Prediction of the ultimate axial load of circular concrete-filled stainless steel tubular columns using machine learning approaches

Abstract

This paper investigates the accuracy of the existing empirical design models and different machine learning (ML) models, known as Decision Tree (DT), Random Forest (RF), K-Nearest Neighbors (KNN), Adaptive Boosting (AdaBoost), Gradient Boosting Regression Tree (GBRT), and Extreme Gradient Boosting (XGBoost) in predicting the ultimate axial load of circular concrete-filled stainless steel tubular (CFSST) columns under axial loading. A test database encompassing the test results of 142 CFSST columns is used to validate the accuracy of the existing empirical design and different ML models. It was demonstrated that all the ML models can provide a better estimation of the ultimate axial load than the existing empirical design models do, in which XGBoost can provide the best estimation of the ultimate axial load of CFSST columns. Finally, a simple equation is proposed based on the XGBoost model for the practical design of CFSST columns.