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Identifiability of tissue material parameters from uniaxial tests using multi-start optimization.
Safa, Babak N; Santare, Michael H; Ethier, C Ross; Elliott, Dawn M.
Afiliación
  • Safa BN; Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology/Emory University, Atlanta, GA, USA; Department of Biomedical Engineering, University of Delaware, Newark, DE, USA; Department of Mechanical Engineering, University of Delaware, Newark, DE, USA. Electronic addres
  • Santare MH; Department of Biomedical Engineering, University of Delaware, Newark, DE, USA; Department of Mechanical Engineering, University of Delaware, Newark, DE, USA.
  • Ethier CR; Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology/Emory University, Atlanta, GA, USA.
  • Elliott DM; Department of Biomedical Engineering, University of Delaware, Newark, DE, USA.
Acta Biomater ; 123: 197-207, 2021 03 15.
Article en En | MEDLINE | ID: mdl-33444797
ABSTRACT
Determining tissue biomechanical material properties from mechanical test data is frequently required in a variety of applications. However, the validity of the resulting constitutive model parameters is the subject of debate in the field. Parameter optimization in tissue mechanics often comes down to the "identifiability" or "uniqueness" of constitutive model parameters; however, despite advances in formulating complex constitutive relations and many classic and creative curve-fitting approaches, there is currently no accessible framework to study the identifiability of tissue material parameters. Our objective was to assess the identifiability of material parameters for established constitutive models of fiber-reinforced soft tissues, biomaterials, and tissue-engineered constructs and establish a generalizable procedure for other applications. To do so, we generated synthetic experimental data by simulating uniaxial tension and compression tests, commonly used in biomechanics. We then fit this data using a multi-start optimization technique based on the nonlinear least-squares method with multiple initial parameter guesses. We considered tendon and sclera as example tissues, using constitutive models that describe these fiber-reinforced tissues. We demonstrated that not all the model parameters of these constitutive models were identifiable from uniaxial mechanical tests, despite achieving virtually identical fits to the stress-stretch response. We further show that when the lateral strain was considered as an additional fitting criterion, more parameters are identifiable, but some remain unidentified. This work provides a practical approach for addressing parameter identifiability in tissue mechanics.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Tendones / Modelos Biológicos Tipo de estudio: Prognostic_studies Idioma: En Revista: Acta Biomater Año: 2021 Tipo del documento: Article

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Tendones / Modelos Biológicos Tipo de estudio: Prognostic_studies Idioma: En Revista: Acta Biomater Año: 2021 Tipo del documento: Article