Lateral Torsional Buckling Capacity Assessment of Castellated Steel Beams Using Artificial Neural Networks

Abstract

A new model based on Artificial Neural Network (ANN) was established as a trustworthy technique for predicting ultimate lateral torsional buckling (LTB) capacity of castellated steel beams. The required information for training, validating and testing of the developed model obtained from a reliable database. Consequently, a new formulation based on the ANN has been offered for predicting the failure load of castellated steel beams exposed to LTB. All parameters which may affect the LTB capacity of castellated beams were considered for presentation of this formula. Then, outcomes of the proposed formula were compared with predictions of Australian Standard (AS4100) for LTB capacity of castellated beams. This comparison indicated that proposed formula has a good performance for prediction of ultimate strength in castellated beams subjected to LTB. At the end, Garson’s algorithm has been established as a sensitivity analysis to determinate importance of each input in the proposed formula.