The use of changes in the dynamic response of a structure after damage has become very popular in the scientific community to formulate methodologies that permit assessing the integrity of a structure. In this sense, a finite-element model (FEM) that represents the damaged structure can be obtained by minimizing the difference between the dynamic response of this model and the response of the current structure obtained experimentally. The optimization variables are composed of the stiffness reduction factor of each element that belongs to the FEM of the undamaged structure. This paper proposes the use of an adaptive Particle Swarm Optimizer to solve the optimization problem associated with the detection of the damage in a multi-supported beam structure and studies how incomplete data affect the performance of the proposed algorithm. Adaptation is implemented to avoid the definition of the PSO parameters-cognitive and social parameters-by trial and error. Natural frequencies and mode shapes were selected as the dynamic characteristics to be used in the objective function. As this research was developed by using numerical simulations, information about only a few first modes in specific degrees of freedom was considered available in order to take into account the incomplete measurement issue. Results have shown that a minimum quantity of modal data is necessary to guarantee the success of the damage detection methodology and that the ability to locate and quantify damage may not be improved by using excessive information. It has also been observed than simple damage scenarios can be more reliably detected than multiple ones.
Control the springback of metal sheets by using an artificial neural network AIP Conf.Abstract. Structural damage detection is a very important research topic and, currently, there are not specific tools to solve it. A promising tool that can be used is the artificial neural network, ANN, which can deal with hard problems. This paper uses a back propagation ANN with Bayesian regularization training to locate and quantify damage in truss structures. The input parameters corresponded to natural frequencies combined with shape modes, modal flexibilities or modal strain energies. The ANN was trained by considering only simple damage scenarios, random multiple damage scenarios or a combination of them. The results are shown in terms of the percentage of cases in which the ANN trained achieves a determined performance in assessing both the damage extension and the presence of damaged elements. The best performance for the ANN is obtained by using modal strain energies and multiple damage scenarios.
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