This paper presents an approach of inverse damage detection and localization based on model reduction. The problem is formulated as an inverse problem where an optimization algorithm is used to minimize the cost function expressed as the normalized difference between a frequency vector of the tested structure and its numerical model. A finite element model of bi-dimensional monolithic composite beam reinforced by a graphite-epoxy is used to define a numerical model of the tested structure in which different scenarios of damage are considered by stiffness reduction. Then, calculations are made on a reduced model built by the technique of proper orthogonal decomposition coupled by radial basis functions. The accuracy of the method is verified through different damage configurations. The results show that the developed algorithm is a feasible methodology of predicting damage in short computing time and with high accuracy. The effect of noise on the accuracy of the results is investigated in some cases for the structure under consideration
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