Three-Dimensional Micromechanical Complex-Modulus Prediction of Asphalt Concrete Considering the Aggregate Interlocking Effect
Publication: Journal of Materials in Civil Engineering
Volume 29, Issue 10
Abstract
The complex modulus () is a fundamental material property extensively used for characterizing the viscoelastic behavior of asphalt concrete. In recent years, numerous micromechanics-based models have been proposed for predicting the asphalt concrete . Unfortunately, few of them are capable of rationally considering the aggregate interlocking effect that plays a vital role in the reinforcement mechanisms of asphalt concrete. To address this issue, this study presents a new approach, in which the asphalt mastic matrix phase in the traditional models is substituted with a new equivalent matrix phase that incorporates both the viscoelastic properties of asphalt mastic and the effect of aggregate interlock reinforcement. Two interlock factor functions, which were initially proposed for characterizing the confinement dependency of the triaxial of asphalt concrete, are introduced into the two springs, two parabolic elements and one dashpot (2S2P1D) model representing the complex shear modulus () of the original asphalt mastic matrix phase. The feasibility and effectiveness of the approach is demonstrated by means of the traditional two-layer built-in (TLB) and generalized self-consistent (GSC) micromechanics models. The results show that the proposed method overcomes the shortcomings of underpredicting the storage and loss moduli of asphalt concrete over the intermediate- and low-frequency range in the traditional micromechanics methods. Also, without changing any original geometries, the advantages of simplicity and practicability of the traditional models remain. Finally, recommendations for future research are discussed in brief.
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©2017 American Society of Civil Engineers.
History
Received: Jun 14, 2016
Accepted: Mar 1, 2017
Published online: Jun 9, 2017
Published in print: Oct 1, 2017
Discussion open until: Nov 9, 2017
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