Poro-viscoelastic material parameter identification of brain tissue-mimicking hydrogels

Kainz MP, Greiner A, Hinrichsen J, Kolb D, Comellas E, Steinmann P, Budday S, Terzano M, Holzapfel GA (2023)

Publication Type: Journal article

Publication year: 2023


Book Volume: 11

Article Number: 1143304

DOI: 10.3389/fbioe.2023.1143304


Understanding and characterizing the mechanical and structural properties of brain tissue is essential for developing and calibrating reliable material models. Based on the Theory of Porous Media, a novel nonlinear poro-viscoelastic computational model was recently proposed to describe the mechanical response of the tissue under different loading conditions. The model contains parameters related to the time-dependent behavior arising from both the viscoelastic relaxation of the solid matrix and its interaction with the fluid phase. This study focuses on the characterization of these parameters through indentation experiments on a tailor-made polyvinyl alcohol-based hydrogel mimicking brain tissue. The material behavior is adjusted to ex vivo porcine brain tissue. An inverse parameter identification scheme using a trust region reflective algorithm is introduced and applied to match experimental data obtained from the indentation with the proposed computational model. By minimizing the error between experimental values and finite element simulation results, the optimal constitutive model parameters of the brain tissue-mimicking hydrogel are extracted. Finally, the model is validated using the derived material parameters in a finite element simulation.

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Kainz, M.P., Greiner, A., Hinrichsen, J., Kolb, D., Comellas, E., Steinmann, P.,... Holzapfel, G.A. (2023). Poro-viscoelastic material parameter identification of brain tissue-mimicking hydrogels. Frontiers in Bioengineering and Biotechnology, 11. https://doi.org/10.3389/fbioe.2023.1143304


Kainz, Manuel P., et al. "Poro-viscoelastic material parameter identification of brain tissue-mimicking hydrogels." Frontiers in Bioengineering and Biotechnology 11 (2023).

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