Characterization of Triaxial Stress State Linear Viscoelastic Behavior of Asphalt Concrete
Publication: Journal of Materials in Civil Engineering
Volume 29, Issue 4
Abstract
The present study proposed a new method for characterizing linear viscoelastic (LVE) properties of asphalt concrete (AC) under triaxial loading conditions. The presented method adopted the relaxation spectrum model derived from the Havriliak-Negami (HN) complex modulus model and an existing confining pressure–dependent equilibrium modulus model to simulate the triaxial complex modulus and relaxation modulus master curves. The confinement dependency of the triaxial master curves was considered in the relaxation spectrum and equilibrium modulus instead of the time-temperature shift factor. A confinement shift factor model was developed to incorporate the confinement dependency into the HN relaxation spectrum. The feasibility and effectiveness of the method were demonstrated by using the triaxial complex modulus test data sets of three different asphalt mixtures, and its advantages over existing methods were also discussed. The results showed that the method was able to accurately and completely characterize the triaxial LVE behavior of the mixtures in both frequency and time domains. Both the storage and loss moduli test data were simultaneously used during the determination of the model parameters. Thus, the LVE information in the experimental results was fully extracted and all the predicted triaxial master curves achieved relatively smaller deviations from the test data.
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Acknowledgments
The study was sponsored by Tennessee DOT and China Scholarship Council (No. 201406060090); the authors would like to gratefully acknowledge their financial support.
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Information
Published In
Journal of Materials in Civil Engineering
Volume 29 • Issue 4 • April 2017
Copyright
©2016 American Society of Civil Engineers.
History
Received: Sep 25, 2015
Accepted: Aug 9, 2016
Published online: Nov 7, 2016
Published in print: Apr 1, 2017
Discussion open until: Apr 7, 2017
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