Prediction of Compressive Strength of Concrete Using Explainable Machine Learning Models

Predicting the compressive strength of concrete is essential for engineering design and quality assurance. Traditional empirical formulas often fall short in capturing complex multi-factor interactions and nonlinear relationships. This study employs an interpretable machine learning framework using Gradient Boosting Trees, Random Forest, and Backpropagation Neural Networks to predict concrete compressive strength. Bayesian optimization was employed for hyperparameter tuning, and SHAP analysis was used to quantify feature contributions. Based on 223 sets of compression test data, this study systematically compared the predictive performance of the five models. Results demonstrate that the CatBoost model achieved the best results, R2 of 0.9388, RMSE of 2.7131 MPa, and MAPE of 5.45%, outperforming other models. SHAP analysis indicated that cement content had the greatest impact on strength, followed by water content, water reducer, fly ash, and aggregates, with notable interactive effects between factors. Compared to the empirical formula in the current industry standard Specification for Mix Proportion Design of Ordinary Concrete, the CatBoost model showed higher accuracy under specific raw material and curing conditions, with MAPE values of 2.94% and 5.96%, respectively. The optimized CatBoost model, combined with interpretability analysis, offers a data-driven tool for concrete mix optimization, balancing high precision with practical engineering applicability.