Buckling Resistance Prediction of High-Strength Steel Columns Using Metaheuristic-Trained Artificial Neural Networks

This chapter aims to develop practical metaheuristic-trained Artificial Neural Networks to predict the ultimate buckling load of High Strength Steel columns. The section height, flange width, web thickness, flange thickness, and steel yield strength are considered the input variables, while the ultimate buckling load was assumed to be the only output variable. ANN models are trained through algorithms such as LM, BR, and SCG; Four metaheuristic algorithms, namely Particle Swarm Optimization, Colliding Body Optimization, and their developed versions are used for efficient training of the ANNs.