Lateral–torsional buckling resistance prediction model for steel cellular beams generated by Artificial Neural Networks (ANN)

Journal article


Ferreira, F., Shamass, R., Limbachiya, V., Tsavdarisdis, K. and Martins, C. (2021). Lateral–torsional buckling resistance prediction model for steel cellular beams generated by Artificial Neural Networks (ANN). Thin-Walled Structures. 170, p. 108592. https://doi.org/10.1016/j.tws.2021.108592
AuthorsFerreira, F., Shamass, R., Limbachiya, V., Tsavdarisdis, K. and Martins, C.
Abstract

The present paper aims to develop an Artificial Neural Network (ANN) formula to predict the LTB resistance of steel cellular beams. A finite element model is developed and validated through experimental tests. A parametric study is then conducted. 768 models are employed to train the ANN. The results are compared with the analytical models, as well as the equation predicted by ANN. The ANN model with seven neurons can accurately predict the LTB resistance of cellular beams as well the LTB combined with web-post buckling or web distortional buckling modes. Hence, the ANN-based formula can be adopted as design tool.

KeywordsArtificial neural network; Machine learning; Steel cellular beams; Lateral-torsional buckling; Finite element method.
Year2021
JournalThin-Walled Structures
Journal citation170, p. 108592
PublisherElsevier
ISSN0263-8231
Digital Object Identifier (DOI)https://doi.org/10.1016/j.tws.2021.108592
Publication dates
Print15 Nov 2021
Publication process dates
Accepted25 Oct 2021
Deposited06 Dec 2021
Accepted author manuscript
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Open
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