The purpose of this work is to identify simultaneously two irregular interfacial boundaries configurations between the components of three connected domains using a discrete number of displacement measurements obtained by a simple tension test. A unique combination of a global optimization method, i.e., the imperialist competitive algorithm and two local optimization methods, i.e., the conjugate gradient method and Simplex method along with the inverse application of the boundary elements method are employed in an inverse software package. A fitness function, which is the summation of squared differences between calculated displacements and measured displacements at identical locations on the outer boundary, is minimized. Due to the complexity and the ill-posed nature of this identification problem, imperialist competitive algorithm is used to find the best initial guesses of the unknown interface boundaries in order to be used by the local optimization techniques, i.e., conjugate gradient method and then Simplex method to accurately converge to the optimal shape of two irregular interfacial boundaries between the components of an inhomogeneous body. Several examples are selected, and the accuracy of the obtained results is discussed. The effect of experimental measurement errors and the influence of material properties of the sub regions on the identification process are also investigated.
Characterization of the interior of an inhomogeneous body using displacement measurements obtained by conducting a simple tension test was investigated. A homogeneous elastic solid body is assumed to have another solid body with arbitrary shape hidden inside it. The shape and physical properties of this inclusion were unknown. The Boundary Element Method (BEM) coupled with a mete heuristic Genetic Algorithm (GA) and Conjugate Gradient Method (CGM) was used in this characterization problem. A fitness function, which is the summation of squared differences between the measured displacements and calculated displacements at the same locations on the boundary, is minimized using GA and CGM. GA is used to find a good initial estimate of the unknown parameters and the CGM was used as the hybrid function to get converged values of the unknown parameters. For the cases that the fitness function fluctuates severely, a regularization function was added to CGM in order to dampen the fluctuations.
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