Modulus Characterization and Prediction of Asphalt Mixture Using Self-Consistent Micromechanics

The dynamic modulus of asphalt mixture is crucial to its performance, and various methodologies have been developed to predict it. Because asphalt mixture comprises three components (aggregate, asphalt binder, and air voids), predicting the dynamic modulus from aggregate and asphalt binder properties has been proven effective in previous studies. Based on the self-consistent micromechanics, this study rigorously evaluates its forward and backward models for the prediction of the dynamic modulus of an asphalt mixture, considering Poisson’s ratio as well as aggregate gradation. In addition, the influence of aggregate and binder types on the modulus predictability of the asphalt mixture has also been investigated and demonstrated. In practical scenarios where the average aggregate moduli, determined through the backward model, are required as inputs for the forward model, prediction accuracy may slightly decrease compared to using the true aggregate moduli as inputs. However, it remains more accurate than Witczak’s dynamic modulus predictive model. Furthermore, sensitivity analyses were conducted on modulus prediction and the corresponding structural performance using finite-element simulations. While the self-consistent model results are generally reasonable, the uncertainty regarding structural fatigue predictions merits attention.