Evaluation of soft computing techniques for predicting Marshall Stability of waste plastic-reinforced asphalt concrete

Pavement engineering has long prioritized enhancing the quality and durability of asphalt concrete, a foundational material in road construction. This study employs advanced soft computing techniques to predict the Marshall Stability (MS) of asphalt concrete reinforced with waste plastic. Techniques such as Artificial Neural Network (ANN), Random Forest (RF), Random Tree (RT), Support Vector Machine (SVM), and Bagging RT are utilized. Evaluation of model effectiveness is conducted using seven statistical metrics: coefficient of correlation (CC), root mean square error (RMSE), mean absolute error (MAE), relative absolute error (RAE), root relative squared error (RRSE), mean absolute percentage error (MAPE), scatter index (SI), and comprehensive measure (COM). Among the applied models, the Bagging RT model emerges as the top performer, exhibiting superior performance across multiple metrics. Specifically, the Bagging RT model achieves impressive CC values of 0.921 and 0.834, indicating strong correlations between predicted and actual MS values. Additionally, it demonstrates low error metrics, with RMSE values of 1.632 and 2.869, and MAE values of 1.207 and 2.081, respectively. A sensitivity analysis conducted for the Bagging RT-based model underscores the significant influence of aggregate size on MS prediction, highlighting the model’s capability to elucidate critical factors shaping material stability.