Optimized Gradient Boosting Framework for Data-Driven Prediction of Concrete Compressive Strength

Given the significant impact of concrete’s compressive strength on structural service life, the development of accurate and efficient prediction methods is critically important. A hybrid machine learning modeling method based on the Whale Optimization Algorithm (WOA)-optimized XGBoost algorithm is proposed. Using 1030 sets of concrete mix proportion data covering eight key parameters—cement, blast furnace slag, fly ash, water, superplasticizer, coarse aggregate, fine aggregate, and curing age—the predictive performance of four models (linear regression, random forest, XGBoost, and WOA-XGBoost) was systematically compared. The results demonstrate that the WOA-XGBoost model achieved the highest goodness of fit (R2 = 0.9208, MSE = 4.5546), significantly outperforming the other models, and exhibited excellent generalization capability and robustness. Feature importance and SHAP analysis further revealed that curing age, cement content, and water content are the key variables affecting compressive strength, with blast furnace slag showing a significant marginal diminishing effect. This study provides a high-precision data-driven tool for optimizing mix proportions and predicting the strength of complex-component concrete, offering significant application value in promoting the resource utilization of industrial waste and advancing the development of green concrete.