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Publication Number: FHWA-HRT-12-030 Date: August 2012

Publication Number: FHWA-HRT-12-030
Date: August 2012

Estimation of Key PCC, Base, Subbase, and Pavement Engineering Properties From Routine Tests and Physical Characteristics

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CHAPTER 3. LITERATURE REVIEW (3)

SELECTION OF INDEPENDENT VARIABLES FOR THE PREDICTION OF MATERIAL ENGINEERING PROPERTIES

The information collected from literature was used to identify the independent variables or index properties used to predict the material engineering properties identified in chapter 2. The independent variables that the researchers considered most likely to be included in deriving the prediction models for PCC, stabilized, and unbound materials are listed in table 6 through table 8, respectively.

It was envisioned that more than one prediction model might be required or might be derived with the data available in the LTPP database. Multiple models are significant for use in different projects (e.g., new design versus rehabilitation versus pavement management) or stages of pavement life. For example, flexural strength correlations for PCC materials will be derived using index properties that can be useful during the design stage if mix design or optimization is performed. However, a correlation to compressive strength from a core would be useful for predicting the performance of the as-constructed pavement during the QA stage or in pavement management applications.

Data selection, analyses, and statistical modeling are discussed in detail in chapters 4 and 5 of this report. Predictive models can be based on lab or field test data, such as with the prediction of flexural strength based on compressive strength or index properties. Alternatively, correlations can be drawn to categorical variables, such as with PCC CTE. CTE can be a function of mix components and proportioning or a function of aggregate type. The latter option provides SHAs with the opportunity to recommend default values for CTE (as is being done for the MEPDG).

MEPDG calibration data were included as inputs to develop prediction models for design feature inputs (see chapter 5 for further discussion). These variables include the following:

deltaT for JPCP designs.
deltaT for CRCP designs.
Erosion in CRCP design.
EI for JPCP design.

Table 6. Potential or likely independent variables to derive prediction models for PCC material properties or design features for rigid pavements.

Material Property	Constant or Time Dependent	Independent Variables		Comments
Material Property	Constant or Time Dependent	Primary Model	Secondary Model	Comments
Rehabilitation of New PCC Slab
Compressive strength	Time dependent	Aggregate type, cement content, air content, w/c, unit weight, gradation, admixtures, SCMs, and age	N/A	Prediction for 28-day strength and long-term strength in separate models; strength gain model to be updated
Elastic modulus	Time dependent	Aggregate type, cement content, air content, w/c, unit weight, admixtures, and SCMs	Compressive strength/ flexural strength	Prediction for 28-day value and long-term values in separate models
Flexural strength	Time dependent	Aggregate type, cement content, air content, w/c, unit weight, admixtures, and SCMs	Compressive strength	Prediction for 28-day strength and long-term strength in separate models; strength gain model to be updated
Indirect tensile strength (CRCP only)	Time dependent	Compressive strength/flexural strength	N/A
CTE	Constant	Coarse and fine aggregate type, aggregate CTE, coarse and fine aggregate volume, paste volume, and w/c ratio	Aggregate type	Default PCC CTE for each aggregate type and model based on mix design
deltaT for JPCP and CRCP design*	Time dependent	Base type, construction time, PCC index properties, and climatic variables	N/A	Data in MEPDG JPCP and CRCP calibration to be used
Erosion in CRCP design**	Time dependent	Base type, index properties and strength of base, and climate (precipitation)	N/A	Data in MEPDG CRCP calibration models to be used
EI—JPCP**	N/A	Base type, base properties, and climate (precipitation)	N/A	Data in MEPDG JPCP calibration models to be used

Rehabilitation of Existing PCC Slab
Compressive strength	Time dependent	Same as for parameters used in new design
Elastic modulus	Time dependent	Same as for parameters used in new design
Flexural strength	Time dependent	Same as for parameters used in new design

N/A = Not applicable.*Construction dependent. **Base dependent but listed in PCC properties because it is considered a design feature for JPCP or CRCP design.

Table 7. Independent variables to derive prediction models for stabilized materials.

Material Type	Material Property	Constant or Time Dependent	Independent Variables
Lean concrete and cement-treated aggregate	Elastic modulus	Constant	Compressive strength
Lean concrete and cement-treated aggregate	Flexural strength* (for HMA pavement design)	Constant	Compressive strength
Lime-cement-fly ash	Resilient modulus	Time dependent	Unconfined compressive strength or index properties (soil type, Atterberg limits, and gradation)
Soil cement	Resilient modulus	Time dependent	Unconfined compressive strength or index properties (soil type, Atterberg limits, and gradation)
Lime-stabilized soil	Resilient modulus	Time dependent	Unconfined compressive strength or index properties (soil type, Atterberg limits, and gradation)
All material types listed above	Unconfined compressive strength	Time dependent	Soil type, Atterberg limits, and gradation

*Construction dependent.

Table 8. Independent variables to derive prediction models for unbound materials.

Material Property

Constant or Time Dependent

Independent Variables

Comments

Resilient modulus determined using the following two options:

· Regression coefficients k₁, k₂, and k₃ for the generalized constitutive model that defines resilient modulus as a function of stress state and regressed from lab resilient modulus tests.

· Determine the average design resilient modulus for the expected in-place stress state from laboratory resilient modulus tests.

Time dependent

Soil type, Atterberg limits, maximum dry density, optimum moisture content, gradation, and the percent passing the #200 sieve, P₂₀₀.

Analyses will verify several options and combinations of grouping data

Page Owner: Office of Research, Development, and Technology, Office of Infrastructure, RDT

Topics: research, infrastructure, pavements and materials
Keywords: research, infrastructure, pavements and materials, Pavements, LTPP, material properties, MEPDG, prediction model, Index properties
TRT Terms: research, facilities, transportation, highway facilities, roads, parts of roads, pavements
Scheduled Update: Archive - No Update needed

This page last modified on 09/26/2012

Estimation of Key PCC, Base, Subbase, and Pavement Engineering Properties From Routine Tests and Physical Characteristics

CHAPTER 3. LITERATURE REVIEW (3)

SELECTION OF INDEPENDENT VARIABLES FOR THE PREDICTION OF MATERIAL ENGINEERING PROPERTIES

Table 6. Potential or likely independent variables to derive prediction models for PCC material properties or design features for rigid pavements.

Material Property

Constant or Time Dependent

Independent Variables

Comments

Primary Model

Secondary Model

Rehabilitation of New PCC Slab

Rehabilitation of Existing PCC Slab

Table 7. Independent variables to derive prediction models for stabilized materials.

Material Type

Material Property

Constant or Time Dependent

Independent Variables

Table 8. Independent variables to derive prediction models for unbound materials.

Material Property

Constant or Time Dependent

Independent Variables

Comments