Introduction to the LTPP 

Analysis-Ready Materials 

Dataset (ARMAD)

FHWA Publication No.: FHWA-HRT-22-114

FHWA Contact: Y. Jane Jiang (ORCID: 0000-0003-3982-2530), 

HRDI-30, 202-493-3149, jane.jiang@dot.gov

This document is a technical summary of the Federal Highway 

Administration (FHWA) report, Development and Use of the LTPP 

Analysis-Ready Materials Dataset (Afsharikia et al. Forthcoming). The 

release of this TechBrief coincides with the release of the first version 

of the Analysis Ready Materials Dataset (ARMAD) in the Long-Term 

Pavement Performance (LTPP) Standard Data Release (SDR) 36 via 

LTPP InfoPaveTM in the summer of 2022 (FHWA 2022a). This TechBrief 

provides an introduction and overview of the ARMAD. The full report, 

which details the development and implementation of the dataset, will 

be issued later in 2022.

TECHBRIEF

Research, Development, 

and Technology

Turner-Fairbank Highway 

Research Center

6300 Georgetown Pike

McLean, VA 22101-2296

https://highways.dot.gov/research

The LTPP database is the world’s premier source of data and 

information on pavement performance. However, understanding and 

using the database is not necessarily an easy undertaking. For starters, 

the data are typically distributed across multiple tables in the database, 

making some data elements hard to mine. There are also multiple 

values for a given data element, making it difficult to know which 

value(s) to use. In addition, the data may have to be further interpreted 

to present meaningful results. Because of these challenges, the LTPP 

program has undertaken a process to generate analysis-ready datasets 

(ARDs), starting with the dataset detailed in this document—ARMAD 

(FHWA 2015). The ARMAD solves the stated challenges for the layer 

thicknesses and material properties of all test sections in the LTPP 

database with one exception—the Specific Pavement Studies (SPS)-10 

warm-mix asphalt experiment test sections. Testing for these SPS-10 

test sections is presently ongoing, and the results will be incorporated 

upon completion. Similarly, future planned ARDs—climatic 

conditions, traffic conditions, and performance data—will be 

incorporated into upcoming SDRs.

INTRODUCTION

The goal of the 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 
2

and under different loads, environments, subgrade soil, 

and maintenance practices” (FHWA 2015). The following 

six objectives were identified to support the goal:

• 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 (1) loading, (2) environment, 

(3) material properties and variability, (4) construction 

quality, and (5) 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 Strategic Highway Research Program (SHRP) 

objectives and future needs (Manning 1986).

To accomplish the stated goal and objectives, the 

LTPP program undertook a study of the behavior of 

pavement test sections located on in-service roadways 

(FHWA 2015). These in-service pavement test sections 

are classified in the LTPP program under General 

Pavement Studies (GPS) and SPS. GPS consist of a 

series of studies on 976 in-service pavement test sections 

to examine specific features of existing pavement. SPS 

address specific variables involving new construction, 

maintenance treatments, and rehabilitation activities and 

comprises 1,605 pavement test sections. The sections are 

located throughout the United States and Canada (Elkins 

and Ostrom 2021).

Information about pavement material properties is a 

key component of all aspects of pavement engineering, 

including design, construction, quality control, 

maintenance and rehabilitation, and management. 

The pavement layer structure and material properties 

are fundamental requirements for the development of 

practical pavement performance models and inputs for 

pavement design, among other uses.

The LTPP program has a wealth of materials information 

and data. Nearly every test section in the program had 

materials sampling and testing conducted to determine 

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. (See Simpson, Schmalzer, and 

Rada 2007 for the latest version of the LTPP project’s 

laboratory materials testing and handling guide.)

However, there are limitations in the materials properties 

in the LTPP database, as follows:

• Data are distributed across dozens of tables in the LTPP 

database, making some data elements hard to mine.

• There are multiple property values for a given layer, 

making it difficult to know which representative 

value(s) to use.

• Materials characterization data are missing for 

a number of LTPP test sections.

• Data have to be further interpreted in many cases 

to achieve meaningful results.

