Prediction Of Axial Capacity Of Corrosion-affected Rc Columns Strengthened With Inclusive Frp

This article discusses the use of artificial intelligence (AI) techniques to predict the axial strength of fiber-reinforced polymer (FRP) strengthened and unstrengthened reinforced concrete (RC) columns affected by corrosion. The study found that the extreme gradient boosting (XGBoost) model had the highest accuracy in predicting the axial load carrying capacity of these columns, and could be a valuable tool for the scientific community and FRP applicators. Feature importance analysis was also conducted to determine the most influential factors in predicting the axial strength of these columns.