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Bayesian Parameter Estimation for Hyperelastic Constitutive Models of Soft Tissue under Non-homogeneous Deformation

Kenja, Krishna
http://rave.ohiolink.edu/etdc/view?acc_num=ucin1515505801223584

Abstract Details

2017, MS, University of Cincinnati, Engineering and Applied Science: Aerospace Engineering.
The study of soft tissue mechanics is critical in a wide range of applications like computer aided surgery and functional tissue engineering. These applications require that the behavior of soft tissues be simulated and predicted using constitutive models developed through a continuum mechanics framework using experimental data. Variability is an inherent factor in soft tissue data that needs to be addressed in order to quantify the uncertainty in the constitutive models. This work proposes two ways to aid in the development of accurate computational models for simulating the mechanical response of soft tissues. First, we use a Bayesian approach to pose the constitutive model parameter estimation problem in a probabilistic framework, instead of traditional deterministic methods, allowing the incorporation of variability present in soft tissue data. This inclusion enables a stochastic treatment of the parameters resulting in probability densities, rather than deterministic values, for the model parameters. Secondly the epistemic uncertainties in the constitutive models can be overcome by combining them with finite element models of the experimental setup, which allows us to accurately represent the boundary conditions. With numerical experiments carried out using multiple constitutive models, qualitative as well as quantitative criteria are developed to selecting a model that best explains the experimental data. These quantities that help in assessing the suitability of a model are statistically evaluated from the parameter probability densities. The necessity for post-estimation analysis is highlighted through a demonstration of non-physical behavior from a constitutive model that otherwise accurately captures the mechanical response from an experiment.
Shaaban Abdallah, Ph.D. (Committee Chair)
Kumar Vemaganti, Ph.D. (Committee Chair)
Gui-Rong Liu, Ph.D. (Committee Member)
106 p.
Engineering
soft tissues; hyperelastic; bayesian framework; non-homogeneous deformation

Recommended Citations

Citations

  • Kenja, K. (2017). Bayesian Parameter Estimation for Hyperelastic Constitutive Models of Soft Tissue under Non-homogeneous Deformation [Master's thesis, University of Cincinnati]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1515505801223584

    APA Style (7th edition)

  • Kenja, Krishna. Bayesian Parameter Estimation for Hyperelastic Constitutive Models of Soft Tissue under Non-homogeneous Deformation. 2017. University of Cincinnati, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=ucin1515505801223584.

    MLA Style (8th edition)

  • Kenja, Krishna. "Bayesian Parameter Estimation for Hyperelastic Constitutive Models of Soft Tissue under Non-homogeneous Deformation." Master's thesis, University of Cincinnati, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1515505801223584

    Chicago Manual of Style (17th edition)

ucin1515505801223584
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© 2017, all rights reserved.
This open access ETD is published by University of Cincinnati and OhioLINK.