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An In Silico-Based Investigation on Anisotropic Hyperelastic Constitutive Models for Soft Biological Tissues

Hüsnü Dal Orcid Logo, Alp Kağan Açan, Ciara Durcan, Mokarram Hossain Orcid Logo

Archives of Computational Methods in Engineering, Volume: 30, Issue: 8, Pages: 4601 - 4632

Swansea University Authors: Ciara Durcan, Mokarram Hossain Orcid Logo

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DOI (Published version): 10.1007/s11831-023-09956-3

Abstract

We review twelve invariant and dispersion-type anisotropic hyperelastic constitutive models for soft biological tissues based on their fitting performance to various experimental data. To this end, we used a hybrid multi-objective optimization procedure along with a genetic algorithm to generate the...

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Published in: Archives of Computational Methods in Engineering
ISSN: 1134-3060 1886-1784
Published: Springer Science and Business Media LLC 2023
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URI: https://cronfa.swan.ac.uk/Record/cronfa63941
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spelling v2 63941 2023-07-25 An In Silico-Based Investigation on Anisotropic Hyperelastic Constitutive Models for Soft Biological Tissues 7ecf37dea19f79476ca596fa79d01454 Ciara Durcan Ciara Durcan true false 140f4aa5c5ec18ec173c8542a7fddafd 0000-0002-4616-1104 Mokarram Hossain Mokarram Hossain true false 2023-07-25 We review twelve invariant and dispersion-type anisotropic hyperelastic constitutive models for soft biological tissues based on their fitting performance to various experimental data. To this end, we used a hybrid multi-objective optimization procedure along with a genetic algorithm to generate the initial guesses followed by a gradient-based search algorithm. The constitutive models are then fit to a set of uniaxial and biaxial tension experiments conducted on tissues with different histology. For the in silico investigation, experiments conducted on human aneurysmatic abdominal aorta, linea alba, and rectus sheath tissues are utilized. Accordingly, the models are ranked with respect to an objective normalized quality of fit metric. Finally, a detailed discussion is carried out on the fitting performance of the models. This work provides a valuable quantitative comparison of various anisotropic hyperelastic models, the findings of which can aid researchers in selecting the most suitable constitutive model for their particular analysis. The investigation reveals superior fitting performance of dispersion-type anisotropic constitutive formulations over invariant formulations. Journal Article Archives of Computational Methods in Engineering 30 8 4601 4632 Springer Science and Business Media LLC 1134-3060 1886-1784 1 11 2023 2023-11-01 10.1007/s11831-023-09956-3 COLLEGE NANME COLLEGE CODE Swansea University 2024-09-04T16:22:11.3113142 2023-07-25T09:01:46.5472605 Faculty of Science and Engineering School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Mechanical Engineering Hüsnü Dal 0000-0002-2973-3991 1 Alp Kağan Açan 2 Ciara Durcan 3 Mokarram Hossain 0000-0002-4616-1104 4 63941__28368__e8ef468cc335428ba8e35c7ff5d38659.pdf 63941AM.pdf 2023-08-24T08:46:50.6930722 Output 1452155 application/pdf Accepted Manuscript true 2024-07-24T00:00:00.0000000 true eng
title An In Silico-Based Investigation on Anisotropic Hyperelastic Constitutive Models for Soft Biological Tissues
spellingShingle An In Silico-Based Investigation on Anisotropic Hyperelastic Constitutive Models for Soft Biological Tissues
Ciara Durcan
Mokarram Hossain
title_short An In Silico-Based Investigation on Anisotropic Hyperelastic Constitutive Models for Soft Biological Tissues
title_full An In Silico-Based Investigation on Anisotropic Hyperelastic Constitutive Models for Soft Biological Tissues
title_fullStr An In Silico-Based Investigation on Anisotropic Hyperelastic Constitutive Models for Soft Biological Tissues
title_full_unstemmed An In Silico-Based Investigation on Anisotropic Hyperelastic Constitutive Models for Soft Biological Tissues
title_sort An In Silico-Based Investigation on Anisotropic Hyperelastic Constitutive Models for Soft Biological Tissues
author_id_str_mv 7ecf37dea19f79476ca596fa79d01454
140f4aa5c5ec18ec173c8542a7fddafd
author_id_fullname_str_mv 7ecf37dea19f79476ca596fa79d01454_***_Ciara Durcan
140f4aa5c5ec18ec173c8542a7fddafd_***_Mokarram Hossain
author Ciara Durcan
Mokarram Hossain
author2 Hüsnü Dal
Alp Kağan Açan
Ciara Durcan
Mokarram Hossain
format Journal article
container_title Archives of Computational Methods in Engineering
container_volume 30
container_issue 8
container_start_page 4601
publishDate 2023
institution Swansea University
issn 1134-3060
1886-1784
doi_str_mv 10.1007/s11831-023-09956-3
publisher Springer Science and Business Media LLC
college_str Faculty of Science and Engineering
hierarchytype
hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Mechanical Engineering{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Mechanical Engineering
document_store_str 1
active_str 0
description We review twelve invariant and dispersion-type anisotropic hyperelastic constitutive models for soft biological tissues based on their fitting performance to various experimental data. To this end, we used a hybrid multi-objective optimization procedure along with a genetic algorithm to generate the initial guesses followed by a gradient-based search algorithm. The constitutive models are then fit to a set of uniaxial and biaxial tension experiments conducted on tissues with different histology. For the in silico investigation, experiments conducted on human aneurysmatic abdominal aorta, linea alba, and rectus sheath tissues are utilized. Accordingly, the models are ranked with respect to an objective normalized quality of fit metric. Finally, a detailed discussion is carried out on the fitting performance of the models. This work provides a valuable quantitative comparison of various anisotropic hyperelastic models, the findings of which can aid researchers in selecting the most suitable constitutive model for their particular analysis. The investigation reveals superior fitting performance of dispersion-type anisotropic constitutive formulations over invariant formulations.
published_date 2023-11-01T16:22:09Z
_version_ 1809279436260900864
score 11.037603

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