Parametric study and neural network-based prediction for stress concentration factor of concrete-filled steel tubular T-joint

Abstract

In this study, an extensive parametric investigation for the stress concentration factor (SCF) of concrete-filled steel tubular (CFST) T-joints was conducted based on a validated finite element (FE) model in which the weld profile, mesh, and contact simulation were specifically and precisely dealt with. Distribution of the SCF on the intersecting line was depicted. Effect of non-dimensional geometric parameters and the elastic modulus of concrete was discussed in detail. Results show that the SCF is monotonically distributed on the intersecting line in the chord side, and the maximum SCF probably located at the crown position. There is the possibility that the maximum SCF of the brace occurs at a position neither the crown nor the saddle. Reducing the length of chord or promoting the elastic modulus of concrete lowers the SCF at most positions, but has no effect on the SCF at chord saddle. A back-propagation neural network (BPNN) model was established, and was trained based on 724 FE results of the SCF from the parametric study. The trained BPNN model predicts the SCF of CFST T-joints with a low error of 14.5%, and it improves the accuracy by 62.0% compared to the current formulae.