Cavity detection in biomechanics by an inverse evolutionary point load BEM technique

    Authors

    D. Ojeda; E. Divo; A. Kassab;M. Cerrolaza

    Comments

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    Abbreviated Journal Title

    Inverse Probl. Sci. Eng.

    Keywords

    boundary element method (BEM); cavity detection; genetic algorithm; elastostatics; BOUNDARY-ELEMENT METHOD; SHAPE OPTIMIZATION; IDENTIFICATION; ALGORITHMS; CRACKS; Engineering, Multidisciplinary; Mathematics, Interdisciplinary; Applications

    Abstract

    An efficient solution of the inverse geometric problem for cavity detection using a point load superposition technique in the elastostatics boundary element method (BEM) is presented in this article. A superposition of point load clusters technique is used to simulate the presence of cavities. This technique offers tremendous advantages in reducing the computational time for the elastostatics field solution as no boundary re-discretization is necessary throughout the inverse problem solution process. The inverse solution is achieved in two steps: (1) fixing the location and strengths of the point loads, (2) locating the cavity geometry. For a current estimated point load distribution, a first objective function measures the difference between BEM-computed and measured deformations at selected points. A genetic algorithm is employed to automatically alter the locations and strengths of the point loads to minimize the objective function. Upon convergence, a second objective function is defined to locate the cavity geometry modelled as traction-free surface. Results of cavity detection simulations using numerical experiments and simulated random measurement errors validate the approach in regular and irregular geometrical configurations.

    Journal Title

    Inverse Problems in Science and Engineering

    Volume

    16

    Issue/Number

    8

    Publication Date

    1-1-2008

    Document Type

    Article; Proceedings Paper

    Language

    English

    First Page

    981

    Last Page

    993

    WOS Identifier

    WOS:000260763600004

    ISSN

    1741-5977

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