Evaluating the predictive accuracy of supervised machine learning models to explore the mechanical strength of blast furnace slag incorporated concrete

Blast furnace slag (BFS) concrete offers significant environmental and durability advantages over ordinary portland cement (OPC) concrete, including reduced CO₂ emissions, enhanced long-term strength, and stronger resistance to chemical attacks. However, refining its mix design using conventional experimental methods is time-consuming and costly. This study addresses this challenge by developing advanced machine learning (ML) models to predict the compressive strength of BFS-incorporated concrete. A large dataset of 675 samples featuring cement, BFS, fly ash, aggregates, water, superplasticizer (SP), and curing age was assembled. Six ML models—AdaBoost, Decision Tree, Gradient Boosting Regressor, K-Nearest Neighbors, LightGBM, and XGBoost were evaluated. Comprehensive hyperparameter tuning via grid search and cross-validation optimized model performance and mitigated overfitting. Predictive accuracy was assessed using R 2 , RMSE, MAE, and MAPE metrics. Model interpretability was enhanced through SHAP analysis and partial dependence plots (PDP), revealing curing age, SP, and cement as dominant features influencing compressive strength. Results demonstrated that LightGBM (test R 2 = 0.946, RMSE = 4.41 MPa) and XGBoost (test R 2 = 0.943, RMSE = 4.52 MPa) exhibited almost comparable predictive performance; however, LightGBM achieved the highest overall accuracy, reflected in its slightly higher test R 2 and lower RMSE, which declares LightGBM the best model for predicting CS of BFS-concrete. PDP analysis revealed that the optimal BFS replacement was observed between 30 and 40% range. This ML framework eliminates resource-intensive experimentation, accelerating sustainable concrete design with industrial byproducts.