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Cost-Effective Approaches Based on Machine Learning to Predict Dynamic Modulus of Warm Mix Asphalt with High Reclaimed Asphalt Pavement

机译：基于机器学习的经济有效的方法预测高回收沥青路面温热混合沥青的动态模量

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摘要

Warm mix asphalt (WMA) technology, taking advantage of reclaimed asphalt pavements, has gained increasing attention from the scientific community. The determination of technical specifications of such a type of asphalt concrete is crucial for pavement design, in which the asphalt concrete dynamic modulus (E*) of elasticity is amongst the most critical parameters. However, the latter could only be determined by complicated, costly, and time-consuming experiments. This paper presents an alternative cost-effective approach to determine the dynamic elastic modulus (E*) of WMA based on various machine learning-based algorithms, namely the artificial neural network (ANN), support vector machine (SVM), Gaussian process regression (GPR), and ensemble boosted trees (Boosted). For this, a total of 300 samples were fabricated by warm mix asphalt technology. The mixtures were prepared with 0%, 20%, 30%, 40%, and 50% content of reclaimed asphalt pavement (RAP) and modified bitumen binder using Sasobit and Zycotherm additives. The dynamic elastic modulus tests were conducted by varying the temperature from 10 °C to 50 °C at different frequencies from 0.1 Hz to 25 Hz. Various common quantitative indications, such as root mean square error (RMSE), mean absolute error (MAE), and correlation coefficient (R) were used to validate and compare the prediction capability of different models. The results showed that machine learning models could accurately predict the dynamic elastic modulus of WMA using up to 50% RAP and fabricated by warm mix asphalt technology. Out of these models, the Boosted algorithm (R = 0.9956) was found as the best predictor compared with those obtained by ANN-LMN (R = 0.9954), SVM (R = 0.9654), and GPR (R= 0.9865). Thus, it could be concluded that Boosted is a promising cost-effective tool for the prediction of the dynamic elastic modulus (E*) of WMA. This study might help in reducing the cost of laboratory experiments for the determination of the dynamic modulus (E*).
机译：热混合沥青（WMA）技术，利用再生沥青路面，从科学界获得了越来越多的关注。这种类型沥青混凝土技术规格的测定对于路面设计至关重要，其中弹性的沥青混凝土动态模量（E *）是最关键的参数。然而，后者只能通过复杂，昂贵和耗时的实验来确定。本文介绍了基于各种机器学习的算法确定WMA的动态弹性模量（E *），即人工神经网络（ANN），支持向量机（SVM），高斯过程回归（ GPR）和合奏增强树（提升）。为此，通过温热的沥青技术制造了总共300个样品。使用Sasobit和Zycotherm添加剂，用0％，20％，30％，40％和50％的再生沥青路面（RAP）和改性沥青粘合剂制备。通过在0.1Hz至25Hz的不同频率下改变10℃至50℃的温度来进行动态弹性模量试验。使用各种常见的定量指示，例如根均方误差（RMSE），平均绝对误差（MAE）和相关系数（R）来验证和比较不同模型的预测能力。结果表明，使用高达50％的RAP和通过温热的沥青技术进行准确地预测WMA的动态弹性模量。除了这些模型中，与通过Ann-LMN（R = 0.9954），SVM（R = 0.9654）和GPR（R = 0.9865）获得的升级算法（R = 0.9956）作为最佳预测因素。因此，可以得出结论，提升是一种有希望的成本效益的工具，用于预测WMA的动态弹性模量（E *）。本研究可能有助于降低实验室实验的成本，以确定动态模量（E *）。

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