Boosting Hot Mix Asphalt Dynamic Modulus Prediction Using Statistical and Machine Learning Regression Modeling Techniques

Ahmed M. Awed, Ahmed N. Awaad, Mosbeh R. Kaloop, Jong Wan Hu (), Sherif M. El-Badawy and Ragaa T. Abd El-Hakim
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Ahmed M. Awed: Public Works Engineering Department, Faculty of Engineering, Mansoura University, Mansoura 35516, Egypt
Ahmed N. Awaad: Public Works Engineering Department, Faculty of Engineering, Mansoura University, Mansoura 35516, Egypt
Mosbeh R. Kaloop: Public Works Engineering Department, Faculty of Engineering, Mansoura University, Mansoura 35516, Egypt
Jong Wan Hu: Department of Civil and Environmental Engineering, Incheon National University, Incheon 22012, Republic of Korea
Sherif M. El-Badawy: Public Works Engineering Department, Faculty of Engineering, Mansoura University, Mansoura 35516, Egypt
Ragaa T. Abd El-Hakim: Public Works Engineering Department, Faculty of Engineering, Tanta University, Tanta 31527, Egypt

Sustainability, 2023, vol. 15, issue 19, 1-27

Abstract: The prediction of asphalt mixture dynamic modulus ( E* ) was investigated based on 1128 E* measurements, using three regression and thirteen machine learning models. Asphalt binder properties and mixture volumetrics were characterized using the same feeding features in the NCHRP 1-37A Witczak model. However, three aggregate gradation characterization approaches were involved in both modelling techniques: the NCHRP 1-37A gradation parameters, Weibull distribution factors, and Bailey method parameters. This study evaluated the performance of these models based on various performance indicators, using both statistical and machine learning regression modeling techniques. K-fold cross-validation and learning curve analysis were conducted to assess the models’ generalization capabilities. The conclusions of this study demonstrate the superiority of the ML models, particularly the Catboost ensemble learning regression (CbR). Hyperparameter optimization and residual analysis were performed to fine-tune and confirm the heteroscedasticity of the CbR model. The Bailey-based CbR model showed the highest coefficient of determination ( R 2 ) of 0.998 and the lowest root mean square error ( RMSE ) of 220 MPa. Moreover, SHAP values interpreted the CbR model and showed the relative importance of its feeding features. Based on the findings of this study, the CbR model is suggested to accurately predict E* for a variety of asphalt mixtures. This information can be used to improve pavement design and construction, leading to more durable and long-lasting pavements.

Keywords: dynamic modulus; Weibull distribution; Bailey method; statistical-based regression models; machine learning-based regression models; NCHRP 1-37 A Witczak model; Catboost regression algorithm (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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