Ferritic stainless steel thin sheet bolted connections failing by bearing-curling interaction: Testing, modelling and design

This paper presents experimental and numerical studies of ferritic stainless steel thin sheet bolted connections failing by bearing-curling interaction. An experimental programme was firstly conducted on fifteen ferritic stainless steel thin sheet bolted connection specimens, including eleven specimens designed with curling and four specimens designed without curling. The test setup and procedures as well as the key test results, including failure loads, failure modes, load-in-plane elongation curves and load-out-of-plane deformation curves, were reported. Following the experimental programme, a numerical modelling programme was performed, where finite element models were developed and validated against the test results and then used to perform parametric studies to generate further numerical data. Based on the obtained numerical data, the effects of various geometric parameters, including end distances, edge distances, longitudinal spacings and sheet thicknesses, on failure loads of ferritic stainless steel thin sheet bolted connections were discussed. Both the test and numerical data were then used to assess the codified design rules for ferritic stainless steel thin sheet bolted connections susceptible to bearing-curling interaction, as set out in the European code and American specification. On the basis of the assessment results, the European code and American specification were found to respectively underestimate the actual load-carrying capacities by 57% and 18%, on average. Besides, a relevant design method proposed for carbon steel thin sheet bolted connections was assessed for its applicability to ferritic stainless steel thin sheet bolted connections, with the results showing that it provided more consistent failure load predictions than the design codes, but the predicted failure loads were still conservative. Therefore, a revised design formula was proposed and shown to provide improved failure load predictions.