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Prediction of arterial failure based on a microstructural bi-layer fiber–matrix model with softening
Abstract Two approaches to predict failure of soft tissue are available. The first is based on a pointwise criticality condition, e.g. von Mises maximum stress, which is restrictive because only local state of deformation is considered to be critical and the failure criterion is separated from stres...
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Published in: | Journal of biomechanics 2008-01, Vol.41 (2), p.447-453 |
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Main Author: | |
Format: | Article |
Language: | English |
Subjects: | |
Citations: | Items that this one cites Items that cite this one |
Online Access: | Get full text |
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Summary: | Abstract Two approaches to predict failure of soft tissue are available. The first is based on a pointwise criticality condition, e.g. von Mises maximum stress, which is restrictive because only local state of deformation is considered to be critical and the failure criterion is separated from stress analysis. The second is based on damage mechanics where internal (unobservable) variables are introduced which make the experimental calibration of the theory complex. As an alternative to the local failure criteria and damage mechanics we present a softening hyperelasticity approach, where the constitutive description of soft tissue is enhanced with strain softening, which is controlled by material constants. This approach is attractive because the new material constants can be readily calibrated in experiments on the one hand and the failure criteria are global on the other hand. We illustrate the efficiency of the softening hyperelasticity approach on the problem of prediction of arterial failure. For this purpose, we enhance a bi-layer fiber–matrix microstructural arterial model with softening and analyze the arterial failure under internal pressure. We show that the overall arterial strength is (a) dominated by the media layer, (b) controlled by microfibers and (c) increased by residual stresses. |
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ISSN: | 0021-9290 1873-2380 |
DOI: | 10.1016/j.jbiomech.2007.08.001 |