Abstract

To address the limitations of traditional BP neural networks in predicting manufactured sand concrete strength, specifically their susceptibility to local optima and “black-box” opacity, this study developed an integrated framework combining improved optimization algorithms with the Shapley Additive Explanations (SHAP) method. Using a dataset of 375 data points, genetic algorithm-back propagation (GA-BP) and GOOSE-BP prediction models were developed, with AutoFeat employed for explicit model construction based on a SHAP feature analysis. The results demonstrate that the GOOSE-BP model significantly outperformed traditional methods, achieving an R2 of 0.916 and reducing prediction errors by 47.5%. The SHAP analysis identified paste thickness and stone powder content as the primary determinants of strength. Key thresholds were established, including a water-to-binder ratio sensitivity range of 0.35–0.50, an optimal stone powder content of 80–110 kg/m3, and a recommended sand ratio of 0.38–0.45. By converting complex nonlinear mappings into interpretable explicit expressions, this study provides a robust scientific basis and a practical computational tool for predicting concrete strength, facilitating the deep integration of machine learning with civil engineering practice.

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