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- W4280492474 abstract "Assessing and localizing damages are an important problem in structural engineering. Artificial neural networks (ANN) have an excellent pattern recognition capability. In this paper, a structural anomaly diagnosis method based on ANN model using displacement response signals is proposed to assess and localize damages, and applied to a five-story frame structure under random base excitation. The random displacement responses are used as the input to ANN for detecting structural damages, which differ from conventional methods such as using modal parameters extracted from responses. The ANN model is set up by training and then validation using random displacement responses. Damages in a structure are denoted by stiffness degradation. Detection results are mainly affected by incomplete measurement due to intensive noise, finite sampling time length and measured degree of freedom (DOF). Numerical results show the effects of the incomplete measurement on the accuracy of predicting damages based on the proposed method." @default.
- W4280492474 created "2022-05-22" @default.
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- W4280492474 date "2022-05-16" @default.
- W4280492474 modified "2023-10-18" @default.
- W4280492474 title "Effectiveness analysis of structural anomaly diagnosis based on ANN model" @default.
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- W4280492474 doi "https://doi.org/10.21595/vp.2022.22446" @default.
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