Static and dynamic behavior of novel y‐shaped sandwich beams subjected to compressive loadings: integration of supervised learning and experimentation

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Khalvandi, A, Kamarian, S, Bodaghi, M ORCID: https://orcid.org/0000-0002-0707-944X, Saber‐Samandari, S and Song, J, 2025. Static and dynamic behavior of novel y‐shaped sandwich beams subjected to compressive loadings: integration of supervised learning and experimentation. Advanced Engineering Materials. ISSN 1438-1656

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Official URL: https://doi.org/10.1002/adem.202402157

Abstract

In this article, an in-depth investigation into the mechanical response of novel Y-shaped core sandwich beams under static and dynamic compressive loading conditions is presented. Utilizing deep feed-forward neural networks (DFNNs) as the primary supervised learning scheme, the compressive behavior of these advanced structures is predicted. The trained DFNN model demonstrates high fidelity in capturing the stress–strain relationships, as evidenced by the close alignment of predicted and experimental results. Key design parameters of the cores of the sandwich beams are varied to understand their influence on the beams’ linear, plateau, and densification regions, where higher values of design parameters contribute to increased stiffness, prolonged plateau regions, and higher densification points. Additionally, the impact of loading rates (1, 7, and 14 mm min−1) on the mechanical performance is analyzed, revealing significant rate-dependent behaviors. The decision tree algorithm exhibits superior classification performance with a 99.79% accuracy, further validating the robustness of the predictive model. In contrast, the support vector machine algorithm with radial basis function shows moderate accuracy at 75.12%. Through these findings, the potential of DFNNs in predictive modeling and the importance of design parameters and loading rates in optimizing the mechanical performance of novel Y-shaped core sandwich beams is proposed.

Item Type:	Journal article
Publication Title:	Advanced Engineering Materials
Creators:	Khalvandi, A., Kamarian, S., Bodaghi, M., Saber‐Samandari, S. and Song, J.
Publisher:	Wiley
Date:	3 January 2025
ISSN:	1438-1656
Identifiers:	Number Type 10.1002/adem.202402157 DOI 2332812 Other
Rights:	© 2024 The Author(s). Advanced Engineering Materials published by Wiley-VCH GmbH. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
Divisions:	Schools > School of Science and Technology
Record created by:	Laura Borcherds
Date Added:	08 Jan 2025 09:56
Last Modified:	08 Jan 2025 09:56
URI:	https://irep.ntu.ac.uk/id/eprint/52807