Neural network application for distortional buckling capacity assessment of castellated steel beams. (October 2020)

• Record Type: Journal Article
• Authors: Hosseinpour, Mahmoud; Sharifi, Yasser; Sharifi, Hojjat
• Is Part Of: Structures. Volume 27(2020)
• Page Start: 1174
• Page End: 1183
• Publication Date: 2020-10
• Subjects: Artificial Neural Network (ANN) -- Castellated steel beam -- Lateral distortional buckling -- Finite element modeling -- Structural design code; Structural engineering -- Periodicals; 624.1
• DOI: 10.1016/j.istruc.2020.07.027
• Languages: English
• ISSNs: 2352-0124

Abstract

Abstract: Artificial Neural Network (ANN) model was developed as a reliable modeling method for simulating and predicting the ultimate moment capacities of castellated steel beams. The training and testing data for neural networks are obtained using Finite Element Analysis (FEA). For this purpose, a series of nonlinear finite element analyses have been carried out to simulate the distortional buckling behavior of castellated steel beams, and the effects of nine independent parameters on the lateral-distortional buckling mode, have been investigated. Moreover, unlike the existing design codes, the ANN model considers the effects of web distortion on the ultimate buckling strength of beams. Then, a new formula based on ANNs has been proposed to predict the ultimate moment capacities of castellated steel beams subjected to lateral-distortional buckling. The attempt was done to evaluate a practical formula considering all parameters which may affect the distortional capacity of castellated steel beams. Then, a sensitivity analysis using Garson's algorithm has been developed to determine the importance of each input parameter. Finally, a comparison has been made between the proposed formula and the predictions obtained from AS4100, EC3, and AISC codes. It is shown that the proposed formula is more accurate than these design codes.