OBJECTIVE AND SCOPE OF ARMAD

Prior to the implementation of ARMAD, the acquisition 

and interpretation of LTPP materials data characterization 

could potentially require expert-level knowledge of 

each LTPP experiment, material and protocol, and the 

pavement performance database, creating a significant 

barrier to implementation for practitioners and 

researchers. Consequently, an LTPP user needed to spend 

a substantial amount of time on a data-wrangling effort 

to find, extract, merge, and interpret the available data to 

develop a suitable analysis dataset. Therefore, the program 

developed a process to generate the LTPP ARMAD for 

each section and each layer for every construction period 

in the LTPP database. LTPP ARMAD solves the stated 

challenges, through the following processes:

• Consolidating the number of tables from many to 

a few for all material types.

• Identifying the essential set of material properties to 

characterize each material type.

• Developing representative materials characterization 

data for every section and every layer within the 

LTPP program.

• Interpreting the data to ensure meaningful engineering 

material properties are provided (such as subgrade 

resilient modulus).

The LTPP ARMAD integrates 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, and subgrade materials.

• Other layer materials such as surface treatments 

and engineering fabrics.
3

The scope of this effort was to identify an essential 

set of material properties for each pavement layer 

type, assemble and process the data of those 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 quality 

indicator was assigned to each material property to ensure 

that users can understand the method used to obtain the 

material property and, hence, the associated data quality.

LTPP MATERIALS TESTING 

PROGRAM SYNOPSIS

The details regarding the GPS and SPS materials 

sampling and testing programs can be reviewed 

through the documents in the Resources section of this 

TechBrief. For a description of each experiment, please 

see the Long-Term Pavement Performance Information 

Management System (IMS) User Guide, pages 4 (GPS) 

and 5 (SPS), which are available on the homepage of 

LTPP InfoPave at https://infopave.fhwa.dot.gov/ 

(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 both the approach and leave ends. Therefore, for 

each layer, there were at least two samples/specimens 

taken and tested, one from the approach end and one from 

the leave end. Destructive sampling within test section 

limits would violate LTPP practices because it would 

interfere with long-term performance trends.

It is important to note that, with one exception (GPS 

overlays after 1989), the test results for the GPS sections 

represent different ages of the pavement and were not 

taken at the time of construction, because these sections 

were in-service test sections that were not constructed 

specifically for the LTPP program.

SPS Test Sections

For SPS-1, -2, -5, -6, -7, -8, -9, and -10, which consist 

of multiple co-located test sections, a different approach 

was taken. In general, for SPS projects (besides SPS-3 

and SPS-4), several samples of the different layers 

and materials were extracted, 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) were taken. These 

samples were tied to a particular test section as illustrated 

in figure 2. In this example, three bulk samples of the 

subgrade and dense-graded aggregate base were taken 

within the project adjacent to sections 1, 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, and the full suite of materials tests was conducted 

at each location. For several SPS projects, the materials 

were sampled and tested during the construction process.

For each individual SPS project site (besides SPS-3 

and SPS-4), the LTPP program developed customized 

materials sampling and testing plans, which can be 

downloaded from https://infopave.fhwa.dot.gov/Reports/

Library (FHWA 2022b).

For SPS-3 and -4 projects, there was no sampling or 

testing of the layers because these were maintenance 

experiments, and only the surface of the pavement was of 

concern. However, each SPS-3 and -4 site generally had a 

GPS test section near it. Typically, the material properties 

measured for the GPS section were used for the SPS-3 

and -4 test sections, as illustrated in figure 3.

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

Source: FHWA.
4

Figure 2. Illustration. Example of unbound materials sampling plan for an LTPP SPS project.

Source: FHWA.

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

Source: FHWA.

DEVELOPMENT PROCESS

The ARMAD was developed by using a phased 

approach by material type in the following order:

1. Unbound granular base, subbase, and subgrade 

materials.

2. PCC materials.

3. AC materials.

4. Stabilized (chemically treated) materials.

5. Other layer material types, such as surface 

treatments and engineering fabrics.

The development process for each type of material 

entailed the following steps:

1. Selecting material properties—selection of the 

properties to be included in the LTPP ARMAD.

2. Data discovery—determination of the location of 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—a decision tree was 

used to derive such values. 

4. Populating the LTPP database—the material 

properties data were populated in the LTPP database.

5. Disseminating data—the final dataset was 

disseminated via the LTPP InfoPave web portal 

(FHWA 2022a).

