Predictive Models to Estimate Phase Angle of Asphalt Mixtures
Publication: Journal of Materials in Civil Engineering
Volume 27, Issue 8
Abstract
The main objective of this study was to develop predictive models to estimate the viscoelastic phase angle () of different asphalt mixtures using robust mathematical functions and operators in the form of a powerful beta distribution format. A total of 163 conventional dense-graded asphalt concrete (DGAC), 13 asphalt-rubber asphalt concrete (ARAC) gap graded, and 7 asphalt-rubber friction course (ARFC) open-graded mixes were available, amounting to a total of 5,490 data points. The asphalt materials parameters were used to develop predictive equations for DGAC, ARAC, and ARFC mixes separately using principal component analyses techniques and mathematical regression. In addition, master curves were developed for each mix type using the predicted . The statistical goodness of fit measured for all of the predictive models, DGAC, ARAC, and ARFC, were very good to excellent with between 0.80 and 0.85, and less than 0.35. The performance and accuracy of the predictive models was assessed by comparing the predicted and the actual measured on similar mixtures obtained from past studies. The model predictions had very good to excellent correlations with the measured values indicated by very high . Overall, the models and the relationships showed low bias and high precision indicating that the newly developed models are an excellent means of acquiring the for the three different asphalt mixes in the absence of testing capabilities. Furthermore, it is envisioned that can be incorporated in futuristic sustainable flexible pavement designs and practices, complementing stiffness parameters such as moduli and contributing to a comprehensive understanding of the intricate viscoelastic characteristics of the asphalt mixes.
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Acknowledgments
The authors would like to thank Prof. Kamil Kaloush of Arizona State University (ASU) who authorized the use of ASU’s advanced pavement material characterization database to develop the various models in this study.
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Information & Authors
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Published In
Journal of Materials in Civil Engineering
Volume 27 • Issue 8 • August 2015
Copyright
© 2014 American Society of Civil Engineers.
History
Received: May 25, 2014
Accepted: Sep 12, 2014
Published online: Oct 24, 2014
Discussion open until: Mar 24, 2015
Published in print: Aug 1, 2015
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