Data-driven prediction of failure loads in low-cost FRP-confined reinforced concrete beams

Abstract

This study investigates the application of machine learning (ML) models to predict the ultimate failure load of reinforced concrete (RC) beams confined with low-cost fiber-reinforced polymers (FRP), relatively underexplored area. A dataset of 100 samples, including beams designed to fail in flexure and shear, was compiled from literature and experimental testing. Four ML models—XGBoost, Random Forest (RF), Neural Network (NN), and Decision Tree (DT)—were evaluated using k-fold cross-validation with performance metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and R². XGBoost outperformed the other models, achieving the highest R² of 0.96 and the lowest RMSE of 12.81, while SHAP analysis identified beam height, bottom rebar strength, and beam width as key predictors. These results highlight the effectiveness of ensemble methods for predicting failure loads in RC beams and provide insights into the most influential features affecting structural performance.