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RESEARCH PAPERS

A Dynamic Material Parameter Estimation Procedure for Soft Tissue Using a Poroelastic Finite Element Model

[+] Author and Article Information
J. P. Laible

Department of Civil and Mechanical Engineering, University of Vermont, Burlington, VT 05405

D. Pflaster, M. H. Krag

McClure Musculoskeletal Research Center, Department of Orthopaedics and Rehabilitation, University of Vermont, Burlington, VT 05405

B. R. Simon

Department of Aerospace and Mechanical Engineering, University of Arizona, Tucson, AZ 85721

M. Pope

Department of Civil and Mechanical Engineering and McClure Musculoskeletal Research Center, Department of Orthopaedics and Rehabilitation, University of Vermont, Burlington, VT 05405

L. D. Haugh

McClure Muscolosketal Research Center, Department of Orthopaedics and Rehabilitation and Department of Mathematics and Statistics, University of Vermont, Burlington, VT 05405

J Biomech Eng 116(1), 19-29 (Feb 01, 1994) (11 pages) doi:10.1115/1.2895699 History: Received September 25, 1991; Revised May 04, 1993; Online March 17, 2008

Abstract

A three-dimensional finite element model for a poroelastic medium has been coupled with a least squares parameter estimation method for the purpose of assessing material properties based on intradiscal displacement and reactive forces. Parameter optimization may be based on either load or displacement control experiments. In this paper we present the basis of the finite element model and the parameter estimation process. The method is then applied to a test problem and the computational behavior is discussed. Sequential optimization on different parameter groups was found to have superior convergence properties. Some guidelines for choosing the starting parameter values for optimization were deduced by considering the form of the objective function. For load control experiments, in which displacement data is used for the optimization, the starting values for the elastic modulus should be lower in magnitude than an “anticipated” modulus. The permeability starting values should be higher than an anticipated permeability. For displacement control experiments, the reverse is true. The optimization scheme was also tested on data with random variations.

Copyright © 1994 by The American Society of Mechanical Engineers
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