 

Note: the distance between GPS and SPS test sections varied. 
5

Selecting Material Properties

The development of the LTPP ARMAD began with 

identifying the key pavement layer material properties by 

layer type (Afsharikia et al. Forthcoming). The ARMAD 

contains an essential set of material properties necessary 

to characterize each layer and effectively analyze 

pavement performance or calibrate pavement performance 

models, specifically (but not exclusively) the American 

Association of State Highway Transportation Officials 

(AASHTO) Pavement Mechanistic-Empirical (ME) 

software (Afsharikia et al. Forthcoming, AASHTO 

2020). The criteria for the selection of these 

properties are described in the following sections.

Fundamental Material Properties

Fundamental material properties such as thickness, 

material type, gradation, volumetric properties, and so 

forth were included in the dataset.

Importance to Pavement Performance Analyses 

To direct the focus of this work to the most important 

material characteristics, the material properties inputs for 

the AASHTO Mechanistic-Empirical Pavement Design 

Guide (MEPDG) analysis and performance models 

were considered (AASHTO 2020). LTPP data are often 

used for calibrating MEPDG distress prediction models 

or for MEDPG performance analysis (Afsharikia et al. 

Forthcoming; AASHTO 2020). 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 base/subbase, were not measured on 

every pavement section due, in most cases, to limited 

resources for performing laboratory tests. In the absence 

of laboratory test results, correlation equations and/or 

prediction models provide an alternative for estimating 

these properties based on other material properties. 

The ARMAD also contains correlated material 

properties, which are useful in calculating missing 

properties and developing newer correlation equations 

(Afsharikia et al. Forthcoming).

Key Properties Availability

The list of key material properties selected for 

representation in the ARMAD is presented in table 1 

(Afsharikia et al. Forthcoming). 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 1, which indicates whether 

a given material property is measured by the LTPP 

program. Notably, even if a material property is measured 

by the LTPP program, there are still some layers where 

the indicated material property will be missing for a 

given class of experiments (GPS or SPS), individual 

experiments, or test sections.

Table 1. List of key material properties.

Category Material Element Availability in LTPP Database

Unbound base/ subbase/

subgrade materials

Layer thickness Yes

Material type Yes

Poisson’s ratio Yes

Resilient modulus Yes

Soil classification 

(gradation and Atterberg limits) Yes

Compaction characteristics 

(optimum moisture content 

and maximum dry density)

Yes

Specific gravity Yes

Resilient modulus and parameters Yes

Saturated hydraulic conductivity Yes

PCC mixture

Layer thickness Yes

Material type Yes

Poisson’s ratio No

Modulus of rupture Yes

Modulus of elasticity Yes
6

Table 1. List of key material properties. (Continued)

Category Material Element Availability in LTPP Database

PCC mixture

Compressive strength Yes

Tensile strength Yes

Coefficient of thermal expansion Yes

Mixture properties 

(unit weight, water-to-cement 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 

(G*) and phase angle (δ)—set of 

values at different frequencies and 

temperature to create master curve

Yes

Binder type Yes

Viscosity Yes

Dynamic modulus (E*)—set of 

values at different temperatures and 

frequencies to build a master curve

Yes

Tensile strength Yes

Creep compliance Yes

Volumetric properties (unit weight, bulk 

specific gravity, 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
7

Table 1. List of key material properties. (Continued)

Category Material Element Availability in LTPP Database

Chemically stabilized materials

Heat capacity No

Thermal conductivity No

Resilient/elastic modulus No

Compressive strength Yes

Surface treatments and 

engineering fabrics

Layer thickness Yes

Material type Yes

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

Data Discovery

The next step 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 because it has 

already gone through a section-by-section layering 

reconciliation process and is the most representative 

source of information for layer thickness, material type, 

and material classification. The layer thickness for the 

LTPP ARMAD was derived directly from table 1.

To retrieve data for an available material property from 

the LTPP database, the tables containing the indicated 

property were selected. The LTPP IMS User Guide was 

used extensively to identify the relevant LTPP source 

data tables (Elkins and Ostrom 2021).

For each material property indicated as available, a 

preliminary list of the appropriate LTPP tables was 

developed; then, 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 evaluated. Finally, the 

table with the most representative values was selected 

along with secondary tables, if they existed.

Deriving Representative Values

After the 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 models 

were used to evaluate the variability and select the most 

representative values or predict missing values. These 

decision trees are explained further in the final report.

For engineering fabric 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 the LTPP Database

The final data and metadata resulting from this effort 

were stored in the LTPP database. 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 contain 

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. 

