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Calibration of hyperelastic constitutive models: the role of boundary conditions, search algorithms, and experimental variability

Biomechanics and Modeling in Mechanobiology volume 19, pages 1935–1952 (2020)Cite this article

Abstract

The calibration of hyperelastic constitutive models of soft tissue and tissue surrogates is often treated as an exercise in curve-fitting to the average experimental response, and many of the complicating factors such as experimental boundary conditions and data variability are ignored. In this work, we focus on three questions that arise in this area: the ramifications of ignoring the experimental boundary conditions, the use of local optimizers, and the role of data variability. Using data from a uniaxial extension experiment on a tissue surrogate, we study how these three factors affect the calibration of isotropic hyperelastic constitutive models. Our results show that even with the simplest of constitutive models, it is necessary to look beyond a “good fit” to the average.

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Affiliations

  1. Department of Mechanical and Materials Engineering, University of Cincinnati, Cincinnati, OH, 45221-0072, USA

    Krishna Kenja & Kumar Vemaganti

  2. Mathematics and Computer Science Division, Argonne National Laboratory, Argonne, IL, 60439, USA

    Sandeep Madireddy

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  1. Krishna Kenja
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  2. Sandeep Madireddy
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Correspondence to Kumar Vemaganti.

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Kenja, K., Madireddy, S. & Vemaganti, K. Calibration of hyperelastic constitutive models: the role of boundary conditions, search algorithms, and experimental variability. Biomech Model Mechanobiol 19, 1935–1952 (2020). https://doi.org/10.1007/s10237-020-01318-3

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  • DOI: https://doi.org/10.1007/s10237-020-01318-3

Keywords

  • Constitutive modeling
  • Hyperelastic
  • Bayesian
  • Uncertainty
  • Search algorithm
  • Soft tissue