Journal
PROCEEDINGS OF THE ROYAL SOCIETY A-MATHEMATICAL PHYSICAL AND ENGINEERING SCIENCES
Volume 474, Issue 2211, Pages -Publisher
ROYAL SOC
DOI: 10.1098/rspa.2017.0858
Keywords
stochastic hyperelastic models; nonlinear elastic deformations; uncertainty quantification; rubber; brain tissue; model selection
Categories
Funding
- Engineering and Physical Sciences Research Council of Great Britain [EP/M011992/1]
- EPSRC [EP/M011992/1] Funding Source: UKRI
- Engineering and Physical Sciences Research Council [EP/M011992/1] Funding Source: researchfish
Ask authors/readers for more resources
Biological and synthetic materials often exhibit intrinsic variability in their elastic responses under large strains, owing to microstructural inhomogeneity or when elastic data are extracted from viscoelastic mechanical tests. For these materials, although hyperelastic models calibrated to mean data are useful, stochastic representations accounting also for data dispersion carry extra information about the variability of material properties found in practical applications. We combine finite elasticity and information theories to construct homogeneous isotropic hyperelastic models with random field parameters calibrated to discrete mean values and standard deviations of either the stress-strain function or the nonlinear shear modulus, which is a function of the deformation, estimated from experimental tests. These quantities can take on different values, corresponding to possible outcomes of the experiments. As multiple models can be derived that adequately represent the observed phenomena, we apply Occam's razor by providing an explicit criterion for model selection based on Bayesian statistics. We then employ this criterion to select a model among competing models calibrated to experimental data for rubber and brain tissue under single or multiaxial loads.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available