Computational modeling study demonstrates accurate inverse mix design for steel slag asphalt mixtures using machine learning and grey wolf optimization, highlighting sustainable pavement design.
Pavement mix design for steel slag relies largely on empirical Marshall tests requiring numerous specimens and lengthy cycles. To address this, an inverse mix design (IMD) framework combining machine-learning forward prediction with grey wolf optimization (GWO) was developed. A dataset of 300 samples with 13 input features and 2 output indicators was compiled. Three algorithms—XGBoost, CatBoost, and random forest (RF)—were compared, and model interpretability was analyzed using SHAP and ALE. CatBoost achieved the best overall performance. SHAP identified steel slag f-CaO content and replacement ratio as the dominant factors governing moisture susceptibility. GWO search errors for all three design scenarios were below 0.24%. Laboratory validation showed a mean deviation of 1.02% between target and measured values, confirming the method’s feasibility. The method also supports sustainable pavement engineering by facilitating higher steel slag utilization, contributing to CO2 reduction and natural aggregate conservation.
No takes yet. Share an insight, caveat, or question.
Song et al. (2026) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: