Abstract

Concrete-filled steel tubular (CFST) columns are currently used in offshore structures and oil and gas drilling platforms, and, as such, the external steel tubes of the columns are at risk as they are exposed to the aggressive oceanic climate and/or sea water. This has recently led to the introduction of concrete-filled stainless steel tubular (CFSST) columns, which benefit from using stainless steel that is a superior, metallic, corrosion-resistant material. Accordingly, CFSST short columns have recently attracted the attention of the scientific community. Additionally, through the use of concrete-filled double skin steel tubular (CFDST) columns, lighter cross sections can be designed and the amount of steel material can be reduced so that the cost of the column is also reduced. More recently, composite columns consisting of dual steel tubes (external and internal stainless steel and carbon steel tubes, respectively) with concrete filled in the entire tubular section have been introduced. These columns, which are known as concrete-filled double-tube (CFDT) columns, have proved that both a lower cost and a lighter weight can be achieved compared to concrete-filled stainless steel tubular (CFSST) columns. This is mainly attributed to the increased strength of the concrete fill inside the internal tube, which is surrounded by both the external and internal steel tubes. However, circular CFSST, CFDST, and CFDT columns have received scant attention, despite the fact that they are generally slender in practice. This chapter presents the fundamental behavior of circular concrete-filled lean duplex stainless steel tubular (CFSST) short columns under axial compression. Three-dimensional finite element (FE) models for CFSST short columns subjected to axial compression are developed using the FE package ABAQUS. Parametric studies are undertaken, the ultimate axial strengths of CFSST columns predicted by the FE models are compared with those calculated using Eurocode 4, the ACI code, the continuous strength method (CSM) by Lam and Gardner, and Liang and Fragomeni’s design formulas, and the best predictor is recommended.

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