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Research Papers

Viscoelasticity and Preconditioning of Rat Skin Under Uniaxial Stretch: Microstructural Constitutive Characterization

[+] Author and Article Information
Olga Lokshin

Faculty of Biomedical Engineering, Technion–Israel Institute of Technology, Haifa 32000, Israel

Yoram Lanir1

Faculty of Biomedical Engineering, Technion–Israel Institute of Technology, Haifa 32000, Israelyoramlanir@yahoo.com

1

Corresponding author.

J Biomech Eng 131(3), 031009 (Jan 07, 2009) (10 pages) doi:10.1115/1.3049479 History: Received November 20, 2007; Revised May 16, 2008; Published January 07, 2009

In spite of impressive progress in developing general constitutive laws for soft tissues, there exists still no comprehensive model valid for any general deformation scheme. The present study focuses on the uniaxial response of the skin as a model for other multifibrous soft tissues. While the skin’s nonlinear viscoelastic constitutive response has been extensively studied and modeled, the phenomena associated with mechanical preconditioning have so far not been dealt with. Yet preconditioning is an inherent response feature in the skin, both in vitro and in vivo. It is hypothesized that by considering the structure of the elastic and collagen fibers and their individual rheological properties, it is possible to develop a reliable general constitutive law for the skin’s uniaxial response. A stochastic hybrid constitutive model was developed based on the collagen and elastic fiber morphologies and their rheological properties. The multiple protocol uniaxial data of Eshel and Lanir (“Effects of Strain Level and Proteoglycan Depletion on Preconditioning and Viscoelastic Responses of Rat Dorsal Skin  ,” 2001, Ann. Biomed. Eng., 29, pp. 164–172) served to estimate the model’s parameters and to validate its reliability. Parametric investigation was then used to test model parsimony (minimal form) and to elucidate the roles of response mechanism and the relative contribution of each constituent. The model predictions show a very close fit to the data and good predictive capability. The results are consistent with the quasilinear viscoelastic response of both elastic and collagen fibers and are likewise consistent with the notion (supported by published experimental observations) that preconditioning in collagen is probably due to an increase in the fiber reference length and is due to strain softening (Mullins effect) in elastic fibers. The predictions also agree with the observed predominance of elastic fibers at low strains and suggest that as strain increases, collagen becomes predominant, but the effect of elastic fibers is still significant. The parsimony analysis of the 22 model parameters (18 are nonlinear in the model) points to the predominant role of viscoelasticity and preconditioning in both fibers, followed in order of importance by collagen waviness and elastic fiber nonlinearity. A reliable and comprehensive uniaxial constitutive law for the rat skin was developed based on the tissue microstructure and on its constituents’ rheological properties.

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Copyright © 2009 by American Society of Mechanical Engineers
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Figures

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Figure 1

The test protocol: (a) the strain protocol consisted of a number of stretch sets with ascending strain level; (b) the protocol of one set with constant maximal strain level; (c) the corresponding measured stress for the protocol in (b). Note the preconditioning effect, i.e., decrease in peak stress at consecutive cycles.

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Figure 2

Comparison between the measured response of sample jlo6 (light line) and the model prediction (dark line)

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Figure 3

The stress-strain responses for the first loading phase in each set (sample jl06). There is increased shift to the right with increasing ramp strain level and parallel reduction in the initial slope. The reference (”Gage”) length remains unaltered.

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Figure 4

Comparison between measured (sample jl06) stress response during the first loading phases in each set (gray line) and model prediction (dark line). Different stress scales were used in the sub-figures.

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Figure 5

The measured response of sample jlo6 (gray line), and comparison with the model prediction with parameters estimated from: the full data (dark line), from a partial database, excluding the data of Set 4 (inset, thin line). The “full-data” model predictions overlap the data itself. In sets 1,2,3,5, the same is true for the “partial-data” model predictions.

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