The University of Southampton
University of Southampton Institutional Repository

On accurately capturing the through-thickness variation of transverse shear and normal stresses for composite beams using FSDT coupled with GPR

On accurately capturing the through-thickness variation of transverse shear and normal stresses for composite beams using FSDT coupled with GPR
On accurately capturing the through-thickness variation of transverse shear and normal stresses for composite beams using FSDT coupled with GPR

Available shear deformation theories (SDTs) in the literature have their own merits and demerits. Among SDTs, first-order shear deformation theory (FSDT) and higher-order shear deformation theories (HSDT) are most widely used for the analysis of laminated composite and sandwich (LCS) beams. However, these theories are not able to predict the continuation of transverse shear stresses at interfaces across the thickness of the LCS beams. Due to the assumption of the constant variation of the transverse displacement field across the thickness of the layer, the FSDT is not able to predict the values for the transverse normal stresses. The present work aims to transform the stress variations across the thickness of LCS beams obtained from FSDT to the 3D Elasticity solutions using Gaussian Process Regression (GPR) based surrogate model. Further, the surrogate model is exploited to predict the variation of transverse normal stresses σ zz across the thickness. Without large computational efforts, the proposed methodology will be able to capture the through-thickness stress variations equivalent to 3D Elasticity solutions, leading to an accurate yet efficient prediction.

FSDT, Machine learning based on Gaussian process regression, Stress distribution in laminated sandwich beams, Transverse normal stress, Transverse shear stress continuity
0263-8223
Garg, A.
10c59976-908a-4dc7-ab66-1cc0061c8b52
Mukhopadhyay, T.
2ae18ab0-7477-40ac-ae22-76face7be475
Belarbi, M.O.
0ad6efd5-c217-491c-bc4f-9ca42feee384
Chalak, H.D.
f7d08c01-d08a-4d61-af3b-9e08a897a90d
Singh, A.
b75a19c4-0ce4-4602-b07f-b411b7c8652b
Zenkour, A.M.
77c9b90c-f6d5-4eef-baf6-8c042c1b7154
Garg, A.
10c59976-908a-4dc7-ab66-1cc0061c8b52
Mukhopadhyay, T.
2ae18ab0-7477-40ac-ae22-76face7be475
Belarbi, M.O.
0ad6efd5-c217-491c-bc4f-9ca42feee384
Chalak, H.D.
f7d08c01-d08a-4d61-af3b-9e08a897a90d
Singh, A.
b75a19c4-0ce4-4602-b07f-b411b7c8652b
Zenkour, A.M.
77c9b90c-f6d5-4eef-baf6-8c042c1b7154

Garg, A., Mukhopadhyay, T., Belarbi, M.O., Chalak, H.D., Singh, A. and Zenkour, A.M. (2022) On accurately capturing the through-thickness variation of transverse shear and normal stresses for composite beams using FSDT coupled with GPR. Composite Structures, 305, [116551]. (doi:10.1016/j.compstruct.2022.116551).

Record type: Article

Abstract

Available shear deformation theories (SDTs) in the literature have their own merits and demerits. Among SDTs, first-order shear deformation theory (FSDT) and higher-order shear deformation theories (HSDT) are most widely used for the analysis of laminated composite and sandwich (LCS) beams. However, these theories are not able to predict the continuation of transverse shear stresses at interfaces across the thickness of the LCS beams. Due to the assumption of the constant variation of the transverse displacement field across the thickness of the layer, the FSDT is not able to predict the values for the transverse normal stresses. The present work aims to transform the stress variations across the thickness of LCS beams obtained from FSDT to the 3D Elasticity solutions using Gaussian Process Regression (GPR) based surrogate model. Further, the surrogate model is exploited to predict the variation of transverse normal stresses σ zz across the thickness. Without large computational efforts, the proposed methodology will be able to capture the through-thickness stress variations equivalent to 3D Elasticity solutions, leading to an accurate yet efficient prediction.

This record has no associated files available for download.

More information

Accepted/In Press date: 30 November 2022
e-pub ahead of print date: 7 December 2022
Additional Information: Funding Information: TM would like to acknowledge the initiation grant received from IIT Kanpur. Publisher Copyright: © 2022 Elsevier Ltd
Keywords: FSDT, Machine learning based on Gaussian process regression, Stress distribution in laminated sandwich beams, Transverse normal stress, Transverse shear stress continuity

Identifiers

Local EPrints ID: 476392
URI: http://eprints.soton.ac.uk/id/eprint/476392
ISSN: 0263-8223
PURE UUID: 84f6ad4a-ae72-4ac2-93b4-7c7e8a265407
ORCID for T. Mukhopadhyay: ORCID iD orcid.org/0000-0002-0778-6515

Catalogue record

Date deposited: 19 Apr 2023 17:09
Last modified: 17 Mar 2024 04:18

Export record

Altmetrics

Contributors

Author: A. Garg
Author: T. Mukhopadhyay ORCID iD
Author: M.O. Belarbi
Author: H.D. Chalak
Author: A. Singh
Author: A.M. Zenkour

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×