Neural networks for lateral torsional buckling strength assessment of cellular steel I-beams. Issue 9 (July 2019)

• Record Type: Journal Article
• Authors: Sharifi, Yasser; Moghbeli, Adel; Hosseinpour, Mahmoud; Sharifi, Hojjat
• Is Part Of: Advances in structural engineering. Volume 22:Issue 9(2019)
• Page Start: 2192
• Page End: 2202
• Publication Date: 2019-07
• Subjects: artificial neural network -- cellular steel beams -- Garson's algorithm -- lateral torsional buckling; Structural engineering -- Periodicals; Construction, Technique de la; Structural engineering; Periodicals; 624.1
• DOI: 10.1177/1369433219836176
• Languages: English
• ISSNs: 1369-4332

Abstract

An artificial neural network model was developed as a reliable modeling method for simulating and predicting the ultimate force capacities of cellular steel beams. The required data in training, validating, and testing states were obtained from a reliable database. A new formula based on the artificial neural network was proposed to predict the failure loads of cellular steel beams subjected to lateral torsional buckling. The attempt was done to evaluate a practical formula considering all parameters which may affect the lateral torsional buckling strength. Then, a comparison was made between the proposed formula and the predictions obtained from Australian Standard (AS4100). The results provided some evidence that proposed formula obtained more accurate predictions than AS4100 design guides. Finally, a sensitivity analysis was developed using Garson's algorithm to determine the importance of each input parameters.