Browsing by Author "Benaissa, B."

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    Application of proper orthogonal decomposition and radial basis functions for crack size estimation using particle swarm optimization
    (IOP Publishing, 2017) Benaissa, B.; Köppen, M.; Abdel Wahab, M.; Khatir, S.
    Complex engineering problems require simulations, which are computationally expensive in cases of inverse identification tasks since they commonly requires hundreds of thousands of simulations. This paper propose a method based on model reduction for crack size estimation, combining the proper orthogonal decomposition method with radial basis functions. The reduced model is validated by comparing the obtained boundary displacements with the corresponding results from a finite element model. This inverse procedure is formulated as the minimization of the difference between the measured and computed values of displacement at selected boundary nodes, called sensor points, using particle swarm optimization algorithm. Convex and a non-convex specimens have been considered for investigations of crack presence, and identification of its size, different crack sizes have been tested to demonstrate the efficiency of the proposed approach
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    Crack growth estimation using model reduction and genetic algorithm
    (· · · · ·, 2014) Benaissa, B.; Belaidi, Idir; Khatir, S.; Hamrani, A.; Lessoued, M.; Sahali, M. A.
    In this study we use the material elastic properties as a base, a tow dimensional cracked plate under traction is modelled by finite element method (FEM) than a reduced model is built using the proper orthogonal decomposition method (POD), the crack length is estimated as an inverse identification problem, basing on the deformation obtained from the boundary nodes of the structure considered as sensor points. A genetic algorithm (GA) is used for the minimization of the error function which is expressed as the difference between displacement field of the boundaries caused by the crack size proposed randomly by GA and the field measured at the actual identity. The approach presented accurate results and could guess the real crack size in a precession less that 10-6 of the cost function, proving its effectiveness even with a very low number of 4 sensors, and shows that the boundary displacement measurements are practical. The use of the reduced model provides tangible benefits mainly the very low computational cost