This table can be used by practitioners and researchers 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 about 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, coefficient 

of variation (COV), number of samples used in the 

calculation, and the source variability code of the value, 

as appropriate.
8

Data Tables

Table 2 lists the tables created in the PPDB. The collective 

set of tables contains more than 1 million records.

Contents of Each ARMAD Material Property Table

In general, the contents of each material property table 

include a description of the section and the layer type 

by section layer. Specifically, the contents include 

information that identifies a specific layer in a test 

section. The contents also include information on all 

material properties for each given layer, as shown in 

table 1. Thereby, the user can determine and use all of 

the materials information derived for ARMAD for each 

layer of the test section. These tables will likely be of 

primary interest to researchers because they provide the 

most representative value for a given material property, as 

guided by the ARMAD subject matter experts to the LTPP 

program. These tables should be considered as an ARD.

Table 2. List of tables created for ARMAD in the LTPP database.

Table Name Description

ANALYSIS_TST_UNBOUND Representative properties of unbound base, 

subbase, and subgrade materials.

ANALYSIS_TST_UNBOUND_SUPPORT Support information for the unbound base, 

subbase, and subgrade material properties.

ANALYSIS_TST_PCC Representative properties of PCC materials.

ANALYSIS_TST_PCC_SUPPORT Support information for the PCC material properties.

ANALYSIS_TST_AC Representative properties of AC materials.

ANALYSIS_TST_AC_SUPPORT Support information for the AC material properties.

ANALYSIS_TST_AC_ESTAR AC dynamic modulus data generated from an analysis study 

(Kim et al. 2011). There is presently no support table. 

ANALYSIS_TST_AC_CREEP_COMP AC creep compliance testing properties.

ANALYSIS_TST_AC_CRCOM_SUPPORT Support information for the AC creep compliance values.

ANALYSIS_TST_ACT Representative properties of asphalt-treated base 

and subbase testing materials.

ANALYSIS_TST_ACT_SUPPORT Support information for the asphalt-treated base 

and subbase material properties.

ANALYSIS_TST_PCT Representative properties of cement-treated base 

and subbase materials.

ANALYSIS_TST_PCT_SUPPORT Support information for cement-treated base 

and subbase materials.

ANALYSIS_TST_TR Representative properties of treatment layers.

Contents of Each ARMAD Support Table

In general, the contents of each material support table 

include a set of key fields that identify the layer within 

the section and the State, provide a description of the 

layer, and provide the basis for the material property 

value. The support table also includes the minimum, 

maximum, average, median, standard deviation, COV, 

and sample count for each material property, as applicable. 

The user can merge the support table with the ARMAD 

material property table by using the set of key fields to 

further filter the data.

The data element, which is termed REP_CODE_

SOURCE_VARIABILITY in the ANALYSIS_TST_*_

SUPPORT tables, is very important because it informs the 

user about the relative quality of the individual material 

property. A one- or two-character code comprises the 

element. The first character shows the data source, as 
9

Table 3. 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 estimated by basing them on the 

MEPDG or other reliable sources.

Table 2. List of tables created for ARMAD in the LTPP database. (Continued)

Table Name Description

ANALYSIS_TST_TR_SUPPORT Support information for treatment layer types.

ANALYSIS_TST_EF Representative properties of engineering fabric layers.

ANALYSIS_TST_EF_SUPPORT Support table for engineering fabric layers.

shown in table 3. The second character, when present, 

characterizes the representative source code variability. If 

the second character is not present, it means the variability 

is unknown or not applicable.

The specific value for each classification is described 

as follows:

Measured Values—Code A

These properties were directly measured by using 

standard testing in the LTPP program. 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 whether all 

tests were representative. If they were, an average or 

median (depending on data dispersion) was used. If 

not, then available data were assessed to determine 

the best value based on 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 an identified threshold 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. The approach described in 

this paragraph was also used for the remaining data source 

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

Calculated Values—Code B

These properties were calculated by 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 to ensure that users could derive their 

own resilient modulus values at a specified bulk stress. 

However, for the purposes of the ARMAD, one value was 

desired, and thus it was calculated assuming typical bulk 

stress values for different layers.

