Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/28209
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dc.contributor.authorRabi, M-
dc.contributor.authorAbarkan, I-
dc.contributor.authorShamass, R-
dc.date.accessioned2024-02-04T16:41:40Z-
dc.date.available2024-02-04T16:41:40Z-
dc.date.issued2023-07-24-
dc.identifierORCID iD: Rabee Shamass https://orcid.org/0000-0002-7990-8227-
dc.identifier.citationRabi, M., Abarkan, I. and Shamass, R. (2024) 'Buckling resistance of hot‐finished CHS beam‐columns using FE modelling and machine learning', Steel Construction, 0 (ahead of print), pp. 1 - 11. doi: 10.1002/stco.202200036.en_US
dc.identifier.issn1867-0520-
dc.identifier.urihttps://bura.brunel.ac.uk/handle/2438/28209-
dc.description.abstractThe use of circular hollow sections (CHS) has increased in recent years owing to its excellent mechanical behaviour including axial compression and torsional resistance as well as its aesthetic appearance. They are popular in a wide range of structural members, including beams, columns, trusses and arches. The behaviour of hot-finished CHS beam-columns made from normal- and high-strength steels is the main focus of this article. A particular attention is given to predict the ultimate buckling resistance of CHS beam-columns using the recent advancement of the artificial neural network (ANN). Finite element (FE) models were established and validated to generate an extensive parametric study. The ANN model is trained and validated using a total of 3439 data points collected from the generated FE models and experimental tests available in the literature. A comprehensive comparative analysis with the design rules in Eurocode 3 is conducted to evaluate the performance of the developed ANN model. It is shown that the proposed ANN-based design formula provides a reliable means for predicting the buckling resistance of the CHS beam-columns. This formula can be easily implemented in any programming software, providing an excellent basis for engineers and designers to predict the buckling resistance of the CHS beam–columns with a straightforward procedure in an efficient and sustainable manner with least computational time.en_US
dc.format.extent1 - 11-
dc.format.mediumPrint-Electronic-
dc.languageEnglish-
dc.language.isoen_USen_US
dc.publisherWiley on behalf of Ernst & Sohn GmbHen_US
dc.rightsCopyright © 2023 Ernst & Sohn GmbH. This is the peer reviewed version of the following article: Rabi, M., Abarkan, I. and Shamass, R. (2024), Buckling resistance of hot-finished CHS beam-columns using FE modelling and machine learning. Steel Construction , 0 (ahead of print), pp. 1 - 11, which has been published in final form at https://doi.org/10.1002/stco.202200036. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions (see: https://authorservices.wiley.com/author-resources/Journal-Authors/licensing/self-archiving.html).-
dc.rights.urihttps://authorservices.wiley.com/author-resources/Journal-Authors/licensing/self-archiving.html-
dc.subjectartificial neural networks (ANN)en_US
dc.subjectEurocode 3en_US
dc.subjectFE modellingen_US
dc.subjecthot-finished CHS beam-columnsen_US
dc.subjectnormal- and high-strength steelsen_US
dc.titleBuckling resistance of hot‐finished CHS beam‐columns using FE modelling and machine learningen_US
dc.typeArticleen_US
dc.identifier.doihttps://doi.org/10.1002/stco.202200036-
dc.relation.isPartOfSteel Construction-
pubs.issueahead of print-
pubs.publication-statusPublished online-
pubs.volume0-
dc.identifier.eissn1867-0539-
dc.rights.holderErnst & Sohn GmbH-
Appears in Collections:Dept of Civil and Environmental Engineering Embargoed Research Papers

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