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

Vendramell Ferreira, F. P., Shamass, R., Limbachiya, V. , Tsavdaridis, K. D. ORCID: 0000-0001-8349-3979 & Martins, C. H. (2022). Lateral-torsional buckling resistance prediction model for steel cellular beams generated by Artificial Neural Networks (ANN). Thin-Walled Structures, 170, 108592. doi: 10.1016/j.tws.2021.108592

Abstract

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

Publication Type: Article
Additional Information: © 2022. This manuscript version is made available under the CC-BY-NC-ND 4.0 license https://creativecommons.org/licenses/by-nc-nd/4.0/
Publisher Keywords: Artificial neural network, Machine learning, Steel cellular beams, Lateral–torsional buckling, Finite element method
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
T Technology > TJ Mechanical engineering and machinery
Departments: School of Mathematics, Computer Science & Engineering > Engineering > Civil Engineering
[img] Text - Accepted Version
This document is not freely accessible until 15 November 2022 due to copyright restrictions.
Available under License Creative Commons Attribution Non-commercial No Derivatives.

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