Details
| Original language | English |
|---|---|
| Pages (from-to) | 1011-1036 |
| Number of pages | 26 |
| Journal | Biomechanics and Modeling in Mechanobiology |
| Volume | 17 |
| Issue number | 4 |
| Publication status | Published - 28 Feb 2018 |
Abstract
Soft tissues are characterized by a nonlinear mechanical response, highly affected by the multiscale structure of collagen fibers. The effectiveness and the calibration of constitutive models play a major role on the reliability and the applicability of computational models in biomechanics. This paper presents a procedure for the identification of the relationship between collagen-related structural features in soft tissues with model parameters of classical polynomial- and exponential-based constitutive models. Histological features at microscale, as well as biochemical and biophysical properties at nanoscale, are addressed by employing a multiscale structural description of soft tissue mechanics as benchmark data set. Both the direct (from structure to parameters) and the inverse (from parameters to structure) problem are addressed. Suitable optimization problems are introduced for accurate numerical and approximated analytical direct relationships. The inverse identification has been addressed by providing also a measure of the reliability of the computed estimates. Results show the effectiveness of the proposed strategies and allow to discuss the fitting capabilities of classical constitutive approaches in terms of parameters identification.
Keywords
- Constitutive models, Inverse analysis, Parameters identification, Soft tissue mechanics, Structure–mechanics relationship
ASJC Scopus subject areas
- Biochemistry, Genetics and Molecular Biology(all)
- Biotechnology
- Mathematics(all)
- Modelling and Simulation
- Engineering(all)
- Mechanical Engineering
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In: Biomechanics and Modeling in Mechanobiology, Vol. 17, No. 4, 28.02.2018, p. 1011-1036.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Direct and inverse identification of constitutive parameters from the structure of soft tissues. Part 1: micro- and nanostructure of collagen fibers
AU - Marino, Michele
AU - von Hoegen, Markus
AU - Schröder, Jörg
AU - Wriggers, Peter
N1 - Funding Information: Acknowledgements M. Marino acknowledges that this work has been carried out within the framework of the SMART BIOTECS alliance between the Technical University of Braunschweig and the Leibniz University of Hannover. This initiative is financially supported by the Ministry of Economy and Culture (MWK) of Lower Saxony, Germany. Funding Information: M. Marino acknowledges that this work has been carried out within the framework of the SMART BIOTECS alliance between the Technical University of Braunschweig and the Leibniz University of Hannover. This initiative is financially supported by the Ministry of Economy and Culture (MWK) of Lower Saxony, Germany. Michele Marino and Markus von Hoegen have contributed equally to this work. The authors declare that they have no conflict of interest. Publisher Copyright: © 2018, Springer-Verlag GmbH Germany, part of Springer Nature. Copyright: Copyright 2018 Elsevier B.V., All rights reserved.
PY - 2018/2/28
Y1 - 2018/2/28
N2 - Soft tissues are characterized by a nonlinear mechanical response, highly affected by the multiscale structure of collagen fibers. The effectiveness and the calibration of constitutive models play a major role on the reliability and the applicability of computational models in biomechanics. This paper presents a procedure for the identification of the relationship between collagen-related structural features in soft tissues with model parameters of classical polynomial- and exponential-based constitutive models. Histological features at microscale, as well as biochemical and biophysical properties at nanoscale, are addressed by employing a multiscale structural description of soft tissue mechanics as benchmark data set. Both the direct (from structure to parameters) and the inverse (from parameters to structure) problem are addressed. Suitable optimization problems are introduced for accurate numerical and approximated analytical direct relationships. The inverse identification has been addressed by providing also a measure of the reliability of the computed estimates. Results show the effectiveness of the proposed strategies and allow to discuss the fitting capabilities of classical constitutive approaches in terms of parameters identification.
AB - Soft tissues are characterized by a nonlinear mechanical response, highly affected by the multiscale structure of collagen fibers. The effectiveness and the calibration of constitutive models play a major role on the reliability and the applicability of computational models in biomechanics. This paper presents a procedure for the identification of the relationship between collagen-related structural features in soft tissues with model parameters of classical polynomial- and exponential-based constitutive models. Histological features at microscale, as well as biochemical and biophysical properties at nanoscale, are addressed by employing a multiscale structural description of soft tissue mechanics as benchmark data set. Both the direct (from structure to parameters) and the inverse (from parameters to structure) problem are addressed. Suitable optimization problems are introduced for accurate numerical and approximated analytical direct relationships. The inverse identification has been addressed by providing also a measure of the reliability of the computed estimates. Results show the effectiveness of the proposed strategies and allow to discuss the fitting capabilities of classical constitutive approaches in terms of parameters identification.
KW - Constitutive models
KW - Inverse analysis
KW - Parameters identification
KW - Soft tissue mechanics
KW - Structure–mechanics relationship
UR - http://www.scopus.com/inward/record.url?scp=85042630155&partnerID=8YFLogxK
U2 - 10.1007/s10237-018-1009-8
DO - 10.1007/s10237-018-1009-8
M3 - Article
C2 - 29492724
AN - SCOPUS:85042630155
VL - 17
SP - 1011
EP - 1036
JO - Biomechanics and Modeling in Mechanobiology
JF - Biomechanics and Modeling in Mechanobiology
SN - 1617-7959
IS - 4
ER -