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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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The goal of this study was to develop predictive models to estimate material properties and pavement engineering properties for use in routine practice. The study focused on rigid pavement and relevant material types, primarily Portland cement concrete (PCC) materials, stabilized materials, and unbound materials, including subgrade soils. As such, the objectives of this study were as follows:

In recent years, pavement engineering practices have emphasized the importance of proper material characterization to optimize pavement performance. Procedures like the Mechanistic-Empirical Pavement Design Guide (MEPDG) use various material property inputs for pavement performance prediction.(2) The greater need for estimating material properties is being addressed only to a limited extent with the currently available resources. Reliable correlations between material parameters and index properties offer a cost-effective alternative and are equivalent to the level 2 MEPDG inputs. These models can also support agencies in improving QC/QA specifications and pavement management functions.



The Long-Term Pavement Performance (LTPP) study database, which contains material property test results and material index properties, provided the necessary data to develop the models in this study. The most recent version of the LTPP database that was available at the time of the study, Standard Data Release 23.0, was used.(3) Material properties and pavement engineering properties for which develop predictive models were developed were selected based on the following:

Predictive models were developed for PCC compressive strength, PCC flexural strength, PCC elastic modulus, PCC tensile strength, lean concrete base (LCB) modulus, and unbound materials resilient modulus. In addition, rigid pavement design feature input properties were developed using the MEPDG calibration data. These include the jointed plain concrete pavement (JPCP) and continuously reinforced concrete pavement (CRCP) deltaT parameters, where deltaT is defined as the equivalent temperature differential that corresponds to the effective permanent curl-warp locked into the pavement. For all PCC material properties, multiple models were developed for use in different project situations and also provided users with prediction model alternatives depending on the extent of mix design information available.

In developing the models, a uniform set of statistical criteria were used to select independent parameters to define a relationship as well as to mathematically formulate prediction functions. The analyses examined several statistical parameters in choosing the optimal model and in determining the predictive ability of the model. In general, the optimal set of independent variables (through the Mallows coefficient, Cp), the interaction effects (through the variance inflation factor (VIF)), the significance of the variable (through the p-value), and the goodness of fit (through the R2 value) were verified. Additionally, the study validated or refined existing models and developed new relationships. In the analyses, the following general observations were made:



The following models have been developed under this study.

PCC compressive strength models include the following:

PCC flexural strength models include the following:

PCC elastic modulus models include the following:

PCC indirect tensile strength model is as follows:

PCC CTE models include the following:

The JPCP design deltaT model is as follows:

The CRCP design deltaT model is as follows:

The lean concrete base elastic modulus is as follows:

The unbound materials resilient modulus is as follows:


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