A Bayesian constitutive model selection framework for biaxial mechanical testing of planar soft tissues: application to porcine aortic valves

Aggarwal, A. , Hudson, L. T., Laurence, D. W., Lee, C.-H. and Pant, S. (2023) A Bayesian constitutive model selection framework for biaxial mechanical testing of planar soft tissues: application to porcine aortic valves. Journal of the Mechanical Behavior of Biomedical Materials, 138, 105657. (doi: 10.1016/j.jmbbm.2023.105657)

[img] Text
288751.pdf - Published Version
Available under License Creative Commons Attribution.

3MB

Abstract

A variety of constitutive models have been developed for soft tissue mechanics. However, there is no established criterion to select a suitable model for a specific application. Although the model that best fits the experimental data can be deemed the most suitable model, this practice often can be insufficient given the inter-sample variability of experimental observations. Herein, we present a Bayesian approach to calculate the relative probabilities of constitutive models based on biaxial mechanical testing of tissue samples. 46 samples of porcine aortic valve tissue were tested using a biaxial stretching setup. For each sample, seven ratios of stresses along and perpendicular to the fiber direction were applied. The probabilities of eight invariant-based constitutive models were calculated based on the experimental data using the proposed model selection framework. The calculated probabilities showed that, out of the considered models and based on the information available through the utilized experimental dataset, the May–Newman model was the most probable model for the porcine aortic valve data. When the samples were grouped into different cusp types, the May–Newman model remained the most probable for the left- and right-coronary cusps, whereas for non-coronary cusps two models were found to be equally probable: the Lee–Sacks model and the May–Newman model. This difference between cusp types was found to be associated with the first principal component analysis (PCA) mode, where this mode’s amplitudes of the non-coronary and right-coronary cusps were found to be significantly different. Our results show that a PCA-based statistical model can capture significant variations in the mechanical properties of soft tissues. The presented framework is applicable to any tissue type, and has the potential to provide a structured and rational way of making simulations population-based.

Item Type:Articles
Additional Information:This work was supported by grant EP/P018912/2 from the Engineering and Physical Sciences Research Council of the UK and grant R01 HL159475 from the National Institutes of Health.
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Aggarwal, Dr Ankush
Creator Roles:
Aggarwal, A.Writing – review and editing, Writing – original draft, Visualization, Validation, Software, Methodology, Investigation, Funding acquisition, Data curation, Conceptualization
Authors: Aggarwal, A., Hudson, L. T., Laurence, D. W., Lee, C.-H., and Pant, S.
College/School:College of Science and Engineering > School of Engineering > Infrastructure and Environment
Journal Name:Journal of the Mechanical Behavior of Biomedical Materials
Publisher:Elsevier
ISSN:1751-6161
ISSN (Online):1878-0180
Published Online:05 January 2023
Copyright Holders:Copyright © 2023 The Authors
First Published:First published in Journal of the Mechanical Behavior of Biomedical Materials 138: 105657
Publisher Policy:Reproduced under a Creative Commons License

University Staff: Request a correction | Enlighten Editors: Update this record

Project CodeAward NoProject NamePrincipal InvestigatorFunder's NameFunder RefLead Dept
306966Predicting cardiovascular biomechanical stiffening due to the interplay of tissue layers with focus on calcific aortic valve diseaseAnkush AggarwalEngineering and Physical Sciences Research Council (EPSRC)EP/P018912/2ENG - Infrastructure & Environment