Abstract
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*).
Highlights
Hot Mix Asphalt (HMA) is the most widely used pavement material [1,2,3]
artificial neural network (ANN) is better than multiple linear regression (MLR) at predicting the air void content, whereas MLR is better than ANN in predicting soluble binder content
The performance of four algorithms, namely ANN-LMN, Support Vector Machine (SVM), Gaussian process regression (GPR), and boosted trees (Boosted), followed by the application of machine learning methods to predict the values of dynamic modulus are evaluated and compared in function of different statistical criteria
Summary
Hot Mix Asphalt (HMA) is the most widely used pavement material [1,2,3]. First introduced in the early 1900s, it could be stated that HMA technology has been fully understood to date [1,2,3]. More advanced and objective machine learning approaches have been developed and applied to predict the mechanical properties of the materials accurately, including asphalt mixtures. ANN is better than MLR at predicting the air void content, whereas MLR is better than ANN in predicting soluble binder content These machine learning models are useful for the prediction of the properties of the asphalt materials. In the WMA mixtures, reclaimed asphalt pavement (RAP) was used as it is considered an excellent solution to reduce the need for virgin materials It could reduce the aging of asphalt binders and reduce construction temperatures due to the reduction of energy consumption as well as greenhouse gas emissions. Matlab codes and packages were used for the development and validation of the models
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