DYNAMIC MODULUS PREDICTIVE MODEL BASED ON ARTIFICIAL NEURAL NETWORK FOR THE SUPERPAVE ASPHALT MIXTURES OF NEW MEXICO

被引：0

作者：

Rahman, A. S. M. Asifur ^{[1
]}

Tarefder, Rafiqul A. ^{[1
]}

机构：

[1] Univ New Mexico, Dept Civil Engn, MSC01 1070,1 Univ New Mexico, Albuquerque, NM 87131 USA

来源：

PROCEEDINGS OF THE ASME INTERNATIONAL MECHANICAL ENGINEERING CONGRESS AND EXPOSITION, 2017 VOL 14 | 2018年

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D O I：

暂无

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

In this study, a new dynamic modulus predictive model for the Superpave asphalt-aggregate mixtures of New Mexico is developed based on the artificial neural network methodology. A total of 54 plant-produced asphalt-aggregate mixtures from all over the state were collected, compacted, cored, and sawed to cylindrical test specimens in the laboratory to conduct dynamic modulus testing at different temperatures and loading frequencies. A database containing 1,620 dynamic moduli with phase angles was then used to develop this artificial neural network based predictive model. A neural architecture with 2 twelve-node hidden layers was found to be remarkably suitable for predicting the dynamic modulus and phase angle of asphalt concrete. Statistical evaluation showed that a fairly accurate estimation of dynamic modulus can be attained by using this model.

引用

页数：10

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