Marshall Stability Prediction with Glass and Carbon Fiber Modified Asphalt Mix Using Machine Learning Techniques

Ankita Upadhya; Mohindra Singh Thakur; Mohammed Saleh Al Ansari; Mohammad Abdul Malik; Ahmad Aziz Alahmadi; Mamdooh Alwetaishi; Ali Nasser Alzaed

doi:10.3390/ma15248944

Marshall Stability Prediction with Glass and Carbon Fiber Modified Asphalt Mix Using Machine Learning Techniques

Materials (Basel). 2022 Dec 14;15(24):8944. doi: 10.3390/ma15248944.

Authors

Ankita Upadhya¹, Mohindra Singh Thakur¹, Mohammed Saleh Al Ansari², Mohammad Abdul Malik³, Ahmad Aziz Alahmadi⁴, Mamdooh Alwetaishi⁵, Ali Nasser Alzaed⁶

Affiliations

¹ Department of Civil Engineering, Shoolini University, Solan 173229, Himachal Pradesh, India.
² Department of Chemical Engineering, College of Engineering, University of Bahrain, Zallaq P.O. Box 32038, Bahrain.
³ Engineering Management Department, College of Engineering, Prince Sultan University, P.O. Box 66833, Riyadh 11586, Saudi Arabia.
⁴ Department of Electrical Engineering, College of Engineering, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia.
⁵ Department of Civil Engineering, College of Engineering, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia.
⁶ Department of Architecture Engineering, College of Engineering, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia.

Abstract

Pavement design is a long-term structural analysis that is required to distribute traffic loads throughout all road levels. To construct roads for rising traffic volumes while preserving natural resources and materials, a better knowledge of road paving materials is required. The current study focused on the prediction of Marshall stability of asphalt mixes constituted of glass, carbon, and glass-carbon combination fibers to exploit the best potential of the hybrid asphalt mix by applying five machine learning models, i.e., artificial neural networks, Gaussian processes, M5P, random tree, and multiple linear regression model and further determined the optimum model suitable for prediction of the Marshall stability in hybrid asphalt mixes. It was equally important to determine the suitability of each mix for flexible pavements. Five types of asphalt mixes, i.e., glass fiber asphalt mix, carbon fiber asphalt mix, and three modified asphalt mixes of glass-carbon fiber combination in the proportions of 75:25, 50:50, and 25:75 were utilized in the investigation. To measure the efficiency of the applied models, five statistical indices, i.e., coefficient of correlation, mean absolute error, root mean square error, relative absolute error, and root relative squared error were used in machine learning models. The results indicated that the artificial neural network outperformed other models in predicting the Marshall stability of modified asphalt mix with a higher value of the coefficient of correlation (0.8392), R² (0.7042), a lower mean absolute error value (1.4996), and root mean square error value (1.8315) in the testing stage with small error band and provided the best optimal fit. Results of the feature importance analysis showed that the first five input variables, i.e., carbon fiber diameter, bitumen content, hybrid asphalt mix of glass-carbon fiber at 75:25 percent, carbon fiber content, and hybrid asphalt mix of glass-carbon fiber at 50:50 percent, are highly sensitive parameters which influence the Marshall strength of the modified asphalt mixes to a greater extent.

Keywords: M5P tree; Marshall stability; artificial neural network; asphalt modified mix; carbon fiber; gaussian processes; glass fiber; hybrid mix; multiple linear regression; support vector machines.

Grants and funding

The authors acknowledge the support of Prince Sultan University for paying the article processing charges (APC) of this publication.