETAIL

Combined 

Code 

334 Lean concrete 354 Treatment: cement—

portland cement 

334 

182 Cement-treated 

subgrade soil 

338 Lime-treated soil 182 

338 Lime-treated soil 354 Treatment: cement—

portland cement 

354 

334 Lean concrete 354 Treatment: cement—

portland cement 

354 

331 Cement aggregate 

mixture

354 Treatment: cement—

portland cement 

354 

351 Treatment: lime, all 

classes of quick lime 

and hydrated lime 

354 Treatment: cement—

portland cement 

354 
57 

Table 16. Aggregate types for combined aggregate type cases.

AGGR_TYPE

_1

AGGR_TYPE_

1_DETAIL

AGGR_TYPE

_2

AGGR_TYPE

_2_DETAIL

Combined 

Code

402 Crushed stone 403 Crushed gravel 402

401 Gravel 402 Crushed stone 402

403 Crushed gravel 411 Blend (different sized 

fine aggregates)

411

409 Natural sand 412 Other (specify) 409

401 Gravel 411 Blend (different sized 

fine aggregates)

411

402 Crushed stone 408 Other (specify) 402

402 Crushed stone 405 Blend (several sizes of 

coarse aggregates)

405

405 Blend (several 

sizes of coarse 

aggregates)

408 Other (specify) 405

401 Gravel 402/409 Crushed stone/natural 

sand 

402

Strength Properties

The compressive strength test was performed on samples taken from LTPP treated base and 

subgrade layers. The process detailed in chapter 3 was used for determining the representative 

compressive strength value based on the available data.

The MEPDG recommended input parameters (levels 2 and 3) for chemically stabilized material 

properties were used to establish the ARMAD elastic and resilient modulus (ELASTIC_MOD 

and RES_MOD) values (AASHTO 2020). The MEPDG recommends estimating elastic or 

resilient modulus as a function of the measured unconfined compressive strength from laboratory 

samples or extracted cores. In the absence of compressive strength data, the MEPDG

recommends default values. The relationships presented in table 17 provide the basis used for 

estimating the ARMAD moduli as a function of LTPP material type. Similarly, the

MEPDG-recommended Poisson’s ratio (POISSON_RATIO) values are presented in table 18 as a 

function of LTPP material type. 
58 

Table 17. MEPDG-recommended elastic/resilient modulus for LTPP chemically treated 

material types.

MATL_

CODE Detail

LAYER_

TYPE

ELASTIC_MOD

(psi)

RES_MOD

(psi)

309 Fine-grained soils TS N/A 45,000 

331 Cement aggregate mixture TS 57,000( )

0.5 or 

1,000,000 

N/A

332 Econocrete TB 57,000( )

0.5 or 

1,000,000 

N/A

333 Cement-treated soil TS 1,200( ) or 

500,000 

N/A

334 Lean concrete TB 57,000( )

0.5 or 

2,000,000 

N/A

338 Lime-treated soil TS N/A 124( ) + 9.98 

or 45,000 

339 Soil cement TS 1,200( ) or 

500,000 

N/A

340 Pozzolanic aggregate 

mixture

TS 500 + or 

1,500,000 

N/A

350 Other TB N/A 45,000 

360 Treatment: other (specify, if 

possible) 

TS N/A 45,000 

N/A = No data.

Table 18. MEPDG-recommended Poisson’s ratio values for each LTPP treated material 

type

MATL_CODE Detail POISSON_RATIO

309 Fine-grained soils 0.15 

331 Cement aggregate mixture 0.15 

332 Econocrete 0.15 

333 Cement-treated soil 0.25 

334 Lean concrete 0.15 

338 Lime-treated soil 0.175 

339 Soil cement 0.25 

340 Pozzolanic-aggregate mixture 0.125 

350 Other 0.2 

360 Treatment: other (specify, if possible) 0.2 

The MEPDG-recommended default unit weight (UNIT_WEIGHT), thermal conductivity 

(THERMAL_CONDUCTIVITY), and heat capacity (HEAT_CAPACITY) values for treated 

materials are 150 pcf, 1.25 BTU/ft/hr/°F, and 0.28 BTU/lb/°F, respectively.
59 

DATA SOURCE AND VARIABILITY

• Measured—labeled as “A.” For this classification, material properties (material type, 

treatment type, and compressive strength) with two or more measurements that were 

combined into a single representative value were divided into low (“AL”) and high 

(“AH”) COV based on a threshold COV of 10 percent.

