Behaviour and design of high-strength concrete-filled rectangular ferritic stainless steel tubular (CFFSST) short columns subjected to axial compression

Abstract

Concrete-filled stainless steel tubes (CFSSTs) are increasingly applied in current composite structures owing to their excellent performance characterized by aesthetic appearance, high corrosion resistance and durability, and ease of maintenance. Compared with austenitic and duplex stainless steels, ferritic stainless steels have not or contain very low nickel content, achieving lower and more stable material costs. This provides a more viable alternative for structural applications. Additionally, it has been used successfully in corrosive marine environment in a few countries, such as South Africa. However, structural applications of concrete-filled ferritic stainless steel tubular (CFFSST) columns are still limited owing to the dearth of performance data and design guidance. To understand their structural behaviour and to include relevant design recommendations in current design specifications, this paper aims at investigating the compressive behaviour of axially loaded rectangular CFFSST short columns using the nonlinear finite element (FE) modelling. The FE models are developed using ABAQUS software, where two different material constitutive models for the flat and corner portions of the rectangular ferritic stainless steel tubes are incorporated. Close agreement is found between the outputs of FE analysis and existing test results with respect to the failure modes, cross-sectional strengths and axial load-shortening curves. Parametric analysis is then conducted to ascertain the impacts of key variables on the compressive behaviour of the studied CFFSST columns. The obtained numerical results as well as available test results are employed to evaluate the feasibility of current design specifications to design the examined CFFSST columns. Finally, a novel strength design model is proposed and shown to achieve a higher degree of accuracy and consistency of the design predictions.