An experimental and computational intelligence simulation for predicting the strength accuracy of self-compacting concrete using Linz–Donawitz Slag as industrial waste derivatives

) of approximately 0.931 and lower error metrics (RMSE = 0.059, MAE = 0.045) as compared to other models. To improve interpretability, Shapley Additive exPlanations (SHAP) and Partial Dependence Plot (PDP) analyses were performed to identify the governing parameters and their interaction-driven effects on CS development. SHAP analysis revealed that cement content and LDS were the most influential variable for predicting the CS of SCC incorporating LDS, while water content was identified as the least significant factor. Partial Dependence Plot (PDP) analysis was employed to assess the impact of each input variable on CS. Consequently, the required amount of each input variable can be determined to meet a specific design strength. Therefore, the CNN-LSTM approach proved to be a reliable and efficient ML model, offering quick and accurate predictions for sustainable SCC performance. However, the present research is limited by the relatively small experimental dataset and the absence of external validation under field conditions. Future research should focus on incorporating larger and more diverse datasets, real-world validation, and multi-objective assessment including durability and long-term performance of LDS-incorporated SCC systems.