ArticleScientific reports2026
Concrete strength prediction using domain-driven feature engineering and explainable LightGBM.
Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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4 authors.
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Abstract
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.
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