Web crippling design of cold-formed stainless steel channels under interior-two-flange loading condition using deep belief network

Abstract

This research presents a deep-learning framework, namely a deep belief network (DBN), for analyzing the interior-two-flange web crippling performance of cold-formed stainless steel channels with centered and offset web holes. An elastoplastic finite element (FE) model, validated using 101 experimental results which were previously reported in the literature, generates a total of 43,200 data points for training the DBN. When compared to a total of 54 experimental results published in the literature, the DBN predictions were shown to be approximately 10% more conservative. Using the same large training data, the developed DBN model outperformed the Backpropagation Neural Network (a typical shallow artificial neural network) and the PaddlePaddle-based linear regression model. A parametric analysis was then performed using the DBN predictions to explore the effect of section size, web holes and bearing length. Design equations for (reduced) web crippling strength are proposed for the cold-formed stainless steel perforated channels, and the feasibility of the proposed equations was assessed by the conducted reliability analysis.