Experimental Investigation and Machine Learning-Based Prediction of Marshall Stability of Stone Mastic Asphalt Using Steel Slag

Abstract

This study applies several machine learning models, including Gradient Boosting Regressor (GBR), CatBoost (CB), Support Vector Machine (SVM), Random Forest (RF), and AdaBoost (AB), to predict the Marshall stability of Stone Mastic Asphalt (SMA) mixtures incorporating steel slag as a partial replacement for conventional coarse aggregates. The dataset consists of 144 Marshall test samples, with input variables including specific gravity, penetration, flash point temperature, softening point temperature, bitumen content, cellulose fiber content, and steel slag content, while Marshall stability is considered as the output variable. The results indicate that all models achieved high predictive accuracy, with CatBoost providing the best performance, achieving R² = 0.936, RMSE = 0.227, and MAE = 0.323 on the validation dataset. Additional analyses, including residual analysis and SHAP-based interpretation, suggest the robustness and interpretability of the model within the investigated dataset of the model. These findings highlight the strong potential of CatBoost for accurately predicting the mechanical performance of SMA mixtures and support decision-making in mixture design in pavement engineering.