Concrete strength prediction using domain-driven feature engineering and explainable LightGBM

Accurately predicting the compressive strength of concrete is essential for optimizing mix designs, ensuring structural integrity, and reducing construction costs. This study integrates domain-informed feature engineering with optimized tree-based ensemble models to benchmark concrete strength prediction on a widely recognized Yeh dataset. We expanded the baseline feature set with 11 engineered variables, evaluating five architectures: Random Forest, XGBoost, LightGBM, Extra Trees, and a Voting Regressor. Among these, LightGBM emerged as the top-performing architecture with [Formula: see text] of 0.959 and [Formula: see text] of 3.47 MPa. SHAP analysis revealed that the newly engineered features were the most influential predictors, with Age-Cement Interaction and Cementitious-to-Water Ratio identified as the primary drivers of model accuracy. To facilitate practical adoption, the optimized framework was deployed via a PyQt5-based desktop interface and a FastAPI web service, enabling mix design optimization across different user environments.