Artificial Neural network-based prediction of SCF at the Intersection of CFST Y-joints

Abstract

Stress concentration factors (SCFs) are used to quantify the hot-spots stress in tubular joints with circular hollow section for fatigue assessment, which are always obtained by finite element analysis or specimens testing. According to design specifications, complex formulas are recommended to calculate the SCFs at special locations of the intersection lines weld toe of tubular joints for individual load cases. To improve the fatigue performance of the joint, the concrete is filled in the chord to form a concrete-filled steel tube (CFST) joint. The capability of back-propagation neural network-based (BPNN) model in calculation of the SCFs in CFST Y-joints was investigated in this study. Three hundred FE numerical models were investigated to evaluate the effects of changes in different geometrical parameters on the SCFs of CFST Y-joint and the FEA results were used to train and test the neural networks. The nonlinear mapping relationships between the affecting variables and the SCFs distributions were established. Research results showed that SCFs prediction results of CFST Y-joints from BPNN models are close to the FE results, and properly trained and well calibrated BPNN can be reliable alternatives to complicated SCFs equations for predicting SCFs distribution at intersection line of CFST Y-joints.