Application of Artificial Neural Networks for web-post shear resistance of cellular steel beams

Abstract

The aim of this paper is to predict web-post buckling shear strength of cellular beams made from normal strength steel using the Artificial Neural Networks (ANN). 304 developed finite-element numerical models were used to train, validate and test 16 different ANN models. To verify the accuracy of the ANN model, the ANN predictions were compared with experimental and analytical results. Results show that ANN models that used geometric parameters as an ANN input were able to predict web-post buckling strength to a higher level of accuracy in comparison to models using only geometric ratios as an ANN input. An ANN-based formula with 4 neurons was proposed in this study. In comparison to existing design guidance, it is shown that an ANN model trained with the Levenberg-Marquardt backpropagation algorithm is capable of predicting the web-post shear resistance to a higher level of accuracy. The formula developed can be easily implemented in Excel or in user graphical interface. It can be a potential tool for structural engineers who aim to accurately estimate the web-post buckling of cellular steel beams without the use of costly resources associated with FE analysis.