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Determination of poroelastic properties of cartilage using constrained optimization coupled with finite element analysis.


ABSTRACT: The feasibility of determining biphasic material properties using a finite element model of stress relaxation coupled with two types of constrained optimization to match measured data was investigated. Comparison of these two approaches, a zero-order method and a gradient-based algorithm, validated the predicted material properties. Optimizations were started from multiple different initial guesses of material properties (design variables) to establish the robustness of the optimization. Overall, the optimal values are close to those found by Cohen et al. (1998) but these small differences produced a marked improvement in the fit to the measured stress relaxation. Despite the greater deviation in the optimized values obtained from the zero-order method, both optimization procedures produced material properties that gave equally good overall fits to the measured data. Furthermore, optimized values were all within the expected range of material properties. Modeling stress relaxation using the optimized material properties showed an excellent fit to the entire time history of the measured data.

SUBMITTER: Chung CY 

PROVIDER: S-EPMC4286505 | biostudies-literature | 2015 Feb

REPOSITORIES: biostudies-literature

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Determination of poroelastic properties of cartilage using constrained optimization coupled with finite element analysis.

Chung Chen-Yuan CY   Mansour Joseph M JM  

Journal of the mechanical behavior of biomedical materials 20141028


The feasibility of determining biphasic material properties using a finite element model of stress relaxation coupled with two types of constrained optimization to match measured data was investigated. Comparison of these two approaches, a zero-order method and a gradient-based algorithm, validated the predicted material properties. Optimizations were started from multiple different initial guesses of material properties (design variables) to establish the robustness of the optimization. Overall  ...[more]

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