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

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EXECUTIVE SUMMARY

BACKGROUND

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:

• Identify a set of material engineering properties for which predictive relationships would be useful in pavement design, construction quality control/quality assurance (QC/QA), and pavement management applications.
• Establish and/or validate relationships between the identified engineering properties and routine test results, index properties, and other readily available information.
• Develop a practical guide accompanied by user friendly software incorporating the recommendations.

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.

MODEL DEVELOPMENT

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:

• Material inputs requirements for the MEPDG design procedure and the sensitivity of the specific parameter for performance prediction.
• Typical agency needs for determining material properties during QA.
• Typical agency needs for determining material properties during routine pavement management functions.
• Data availability in the LTPP database.

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:

• PCC compressive strength could be correlated to several index properties. It was found to increase with decreasing water/cement (w/c) ratio and increasing cementitious materials content (CMC), curing time, and unit weight while decreasing maximum aggregate size (MAS) for a given level of w/c ratio and fineness modulus (FM) of the sand.
• PCC flexural strength could be correlated to the compressive strength using a power model. These relationships were validated and refined using the LTPP data. It also could be correlated to the w/c ratio, unit weight, CMC, and curing time. The correlation was improved significantly in the new models with the additional parameters. The flexural strength increased proportionally with all parameters listed except w/c ratio, with which it had an inverse relationship.
• PCC elastic modulus could be correlated to the compressive strength and unit weight using a power model, as has been done in past studies. These relationships were validated and verified with the data used in this study. Predictions could be made based on aggregate type, unit weight, compressive strength, and age with improved correlation. The elastic modulus increased with an increase in magnitude of all parameters listed.
• PCC tensile strength was found to correlate well with the compressive strength using a power relationship.
• The coefficient of thermal expansion (CTE) of PCC was most sensitive to the coarse aggregate type and the volumetrics of the mix design.
• JPCP deltaT negative gradient increased with an increase in temperature range at the project location for the month of construction and slab width and increased with a decrease in PCC thickness, unit weight, w/c ratio, and latitude of the project location.
• CRCP deltaT negative gradient increased with an increase in maximum temperature at the project location for the month of construction and maximum temperature range and decreased with the use of chert, granite, limestone, and quartzite.
• The modulus of LCB correlated well with its 28-day compressive strength based on a power model.
• The prediction of resilient modulus was possible using parameters k1, k2, and k3 of the constitutive model as follows:
  • The parameter k1 increased with decreasing percent passing the 1/2-inch sieve, increasing liquid limit, and decreasing optimum moisture content.
  • The parameter k2 increased with decreasing percent passing the No. 80 sieve, decreasing liquid limit and percent gravel, and increasing maximum particle size of the smallest 10 percent of the soil sample.
  • The parameter k3 was dependent on the soil classification (coarse-grained versus fine-grained materials).

LIST OF MODELS

The following models have been developed under this study.

PCC compressive strength models include the following:

• Compressive strength model 1—28-day cylinder strength model.
• Compressive strength model 2—Short-term cylinder strength model.
• Compressive strength model 3—Short-term core strength model.
• Compressive strength model 4—All ages core strength model.
• Compressive strength model 5—Long-term core strength model.

PCC flexural strength models include the following:

• Flexural strength model 1—Flexural strength based on compressive strength.
• Flexural strength model 2—Flexural strength based on age, unit weight, and w/c ratio.
• Flexural strength model 3—Flexural strength based on age, unit weight, and CMC.

PCC elastic modulus models include the following:

• Elastic modulus model 1—Model based on aggregate type.
• Elastic modulus model 2—Model based on age and compressive strength.
• Elastic modulus model 3—Model based on age and 28-day compressive strength.

PCC indirect tensile strength model is as follows:

• PCC indirect tensile strength model—Model based on compressive strength.

PCC CTE models include the following:

• CTE model 1—CTE based on aggregate type (level 3 equation for MEPDG).
• CTE model 2—CTE based on mix volumetrics (level 2 equation for MEPDG).

The JPCP design deltaT model is as follows:

• JPCP deltaT model—JPCP deltaT gradient based on temperature range, slab width, slab thickness, PCC unit weight, w/c ratio, and latitude.

The CRCP design deltaT model is as follows:

• CRCP deltaT model—CRCP deltaT gradient based on maximum temperature, maximum temperature range, and aggregate type.

The lean concrete base elastic modulus is as follows:

• Elastic modulus model—Elastic modulus based on 28-day compressive strength.

The unbound materials resilient modulus is as follows:

• Resilient modulus model—Resilient modulus using constitutive model based on gradation, Atterberg limits, optimum moisture content, and soil classification.