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ISRN Materials Science
Volume 2013 (2013), Article ID 267165, 8 pages
http://dx.doi.org/10.1155/2013/267165
Research Article

Predictability of Inverse Impact Force Location as Affected by Measurement Noise

1Civil Engineering and Mechanics Laboratory, Abdelmalek Essaâdi University, 91001 Tangier, Morocco
2Communications Systems and Detection Laboratory, Abdelmalek Essaâdi University, 93002 Tetouan, Morocco
3Department of Physics, Faculty of Sciences at Tetouan, P.O. Box 2121, M’Hannech II, 93002 Tetouan, Morocco
4Civil and Environmental Engineering Laboratory, Institute of Applied Sciences at Lyon, 20 Albert Einstein avenue, 69621 Villeurbanne Cedex, France
5University of Lyon, 69622 Lyon, France
6IFSTTAR, LBMC, UMR-T9406, Université Lyon 1, Villeurbanne, France

Received 17 June 2013; Accepted 4 September 2013

Academic Editors: J. L. C. Fonseca, T. Matsumoto, and M. Saitou

Copyright © 2013 Abdelali El-Bakari et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

The impact force localization inverse problem is considered through a nonlinear optimization procedure. The objective function is derived in the particular case of elastic structures for which Maxwell-Betti theorem holds. Additional geometric constraints were introduced in order to stabilize optimum search. The solution of the constrained non linear mathematical problem was performed by means of two outstanding evolutionary algorithms that include Genetic Algorithm and Particle Swarm Optimization. Focus was done on the robustness aspect of force impact localization predictability when an additive white noise is assumed to perturbed strain measurement. It was found that the Genetic Algorithm fails to track the exact solution independently from the noise level as an error was systematically present in the solution. On the other hand, the Particle Swarm Optimisation based algorithm performed very well even for noise levels as high as 2% of the measured strain signal.