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Advances in Materials Science and Engineering
Volume 2015, Article ID 937126, 8 pages
Research Article

Multiscale Validation of the Applicability of Micromechanical Models for Asphalt Mixture

1School of Highway, Chang’an University, Shaanxi, Xi’an 710064, China
2School of Materials Science and Engineering, Chang’an University, Shaanxi, Xi’an 710064, China

Received 22 April 2015; Revised 22 July 2015; Accepted 28 July 2015

Academic Editor: Antônio G. B. de Lima

Copyright © 2015 Jiupeng Zhang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Asphalt mixture is more complicated than other composite materials in terms of the higher volume fraction of aggregate particles and the viscoelastic property of asphalt matrix, which obviously affect the applicabilities of the micromechanical models. The applicabilities of five micromechanical models were validated based on the shear modulus of the multiscale asphalt materials in this paper, including the asphalt mastic, mortar, and mixture scales. It is found that all of the five models are applicable for the mastic scale, but the prediction accuracies for mortar and mixture scales are poorer. For the mixture scale, all models tend to overestimate at the intermediate frequencies but show good agreement at low and high frequencies except for the Self-Consistent (SC) model. The Three-Phase Sphere (TPS) model is relatively better than others for the mortar scale. The applicability of all the existing micromechanical models is challenged due to the high particle volume fraction in the multiscale asphalt materials as well as the modulus mismatch between particles and matrix, especially at the lower frequencies (or higher temperatures). The particle interaction contributes more to the stiffening effect within higher fraction than 30%, and the prediction accuracy is then deteriorated. The higher the frequency (or the lower the temperature) is, the better the model applicability will be.