1 For measurements with unknown 

COV, the values were labeled as a single character “A.” 

• Calculated—labeled as “B.” This classification was used for calculated elastic modulus 

based on the measured compressive strength. The classification was divided into low 

(“BL”) and high (“BH”) COV based on a threshold COV of 10 percent. If the 

representative value was calculated from a single (“S”) measurement, the value was 

labeled as “BS.” 

• Expanded—labeled as “C.” This classification was applied to the material properties of 

SPS sections that were expanded to similar sections using the 

PROJECT_LAYER_CODE. Properties that were established based on two or more 

measurements from similar sections were labeled as “CL” for low COV and “CH” for 

high COV based on a threshold COV of 10 percent. For expanded values with unknown 

COV, such as representative layer thickness, the values were labeled as a single character 

“C.” 

• Inventory—labeled as “E.” Applied to representative thickness that was adopted from 

SHA-provided information. For properties where the value was derived from multiple 

agency data, the classification was divided into low- (“EL”) and high-COV (“EH”) 

values based on a COV threshold of 10 percent. If the value was calculated from a single 

inventory value, it was labeled as “ES.” 

• Assumed—labeled as “F.” This classification was used for representative values adopted 

from the MEPDG or default PMED software’s inputs, including resilient modulus, elastic 

modulus, unit weight, thermal conductivity, heat capacity, and Poisson’s ratio. This 

classification does not have a COV designation as the values have been assumed. 

1

The COV of 10 percent was determined as the threshold based on a review of the overall LTPP AC data 

statistics and the recommendations from the ARMAD SMEs. 

61 

CHAPTER 8. GENERATION OF OTHER MATERIALS ANALYSIS-READY DATASET

This chapter documents the development of the ARD for other materials such as surface 

treatments, EF, and interlayers. Although their presence is noted in the LTPP database, these 

types of layers were generally not tested by the LTPP program; that is, no physical 

measurements were conducted.

DATA DISCOVERY AND DETERMINING REPRESENTATIVE VALUES

The only designated property for these material types was layer thickness, which was extracted 

directly from the TST_L05B table for the test section. The experiment and section information 

were filtered to include EF, interlayers, and surface treatment material types for the following 

layer types and material codes:

• ANALYSIS_TST_EF and SUPPORT tables were populated for LAYER_TYPE = EF. 

• ANALYSIS_TST_TR and SUPPORT tables were populated for: 

o LAYER_TYPE = AC and surface treatment material types including chip seal, slurry 

seal, sand seal, seal coat (MATL_CODE = 11, 12, 71, 72, 73, 81, 82, and 83). 

o LAYER_TYPE = AC and material types including stress-absorbing membrane, 

dense-graded AC, and open-graded AC interlayers (MATL_CODE = 77, 78, 80).

DATA SOURCE AND VARIABILITY

The REP_CODE_SOURCE_VARIABILITY in the ANALYSIS_TST_TR_SUPPORT and 

ANALYSIS_TST_EF_SUPPORT table was assigned for the layer thickness only 

(REPR_THICKNESS) as follows: 

• Measured—labeled as “A.” For this classification, layer thickness was measured in the 

field. The layer thickness variability is unknown or not applicable; therefore, only the 

character code of “A” was used for this classification. 

• Expanded—labeled as “C.” These layer thickness values were obtained from a nearby 

section because a thickness was not obtained for the subject test section. The variability 

was unknown or not applicable; therefore, only the character code of “C” was used for 

this classification. 

• Inventory—labeled as “E.” In this classification, layer thickness values were not 

available, so they were obtained from inventory data. The variability was unknown or not 

applicable; therefore, only the character code “E” was used for this classification. 

• Assumed—labeled as “F.” This classification was used when layer thickness values were 

not available in the LTPP database, so the thickness values were assumed. Because 

assumed values were used, this classification only uses the single character code of “F.” 

63 

CHAPTER 9. ADDITIONAL READING

The following resource documents can be useful in understanding the LTPP material 

characterization program. These documents can be downloaded from 

https://infopave.fhwa.dot.gov/Reports/Library. 

