Prediction of Shear Capacity of Fiber-Reinforced Polymer-Reinforced Concrete Beams Based on Machine Learning

To address the existing challenges of lacking a unified and reliable shear capacity prediction model for fiber-reinforced polymer (FRP)-strengthened reinforced concrete beams (FRP-SRCB) and the excessive experimental workload, this study establishes a shear capacity prediction model for FRP-SRCB based on machine learning (ML). First, the correlation between input and output parameters was analyzed by the Pearson correlation coefficient method. Then, representative single model (ANN) and integrated model (XGBoost) algorithms were selected to predict the dataset, and their performance was evaluated based on three commonly used regression evaluation metrics. Finally, the prediction accuracy of the ML model was further verified by comparing it with the domestic and foreign design codes. The results manifest that the shear capacity exhibits a strong positive correlation with the beam width and effective height. Compared to the ANN model, the XGBoost-based prediction model achieves determination coefficients (R2) of 0.999 and 0.879 for the training and test sets, respectively, indicating superior predictive accuracy. Furthermore, the shear capacity calculations from design codes show significant variability, demonstrating the superior predictive capability of ML algorithms. These findings offer a guideline for the design and implementation of FRP reinforcement in actual bridge engineering.