Predicting Marshall Stability of Carbon Fiber-Reinforced Asphalt Concrete Using Machine Learning Techniques

Pavement engineering has always relied on asphalt concrete as the primary material. As a result, increasing the quality of asphalt concrete for greater performance and longer durability has remained a focus of research. The present paper aims to assess the potential of soft computing-based models, such as Artificial neural networks, Support vector machines, Gaussian process, M5P tree, Random forest, and Random tree-based models, used for the prediction of Marshall Stability of carbon–fiber asphalt mix. Five different statistical indices are used to evaluate the performance of each model with different input variables, such as Bitumen content (BC), Carbon fiber (CF), Bitumen grade (VG), Fiber length (FL), and Fiber diameter (FD), Specific gravity coarse aggregate (SG(CA)), Water absorption coarse aggregate (WA(CA)) and Specific Gravity of bitumen (SG(B)), to get the Marshall Stability (MS) as an output. Five statistical indices were used to assess the effectiveness of the applied models i.e., Coefficient of correlation (CC), Mean absolute error (MAE), Root mean square error (RMSE), Relative absolute error (RAE) and Root relative squared error (RRSE). According to performance evaluation results, it has been found that the ANN-based model has outperformed all the applied models for predicting the Marshall Stability of asphalt concrete using carbon fiber with CC value as 0.9287 and 0.9126, R 2 value as 0.8624 and 0.8328, MAE value as 1.7527 and 1.8702, RMSE value as 2.3305 and 2.4438, RAE value as 32.51 and 39.64% and RRSE values as 37.30 and 43.59% for both the training and testing stages respectively. Taylor's diagram suggests that the ANN model outperforms the other applied models. Sensitivity analysis shows that the bitumen content (BC) is the more sensitive parameter in the carbon–fiber asphalt mix. Furthermore, carbon fiber is comparable to the sensitivity of the bitumen content which shows the significance of the carbon fiber in the asphalt mix in predicting the Marshall Stability.