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

This paper investigates the accuracy of 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 Regression (GBRT), Extreme (XGBoost) in predicting ultimate axial load circular concrete-filled stainless steel tubular (CFSST) columns under loading. A test database encompassing results 142 CFSST is used to validate ML models. It was demonstrated that all can provide a better estimation than do, which XGBoost best columns. Finally, simple equation proposed based on model for practical