Expanded Values—Code C

In this case, the material properties for a given section 

are 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 that could 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 in the SPS-3 

and -4 test sections, the materials on the test sections were 
10

not themselves sampled. The material properties may 

be derived from an adjacent GPS test section that was 

sampled and tested, because SPS-3 and -4 test sections 

typically had GPS test sections nearby.

Imputed Values—Code D

Imputed values are properties that are estimated by 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 values against the derived mean 

percentages for the same material type.

Inventory Values—Code E

In some cases, inventory data were used, such as when 

testing was not conducted or when a material test did not 

otherwise have a value for a given layer. Inventory data 

are gathered from agency records or agency specifications 

or from reported maintenance/rehabilitation data. These 

data were used on a case-by-case basis.

Assumed Values—Code F

Default values from the AASHTO Pavement ME Design 

software were used for values for which there were no 

data in the LTPP IMS (Afsharikia et al. Forthcoming; 

AASHTO 2020). This classification does not have a COV 

designation because these are assumed values, so they are 

one-character values.

A schematic of the classification system is shown in 

table 4. In general, as the code goes from A to B to C, and 

so forth, the less confidence there is in the value; however, 

for all cases, the best value was selected for a particular 

layer and particular material property.

Tracking Changes in Pavement Structure Over Time

Each time an LTPP test section’s characteristics change 

because of rehabilitation treatments or the application 

of maintenance treatments, the section is assigned a 

new construction number (CN) in the TST_L05B table. 

When a pavement section is first accepted into the LTPP 

program, it is assigned a CN of 1. The CN is incremented 

by one for each successive maintenance or rehabilitation 

event. It was necessary to assign material properties to 

all layers in the section for all construction events to 

perform the LTPP ARMAD process (Afsharikia et al. 

Forthcoming).

Disseminating Data

The LTPP program uses InfoPave to disseminate data and 

information to users. This data portal can be accessed at 

https://infopave.fhwa.dot.gov/ (FHWA 2022a). The PPDB 

tables explained previously can be found in the Data 

hub under Standard Data Release in LTPP InfoPave at 

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 section at 

https://infopave.fhwa.dot.gov/Data/DataSelection

(FHWA 2022d). This download method enables users 

to access an easy-to-use intuitive interface to select and 

download data of their choosing.

INTENDED USE OF THE ARMAD

The ARMAD is the end product of a materials 

data-wrangling effort for the LTPP database to facilitate 

future work by LTPP data users and researchers 

(Afsharikia et al. Forthcoming). The ARMAD has 

many uses in the engineering profession. As mentioned 

previously, the ARMAD is a consolidated summary 

of the vast amount of material properties information 

contained in the LTPP database, along with inputted and 

calculated values. These material properties were selected 

based on the 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 AASHTO Pavement ME software 

(AASHTO 2020).

Examples of the types of studies that can be performed 

with the ARMAD dataset include the following 

(Afsharikia et al. Forthcoming):

• 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 MEPDG (AASHTO 2020).

• Evaluation of the impact of material properties 

on pavement maintenance and rehabilitation.

As with any dataset, caution should be exercised with 

these data, and the assumptions made to populate the 

dataset should be fully understood prior to using it. 

The dataset is a compilation of many data tables and 
11

Table 4. Classification system used for REP_CODE_SOURCE_VARIABILITY field.

DATA SOURCE 

GROUP DESCRIPTION DATA SOURCE CODE SUB-GROUPS

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 by 

using the PROJECT_

LAYER_CODE.

C

Variability unknown or not applicable C

More than one measured value 

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
12

data values, including directly measured, calculated, 

expanded, imputed, derived from inventory data, and 

assumed. Ultimately, the purpose of developing the 

ARMAD—using the best assumptions and algorithms 

known to LTPP at this time—was to eliminate much of 

the data-wrangling effort commonly done by researchers 

to make LTPP data extraction easier.

RESOURCES

The following resource documents are useful in 

understanding the LTPP material characterization 

program. These documents are downloadable from 

https://infopave.fhwa.dot.gov/Reports/Library

(FHWA 2022b):

• FHWA. 1994. Specific Pavement Studies, Material 

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, Material 

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, Material 

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.

• NAS NRC. 1991. Field Material Sampling and 

Testing Guide SHRP-LTPP Guide for Field Materials 

Sampling, Testing, and Handling. Operational 

Memorandum No. SHRP-LTPP-OG-006. Washington, 

DC: United States National Academies of Sciences 

(NAS), National Research Council (NRC).

