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DC Field | Value | Language |
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dc.contributor.author | Abarkan, I | - |
dc.contributor.author | Rabi, M | - |
dc.contributor.author | Vendramell Ferreira, FP | - |
dc.contributor.author | Shamass, R | - |
dc.contributor.author | Limbachiya, V | - |
dc.contributor.author | Jweihan, YS | - |
dc.contributor.author | Pinho Santos, LF | - |
dc.date.accessioned | 2024-07-18T10:49:27Z | - |
dc.date.available | 2024-07-18T10:49:27Z | - |
dc.date.issued | 2024-02-15 | - |
dc.identifier | ORCiD: Ikram Abarkan https://orcid.org/0000-0002-1269-1553 | - |
dc.identifier | ORCiD: Rabee Shamass https://orcid.org/0000-0002-7990-8227 | - |
dc.identifier | ORCiD: Vireen Limbachiya https://orcid.org/0000-0003-0835-8464 | - |
dc.identifier | ORCiD: Yazeed S. Jweihan https://orcid.org/0000-0003-0200-2942 | - |
dc.identifier | ORCiD: Luis Fernando Pinho Santos https://orcid.org/0000-0002-0009-8752 | - |
dc.identifier | 107952 | - |
dc.identifier.citation | Abarkan, I. et al. (2024) 'Machine learning for optimal design of circular hollow section stainless steel stub columns: A comparative analysis with Eurocode 3 predictions', Engineering Applications of Artificial Intelligence, 132, 107952, pp. 1 - 20. doi: 10.1016/j.engappai.2024.107952. | en_US |
dc.identifier.issn | 0952-1976 | - |
dc.identifier.uri | https://bura.brunel.ac.uk/handle/2438/29373 | - |
dc.description | Data availability: Data will be made available on request. | en_US |
dc.description.abstract | Stainless steel has many advantages when used in structures, however, the initial cost is high. Hence, it is essential to develop reliable and accurate design methods that can optimize the material. As novel, reliable soft computation methods, machine learning provided more accurate predictions than analytical formulae and solved highly complex problems. The present study aims to develop machine learning models to predict the cross-section resistance of circular hollow section stainless steel stub column. A parametric study is conducted by varying the diameter, thickness, length, and mechanical properties of the column. This database is used to train, validate, and test machine learning models, Artificial Neural Network (ANN), Decision Trees for Regression (DTR), Gene Expression Programming (GEP) and Support Vector Machine Regression (SVMR). Thereafter, results are compared with finite element models and Eurocode 3 (EC3) to assess their accuracy. It was concluded that the EC3 models provided conservative predictions with an average Predicted-to-Actual ratio of 0.698 and Root Mean Square Error (RMSE) of 437.3. The machine learning models presented the highest level of accuracy. However, the SVMR model based on RBF kernel presented a better performance than the ANN, GEP and DTR machine learning models, and RMSE value for SVMR, ANN, GEP and DTR is 22.6, 31.6, 152.84 and 29.07, respectively. The GEP leads to the lowest level of accuracy among the other three machine learning models, yet, it is more accurate than EC3. The machine learning models were implemented in a user-friendly tool, which can be used for design purposes. | en_US |
dc.format.extent | 1 - 20 | - |
dc.format.medium | Print-Electronic | - |
dc.language | English | - |
dc.language.iso | en_US | en_US |
dc.publisher | Elsevier | en_US |
dc.rights | Copyright © 2024 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (https://creativecommons.org/licenses/by/4.0/). | - |
dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | - |
dc.subject | circular hollow sections | en_US |
dc.subject | stainless steel | en_US |
dc.subject | finite element model | en_US |
dc.subject | artificial neural network | en_US |
dc.subject | support vector machine regression | en_US |
dc.subject | gene expression programming | en_US |
dc.subject | decision trees for regression | en_US |
dc.title | Machine learning for optimal design of circular hollow section stainless steel stub columns: A comparative analysis with Eurocode 3 predictions | en_US |
dc.type | Article | en_US |
dc.date.dateAccepted | 2024-01-19 | - |
dc.identifier.doi | https://doi.org/10.1016/j.engappai.2024.107952 | - |
dc.relation.isPartOf | Engineering Applications of Artificial Intelligence | - |
pubs.publication-status | Published | - |
pubs.volume | 132 | - |
dc.identifier.eissn | 1873-6769 | - |
dc.rights.license | https://creativecommons.org/licenses/by/4.0/legalcode.en | - |
dc.rights.holder | The Authors | - |
Appears in Collections: | Dept of Civil and Environmental Engineering Research Papers |
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FullText.pdf | Copyright © 2024 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (https://creativecommons.org/licenses/by/4.0/). | 13.96 MB | Adobe PDF | View/Open |
This item is licensed under a Creative Commons License