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Effects of Bitumen Origins and Filler Types on the Micromechanics Prediction of Complex Modulus of Asphalt Mastics Considering Interparticle Interactions.

Authors :
Xu, Jiaqiu
Fan, Zepeng
Lu, Guoyang
Wang, Dawei
Liu, Pengfei
Source :
Journal of Materials in Civil Engineering. Jan2024, Vol. 36 Issue 1, p1-17. 17p.
Publication Year :
2024

Abstract

Asphalt mastic is a binary composite in which the bitumen matrix is embedded with filler particles. By taking interparticle interactions and the effects of bitumen origins and filler types into account, this research seeks to achieve a higher predictive precision of the modulus of asphalt mastics using micromechanical models. To achieve this goal, five classical micromechanical models and the Ju-Chen model (JCM) that effectively considers interparticle interactions were adopted to predict the moduli of 25 kinds of asphalt mastics composed of five bitumen origins and five filler types. The results show that the prediction accuracy of the general self-consistent model (GSCM) is the highest among the classical models, which is close to the accuracy of JCM (uniform) with the uniform distribution assumption for the radial distribution function. The JCM (P-Y) with the Percus-Yevick (P-Y) distribution assumption has the highest accuracy of all models, with average goodness of fit of 0.984 for all asphalt mastics. The dilute model (DM) exhibits higher accuracy in two bitumen with smaller low-temperature moduli, whereas the other five models in this study demonstrate higher accuracy in the other three bitumen with bigger low-temperature moduli. For the two models with the highest accuracy, the GSCM and JCM, the filler types have basically no effect on the prediction accuracy of these models. For other models except for GSCM and JCM, the smaller volume fractions of fillers lead to higher prediction accuracy, which can be attributed to the lower filler concentrations in asphalt mastics exhibiting weaker interparticle interactions. This study provides a certain reference for the performance prediction of asphalt mastic composites. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
08991561
Volume :
36
Issue :
1
Database :
Academic Search Index
Journal :
Journal of Materials in Civil Engineering
Publication Type :
Academic Journal
Accession number :
173622011
Full Text :
https://doi.org/10.1061/JMCEE7.MTENG-16609