• Afsharikia, N., J. Groeger, L. Garner, B. Ostrom, and G. Rada. 2022. Introduction to the 

LTPP Analysis-Ready Materials Dataset (ARMAD). Report No. FHWA-HRT-22-114. 

Washington, DC: Federal Highway Administration. 

• FHWA. 1994. Specific Pavement Studies, Materials Sampling and Testing Requirements 

for Experiment SPS-1: Strategic Study of Structural Factors for Flexible Pavements. 

Washington, DC: Federal Highway Administration. 

• FHWA. 1994. Specific Pavement Studies, Materials Sampling and Testing Requirements 

for Experiment SPS-2: Strategic Study of Structural Factors for Rigid Pavements. 

Washington, DC: Federal Highway Administration.

• FHWA. 1996. Specific Pavement Studies, Materials Sampling and Testing Requirements 

for Experiment SPS-9A: SUPERPAVE Asphalt Binder Study. Washington, DC: Federal 

Highway Administration. 

• FHWA. 2015. The Long-Term Pavement Performance Program. Report 

No. FHWA-HRT-15-049. Washington, DC: Federal Highway Administration. 

• FHWA. 2016. LTPP Information Management System (IMS) Quality Control Checks. 

Washington, DC: Federal Highway Administration.

• SHRP. 1990. Specific Pavement Studies, Materials Sampling and Testing Requirements 

for Experiment SPS-5: Rehabilitation of Asphalt Concrete Pavements. Report No. 

SHRP-LTPP-OM-014. Washington, DC: National Research Council. 

• SHRP. 1991. Field Material Sampling and Testing Guide SHRP-LTPP Guide for Field 

Materials Sampling, Testing, and Handling. Report No. SHRP-LTPP-OG-006. 

Washington, DC: National Research Council. 

• SHRP. 1991. Specific Pavement Studies, Materials Sampling and Testing Requirements 

for Experiment SPS-6: Rehabilitation of Jointed Portland Cement Concrete Pavements.

Report No. SHRP-LTPP-OM-019. Washington, DC: National Research Council. 

• SHRP. 1991. Specific Pavement Studies, Materials Sampling and Testing Requirements 

for Experiment SPS-7: Bonded Portland Cement Concrete Overlays. Report No. 

SHRP-LTPP-OM-020. Washington, DC: National Research Council. 
64 

• SHRP. 1992. Specific Pavement Studies, Materials Sampling and Testing Requirements 

for Experiment SPS-8: Study of Environmental Effects in the Absence of Heavy Loads.

Report No. SHRP-LTPP-OM-030. Washington, DC: National Research Council. 

• Simpson, A. L., P. N. Schmalzer, and G. R. Rada. 2007. Long-Term Pavement 

Performance Project: Laboratory Materials Testing and Handling Guide. Report 

No. FHWA-HRT-07-052. Washington, DC: Federal Highway Administration. 
65 

ACKNOWLEDGMENTS

Subject matter experts who provided guidance for this effort included Charles Schwartz, Ph.D.; 

George Chang, Ph.D., P.E.; Harold Von Quintus, P.E.; and Gonzalo Rada, Ph.D., P.E. 

67 

REFERENCES

AASHTO. 1996. Standard Test Method for Determining the Creep Compliance and Strength of 

Hot Mix Asphalt (HMA) Using the Indirect Tensile Test Device. TP 09. Washington, DC: 

American Association of State Highway and Transportation Officials.

AASHTO. 2007. Standard Method of Test for Determining the Creep Compliance and Strength 

of Hot Mix Asphalt (HMA) Using the Indirect Tensile Test Device. T 322. Washington, 

DC: American Association of State Highway and Transportation Officials. 

AASHTO. 2020. Mechanistic-Empirical Pavement Design Guide, A Manual of Practice. 

Washington, DC: American Association of State Highway and Transportation Officials. 

ARA, Inc., ERES Consultants Division. 2004. Guide for Mechanistic-Empirical Design of New 

and Rehabilitated Pavement Structures, NCHRP 1-37A Final Report. Washington, DC: 

Transportation Research Board. 

