標題: Unsupervised fuzzy neural networks for damage detection of structures
作者: Wen, C. M.
Hung, S. L.
Huang, C. S.
Jan, J. C.
土木工程學系
Department of Civil Engineering
關鍵字: unsupervised fuzzy neural network;damage detection;BPN
公開日期: 1-二月-2007
摘要: This work presents an artificial neural network (ANN) approach for detecting structural damage. In place of the commonly used supervised neural network, this work adopts an unsupervised neural network which incorporates the fuzzy concept (named the unsupervised fuzzy neural network, UFN) to detect localized damage. The structural damage is assumed to take the form of reduced elemental stiffness. The damage site is demonstrated to correlate with the changes in the modal parameters of the structure. Therefore, a feature representing the damage location, termed the damage localization feature (DLF) is presented. When the structure experiences damage or change in the structural member, the measured DLF is obtained by analyzing the recorded dynamic responses of the structure. The location of the structural damage then can be identified using the UFN according to the measured DLF information. This study verifies the proposed model using an example involving a five-storey frame building. Both single- and multiple-dam aged sites are considered. The effects of measured noise and the use of incomplete modal data are introduced to inspect the capability of the proposed detection approach. Additionally, the simulation results of well-known back-propagation network (BPN) and UFN are compared. The analysis results indicated that the use of fuzzy relationship in UFN made the detection of structural damage more robust and flexible than the BPN. Copyright (c) 2005 John Wiley & Sons, Ltd.
URI: http://dx.doi.org/10.1002/stc.116
http://hdl.handle.net/11536/11185
ISSN: 1545-2255
DOI: 10.1002/stc.116
期刊: STRUCTURAL CONTROL & HEALTH MONITORING
Volume: 14
Issue: 1
起始頁: 144
結束頁: 161
顯示於類別:期刊論文


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