Uncertainty quantification of elastic material responses: testing, stochastic calibration and Bayesian model selection

Fitt, Danielle, Wyatt, Hayley, Woolley, Thomas ORCID: https://orcid.org/0000-0001-6225-5365 and Mihai, L. Angela ORCID: https://orcid.org/0000-0003-0863-3729 2019. Uncertainty quantification of elastic material responses: testing, stochastic calibration and Bayesian model selection. Mechanics of Soft Materials 1 , 13. 10.1007/s42558-019-0013-1

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Abstract

Motivated by the need to quantify uncertainties in the mechanical behaviour of solid materials, we perform simple uniaxial tensile tests on a manufactured rubber-like material that provide critical information regarding the variability in the constitutive responses between different specimens. Based on the experimental data, we construct stochastic homogeneous hyperelastic models where the parameters are described by spatially independent probability density functions at a macroscopic level. As more than one parametrised model is capable of capturing the observed material behaviour, we apply Baye theorem to select the model that is most likely to reproduce the data. Our analysis is fully tractable mathematically and builds directly on knowledge from deterministic finite elasticity. The proposed stochastic calibration and Bayesian model selection are generally applicable to more complex tests and materials.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Engineering Mathematics
Publisher:	Springer
ISSN:	2524-5600
Funders:	EPSRC
Date of First Compliant Deposit:	17 October 2019
Date of Acceptance:	9 October 2019
Last Modified:	06 Nov 2024 11:07
URI:	https://orca.cardiff.ac.uk/id/eprint/126123

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