Development of differential scheme micromechanics modeling framework for predictions of hot-mix asphalt (HMA) complex modulus and experimental validations

摘要

The viscoelastic modulus of hot-mix asphalt (HMA) such as the complex modulus, E*, is an essential material parameter for better paving mixture design and asphalt pavement design. Under certain circumstances, it is desirable that a reasonable modulus value of certain HMA mixtures be estimated for this purpose. Empirical and semi empirical models have been proposed and used. However, these non-fundamental approaches have significant drawbacks, particularly with application of the model for materials that vary from those used in the calibration of the model, and their reliance on large calibration data sets, which led to introducing some fuzzy factors in their predictions. In order to overcome the limitations of an empirical approach, a fundamental micromechanics modeling framework based on the differential scheme effective medium theory has been developed and introduced herein. To verify and validate the prediction accuracy and applicability, a series of various asphalt-aggregate mixtures starting from the homogeneous asphalt binder phase up to a very highly packed composite of dense HMA mixtures were produced in the lab by progressively increasing the aggregate volume concentration in the composite from 0 to nearly 0.9. These various mixtures were tested in the Hollow Cylinder Tensile Tester (HCT) to obtain the extensional complex modulus (E*) at three low temperatures within -25 to 5 oC range and at various loading frequencies from 10 Hz to 0.01 Hz. Comparisons between the model predicted E* and the experimental E* showed good agreement with reasonable accuracies. Remaining challenges for the practical implementation of the proposed model such as the applicability at intermediate to high temperature materials property prediction and particle orientation effects were discussed based on the analysis and additional model predictions for an independent experimental data set.