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Title: | Exploring the stainless-steel beam-to-column connections response: A hybrid explainable machine learning framework for characterization |
Authors: | Sarfarazi, S Shamass, R Guarracino, F Mascolo, I Modano, M |
Keywords: | steel connections;stainless-steel;machine learning;explainable models;moment-rotation response |
Issue Date: | 17-Jan-2025 |
Publisher: | Springer Nature |
Citation: | Sarfarazi, S. et al. (2025) 'Exploring the stainless-steel beam-to-column connections response: A hybrid explainable machine learning framework for characterization', Frontiers of Structural and Civil Engineering, 19 (1), pp. 34 - 59. doi: 10.1007/s11709-025-1162-y. |
Abstract: | Stainless-steel provides substantial advantages for structural uses, though its upfront cost is notably high. Consequently, it’s vital to establish safe and economically viable design practices that enhance material utilization. Such development relies on a thorough understanding of the mechanical properties of structural components, particularly connections. This research advances the field by investigating the behavior of stainless-steel connections through the use of a four-parameter fitting technique and explainable artificial intelligence methods. Training was conducted on eight different machine learning algorithms, namely, Decision Tree, Random Forest, K-nearest neighbors, Gradient Boosting, Extreme Gradient Boosting, Light Gradient Boosting, Adaptive Boosting, and Categorical Boosting. SHapley Additive Explanations was applied to interpret model predictions, highlighting features like spacing between bolts in tension and end-plate height as highly impactful on the initial rotational stiffness and plastic moment resistance. Results showed that Extreme Gradient Boosting achieved a coefficient of determination score of 0.99 for initial stiffness and plastic moment resistance, while Gradient Boosting model had similar performance with maximum moment resistance and ultimate rotation. A user-friendly graphical user interface (GUI) was also developed, allowing engineers to input parameters and get rapid moment–rotation predictions. This framework offers a data-driven, interpretable alternative to conventional methods, supporting future design recommendations for stainless-steel beam-to-column connections. |
URI: | https://bura.brunel.ac.uk/handle/2438/32075 |
DOI: | https://doi.org/10.1007/s11709-025-1162-y |
ISSN: | 2095-2430 |
Other Identifiers: | ORCiD: Rabee Shamass https://orcid.org/0000-0002-7990-8227 |
Appears in Collections: | Dept of Civil and Environmental Engineering Embargoed Research Papers |
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FullText.pdf | Embargoed until 17 January 2026. Copyright © Higher Education Press 2025, under exclusive licence to Springer Nature Switzerland AG 2025. This version of the article has been accepted for publication, after peer review (when applicable) and is subject to Springer Nature’s AM terms of use, but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: https://doi.org/10.1007/s11709-025-1162-y (see: https://www.springernature.com/gp/open-research/policies/journal-policies ). | 2.68 MB | Adobe PDF | View/Open |
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