Coupled extreme gradient boosting algorithm with artificial intelligence models for predicting compressive strength of fiber reinforced polymer- confined concrete

Accurately predicting and identifying appropriate parameters are necessary for producing a safe and reliable strength model of concrete elements confined with fiber-reinforced polymers (FRP). In this study, an extreme gradient boosting (XGBoost) algorithm was developed for the feature selection and prediction of the ultimate compressive strength of FRP-confined concrete. The modeling process was established using a dataset from open-source literature consisting of 490 circular columns. Three well-known artificial intelligence (AI) models, the multivariate adaptive regression spline (MARS), extreme learning machine (ELM), and RANdom Forest GEnRator (Ranger), were used to validate the proposed model. The results demonstrated the effectiveness of the XGBoost algorithm in the modeling process, selection of suitable parameters, and enhancement of the prediction accuracy. The algorithm achieved excellent prediction results for all input combinations with a coefficient of determination (R²) greater than 0.9, and the best performance is gained by using five input parameters with (R² = 0.955), mean absolute percentage error (MAPE = 0.130), and root mean square error (RMSE = 0.572). The study revealed the flexibility and efficiency of capturing the nonlinear behavior of complex FRP-confined concrete using the proposed model.