Bazant, Z. P., and W. P. Murphy. 1995. Creep and Shrinkage Prediction Model for Analysis and 

Design of Concrete Structures-Model B3. Matériaux et Constructions 28, no. 180: 

357–365. 

Elkins, G., and B. Ostrom. 2021. Long-Term Pavement Performance Information Management 

System User Guide. Report No. FHWA-HRT-21-038. Washington, DC: Federal Highway 

Administration. 

FHWA. 2013. LTPP Information Management System (IMS) Quality Control Checks. 

Washington, DC: Federal Highway Administration. 

FHWA. 2015. The Long-Term Pavement Performance Program. Report No. FHWA 

HRT-15-049. Washington, DC: Federal Highway Administration. 

FHWA. 2022a. “LTPP InfoPave” (web page). https://infopave.fhwa.dot.gov/, last accessed May 

31, 2022. 

FHWA. 2022b. “LTPP InfoPave: Data Feedback” (web page). 

https://infopave.fhwa.dot.gov/Data/DAOFRForm, last accessed March 1, 2022. 

FHWA. 2022c. “LTPP InfoPave: Standard Data Release” (web page). 

https://infopave.fhwa.dot.gov/Data/StandardDataRelease/, last accessed March 1, 2022. 

FHWA. 2022d. “LTPP InfoPave: Data Selection and Download” (web page). 

https://infopave.fhwa.dot.gov/Data/DataSelection, last accessed March 1, 2022.

FHWA. 2022e. “InfoPave: Reports” (web page). https://infopave.fhwa.dot.gov/Reports/Library, 

last accessed March 1, 2022.
68 

Kim, Y. Richard, B. Underwood, M. Sakhaei Far, N. Jackson, and J. Puccinelli. 2011. LTPP 

Computed Parameter: Dynamic Modulus. Report No. FHWA-HRT-10-035. Washington, 

DC: Federal Highway Administration. 

Puccinelli, J., P. Schmalzer, K. Senn, and L. McDonald. 2022. Long-Term Pavement 

Performance Warm Mix Asphalt Study Final Report, Volume IV: SPS-10 Materials 

Sampling and Testing Requirement. Report No. FHWA-HRT-22-021. Washington, DC: 

Federal Highway Administration. 

Elkins, G. E., and B. Ostrom. 2021. Long-Term Pavement Performance Information 

Management System User Guide. Report No. FHWA-HRT-21-038. Washington, DC: 

Federal Highway Administration. 

Rao, C., L. Titus-Glover, B. B. Bhattacharya, M. I. Darter, M. Stanley, and H. L. Von Quintus. 

2012. Estimation of Key PCC, Base, Subbase, and Pavement Engineering Properties 

from Routine Tests and Physical Characteristics. Report No. FHWA-HRT-12-030. 

Washington, DC: Federal Highway Administration. 

SHRP. 1990a. Specific Pavement Studies, Experimental Design and Research Plan for 

Experiment SPS-1, Strategic Study of Structural Factors for Flexible Pavements. 

Washington, DC: National Research Council. 

SHRP. 1990b. Specific Pavement Studies Experimental Design and Research Plan for 

Experiment SPS-2 Strategic Study of Structural Factors for Rigid Pavements. 

Washington, DC: National Research Council. 

SHRP. 1991. SHRP-LTPP Guide for Field Materials Sampling, Testing, and Handling. 

Operational Guide No. SHRP-LTPP-OG-006. Washington, DC: National Research 

Council. 

SHRP. 1992. SHRP-LTPP Interim Guide for Laboratory Materials Handling and Testing (PCC, 

Bituminous Materials, Aggregates, and Soils). Operational Guide No. 

SHRP-LTPP-OG-004. Washington, DC: National Research Council.

Simpson, A. L., P. N. Schmalzer, and G. R. Rada. 2007. Long-Term Pavement Performance 

Project Laboratory Materials Testing and Handling Guide. Report 

No. FHWA-HRT-07-052. Washington, DC: Federal Highway Administration.. 

HRDI-30/12-23(WEB)E Recycled

Recyclable

Recommended citation: Federal Highway Administration,

Development and Use of the LTPP Analysis-Ready Materials Dataset

(Washington, DC: 2023) https://doi.org/10.21949/1521443