沥青
人工神经网络
流变学
回归分析
回归
预测建模
沥青路面
动态模量
工程类
数学
统计
材料科学
计算机科学
机器学习
复合材料
动态力学分析
聚合物
作者
Barugahare Javilla,Armen Amirkhanian,Feipeng Xiao,Serji Amirkhanian
标识
DOI:10.1080/10298436.2020.1799209
摘要
Artificial neural networks (ANNs) and Gb*-based regression models were used for the prediction of the dynamic modulus (|E*|) of South Carolina’s hot mix asphalt mixtures (HMAs) the majority of which contained recycled asphalt pavement (RAP). Models’ training and testing were done using a database that contained 1656 |E*| values from 93 HMA mixtures. Gb*-based models included the Hirsch, revised Hirsch, Bari-Witczak, revised Bari-Witczak, Al-Khateeb 1, Al-Khateeb 2, NCHRP 1-40D, and the simplified global models. The results showed that Gb*-based regression models had a significant bias in prediction; Coupling VMA and Gb* had the most influence on |E*|; four-layer ANNs generally had a better performance than three-layer ANNs on using Hirsch model’s related inputs; ANN 3-15-15-1 and ANN 8-15-15-1 (developed with similar input variables as the Hirsch and Witczak regression models respectively) showed very high performance of R2 > 0.994 on testing. Therefore, ANNs could be considered to capture the influence of the binders’ rheological properties, mixture’s volumetric properties, and RAP on |E*| of HMA mixtures far better than regression-based models.
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