Numerical and machine learning models for concentrically and eccentrically loaded <scp>CFST</scp> columns confined with <scp>FRP</scp> wraps
Chi Xu, Ying Zhang, Haytham F. Isleem, Dianle Qiu, Yun Zhang, Mostafa M. Alsaadawi, Rupesh Kumar Tipu, Waleed E. El‐Demerdash, Asmaa Hamed
Abstract
Abstract Previous research largely concentrated on predicting load‐carrying capacities of concrete‐filled steel tubes (CFST) confined with fiber‐reinforced polymer (FRP) wraps under pure concentric loads, neglecting the more complex failure mechanisms that occur under real‐life eccentric loading conditions. This study, therefore, employs both finite element modeling (FEM) and machine learning methods to accurately predict the load‐bearing capacities under both concentric and eccentric loading conditions. This research analyzed a comprehensive dataset comprising 128 experimental tests and an equivalent number of FEM simulations designed to evaluate their eccentric performance. These models have been thoroughly generated and validated against existing literature. Additionally, the developed ML models, particularly a hybrid deep learning model, demonstrated significant predictive accuracy, with an average R 2 value of 0.969 across all model folds. Partial dependence analysis further highlighted the significant influence of concrete strength and the interactive effects of steel tube area and FRP wrap thickness on the load‐carrying capacity of the columns. Furthermore, to enhance cost‐efficiency and resource management compared with traditional laboratory testing, a user‐friendly graphical user interface (GUI) has been developed and hosted on an open‐source platform such as GitHub. This interface supports real‐time, precise predictive capabilities and promotes a collaborative environment for ongoing model refinement and improvement.