• NAS NRC. 1991. Specific Pavement Studies, Material 

Sampling and Testing Requirements for Experiment 

SPS-5: Rehabilitation of Asphalt Concrete Pavements. 

Operational Memorandum No. SHRP-LTPP-OM-014. 

Washington, DC: National Academy of Sciences, 

National Research Council.

• NAS NRC. 1991. Specific Pavement Studies, Material 

Sampling and Testing Requirements for Experiment 

SPS-6: Rehabilitation of Jointed Portland Cement 

Concrete Pavements. Operational Memorandum No. 

SHRP-LTPP-OM-019. Washington, DC: National 

Academy of Sciences, National Research Council.

• NAS NRC. 1991. Specific Pavement Studies, Material 

Sampling and Testing Requirements for Experiment 

SPS-7: Bonded Portland Cement Concrete Overlays.

Operational Memorandum No. SHRP-LTPP-OM-020. 

Washington, DC: National Academies of Sciences, 

National Research Council.

• NAS NRC. 1992. Specific Pavement Studies, Material 

Sampling and Testing Requirements for Experiment 

SPS-8: Study of Environmental Effects in the Absence 

of Heavy Loads. Operational Memorandum No. 

SHRP-LTPP-OM-030. Washington, DC: National 

Academy of Sciences, 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.

REFERENCES

Afsharikia, N., J. Groeger, L. Garner, B. Ostrom, and G. 

Rada. Forthcoming. Development and Use of the LTPP 

Analysis-Ready Materials Dataset. Washington, DC: 

Federal Highway Administration.

AASHTO. 2020. Mechanistic-Empirical Pavement 

Design Guide: A Manual of Practice, 3rd ed. 

Washington, DC: American Association of State 

Highway and Transportation Officials.

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.

FHWA. 2015. The Long-Term Pavement Performance 

Program. Report No. FHWA HRT-15-049. 

Washington, DC: Federal Highway Administration.

FHWA. 2022a. “LTTP InfoPave” (web page). 

https://infopave.fhwa.dot.gov/, last accessed 

March 1, 2022.

FHWA. 2022b. “LTTP InfoPave: Library” (web page). 

https://infopave.fhwa.dot.gov/Reports/Library, last 

accessed March 1, 2022.

FHWA. 2022c. “LTTP InfoPave: Data: Standard Data 

Release” (web page). https://infopave.fhwa.dot.gov/Data/

StandardDataRelease/, last accessed March 1, 2022.

FHWA. 2022d. “LTPP InfoPave: Data: Data Selection” 

(web page). https://infopave.fhwa.dot.gov/Data/

DataSelection, last accessed March 1, 2022.
13

Researchers—This study was conducted by Wood Environment & Infrastructure Solutions, Inc. under contract 

number 693JJ320D000025. The primary developer of this dataset was Zahra Niosha Afsharikia, Ph.D. The project 

manager at the start of this effort was Jonathan Groeger, but he was replaced by Gonzalo Rada, Ph.D., P.E., 

before its completion. Guidance and assistance were provided by Barbara Ostrom, P.E., and Lauren Gardner, P.E.

Charles Schwartz (subcontractor), Ph.D., George Chang, Ph.D., P.E. (subcontractor), and Harold Von Quintus 

(subcontractor) were subject matter experts for this study.

Distribution—This TechBrief is being distributed according to a standard distribution. Direct distribution is being 

made to the FHWA divisions and Resource Center.

Availability—This TechBrief may be obtained at https://highways.dot.gov/research.

Key Words—Long-Term Pavement Performance (LTPP), materials, subgrade, unbound materials, portland 

cement concrete, asphalt concrete, engineering fabrics, interlayers, ARMAD, General Pavement Studies, Specific 

Pavement Studies, AASHTO Pavement ME, AASHTO MEPDG, InfoPave.

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 TechBrief 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.

Recommended citation: Federal Highway Administration, 

Introduction to the LTPP Analysis Ready Materials 

Dataset (ARMAD) (Washington, DC: 2022) 

OCTOBER 2022 https://doi.org/10.21949/1521944.

FHWA-HRT-22-114

HRDI-30/10-22(WEB)E

Kim, Y. R., 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.

Manning, D. G. 1986. Strategic Highway Research 

Program Research Plans, Final Report. Washington, 

DC: Transportation Research Board of the 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.