Prediction and Interpretation of Shear Capacity of FRP-RC Beams Using Heterogeneous Weighted Ensemble Model and Shapley Additive Explanation Analysis

To accurately predict the shear capacity of fiber-reinforced polymer (FRP) bar-reinforced concrete (RC) beams and overcome the poor prediction stability of conventional machine learning models, this study develops and trains a heterogeneous weighted ensemble prediction model, namely the MLP-XGBoost model, by integrating the Multi-Layer Perceptron (MLP) and Extreme Gradient Boosting (XGBoost) algorithms. A comparative analysis was conducted among the MLP-XGBoost model, conventional baseline models, and traditional empirical formulas in this study. The results demonstrate that all machine learning models outperform traditional empirical formulas in predictive accuracy, while among these machine learning models, the MLP-XGBoost model proposed in this study exhibits the optimal performance in both accuracy and stability. Furthermore, to address the “black-box” nature of machine learning models, this study employs the Shapley Additive Explanations (SHAP) method to quantitatively assess the contribution of each input feature to the shear capacity of FRP-RC beams. A prediction formula for the shear capacity of FRP-reinforced concrete beams with high predictive accuracy, based on the interpretable results of SHAP analysis. This approach provides a more reliable tool for evaluating the shear performance of FRP-RC beams and offers valuable guidance for the optimal design of engineering structures.