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• W2767375627 abstract "Recently much research has been conducted towards finding fast and accurate pattern classifiers applied to Structural Health Monitoring (SHM) systems. In this way, researchers have proposed new methods based on Fuzzy ARTMAP Network (FAN) in order to enhance the success rate for structural damage classification applied to SHM applications. Conversely, the performance of methods based on FAN is very dependent of its setup parameters. In several SHM approaches in the literature, authors have proposed selecting those parameters by using several attempts (empirical and manual selection) and keeping them fixed for all cases in the resulting analysis, hampering the success rate of the neural network. To overcoming that, this paper introduces a new strategy for enhancement of structural damage identification focusing on supervised learning of FAN by using Particle Swarm Optimization (PSO) for selecting optimal setup parameters automatically for the FAN algorithm. Also, the Kappa coefficient is used as an objective function to be maximized through the PSO algorithm. As a result, the optimum setup parameters improved the success rate while the damage identification is being carried out. Indeed this proposed method is certainly very promising and constitutes a novelty. The proposed method achieves more than 75% hit rate that is significantly higher than the state-of-the-art approaches as presented in this paper. Furthermore, this approach yields a 20% improvement when considering the worst case scenario. Hence, this approach shows a practical application of expert and intelligent systems applied to damage identification in SHM systems. To conclude, the proposed approach successfully identifies structural damage with accuracy and efficiency." @default.
• W2767375627 created "2017-11-17" @default.
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• W2767375627 date "2018-04-01" @default.
• W2767375627 modified "2023-10-18" @default.
• W2767375627 title "Kappa-PSO-FAN based method for damage identification on composite structural health monitoring" @default.
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• W2767375627 doi "https://doi.org/10.1016/j.eswa.2017.11.022" @default.
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