Predicting the Web Crippling Capacity of Cold-Formed Steel Lipped Channels Using Hybrid Machine Learning Techniques

Cold-Formed Steel Lipped (CFSL) channels are susceptible to a localized failure mechanism known as web crippling, triggered by concentrated loads or reactions applied to the web of the section. These loads induce buckling and distortion in the web, ultimately leading to the member's collapse. It is a challenging task to accurately determine the web crippling capacity of a CFSL channel due to its complexity and various influencing factors. This paper presents hybrid soft computing techniques for accurately predicting the web crippling capacity of CFSL channels subjected to two flange load cases. The developed soft computing techniques combine Artificial Neural Networks (ANN) with either Genetic Algorithms (GA) or Particle Swarm Optimization (PSO) to improve computational efficiency and accuracy. The finite element models of CFSL channels are developed and validated by experimental results and then employed to generate a database, which is used to train machine learning models, including ANN, GA-ANN, and PSO-ANN. Analysis is undertaken on the reliability of existing design formulas for determining the web crippling capacity of CFSL channels. It is shown that the PSO-ANN model outperforms the other models in terms of prediction accuracy. The existing design codes and formulas are not reliable in estimating the web crippling capacity of CFSL channels. However, the proposed model yields good correlation with finite element analysis results. A user- friendly graphical interface tool is developed for the practical design of cold-formed steel lipped channels.