Computational analysis of axially loaded thin-walled rectangular concrete-filled stainless steel tubular short columns incorporating local buckling effects. (December 2021)

• Record Type: Journal Article
• Authors: Ahmed, Mizan; Tran, Viet-Linh; Ci, Junchang; Yan, Xi-Feng; Wang, Fangying
• Is Part Of: Structures. Volume 34(2021)
• Page Start: 4652
• Page End: 4668
• Publication Date: 2021-12
• Subjects: CFST columns -- Stainless steel -- Short columns -- Local buckling -- Artificial neural network; Structural engineering -- Periodicals; 624.1
• DOI: 10.1016/j.istruc.2021.10.068
• Languages: English
• ISSNs: 2352-0124

Abstract

Abstract: This paper investigates the structural performance of concrete-filled stainless steel tubular (CFSST) columns composed of rectangular and square sections. A fiber-based mathematical model is developed to simulate the nonlinear performance of such columns loaded concentrically accounting for the local buckling of steel tube. An accurate lateral pressure model, as well as a strength degradation parameter are proposed based on the existing test results and incorporated in the mathematical modeling developed. A large test dataset is used to validate the accuracy of the numerical prediction. The mathematical model is employed to study the sensitivities of important column parameters on their axial behavior. The accuracy of the existing confinement model of rectangular CFST columns is evaluated in predicting the performance of CFSST short columns. Furthermore, the accuracy of the existing codified design models as well as and the simplified design model proposed in this study to quantify the ultimate compressive strength of such columns is investigated. An accurate artificial neural network (ANN) model along with the GUI feature is developed for the design engineers as a tool to predict their ultimate axial strengths.