Interpretable Machine Learning Framework for Predicting Air Void Content in Sustainable Steel-Slag SMA Mixtures Using Metaheuristic-Optimized XGBoost

Abstract

Air void content (Va) is a key volumetric parameter governing the performance of Stone Mastic Asphalt (SMA), yet its prediction becomes challenging when steel slag, fibers, and additives are incorporated. This study develops an interpretable machine learning framework for predicting Va using 74 mixtures collected from 16 published studies and nine mixture-related variables. XGBoost was optimized using Particle Swarm Optimization and Grey Wolf Optimizer (GWO), with the best configuration obtained by GWO at a population size of 40 and a minimum development-stage 5-fold cross-validation (CV) RMSE of 0.371%. On the principal 70/30 evaluation partition, the optimized model achieved R2 = 0.944, RMSE = 0.397%, MAE = 0.262%, and MAPE = 0.054, outperforming the evaluated benchmark models in R2, RMSE, MAE, and MAPE. Robustness analyses showed that prediction accuracy was sensitive to data partitioning and literature-source composition, indicating that the reported performance should be interpreted within the represented data domain. SHAP, permutation importance, and feature-ablation analyses consistently identified binder penetration as the most influential predictor, followed mainly by asphalt content and softening point. Overall, the proposed framework provides an interpretable tool for preliminary Va estimation and mixture screening, while independent laboratory and field validation remains necessary before practical deployment.