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Detection of Structural Damage Through Changes in Frequency
作者姓名:ZHU  Hong-ping  HE  Bo  CHEN  Xiao-qiang
作者单位:School of Civil Engineering and Mechanics, Huazhong University of Science and Technology, Wuhan 430074, Hubei, China
基金项目:Supported by the National Natural Science Foundation of China (No. 50378041) and the Specialized Research Fund for the Doctoral Program of Higher Education (Grant No. 20030487016).
摘    要:0IntroductionThbeee nvi pbrroatpioosne-db aasnedd d deavemlaogpeed d iente tchtieo npa satp p3r0o aycehaerss , haanvdethe study onthis topicis still active1].Since natural frequen-cies can provide the global information of structures ,and canbe exactly m…

关 键 词:损伤检测  频率变换  灵敏度分析  人工网络  鲁棒性
文章编号:1007-1202(2005)06-1069-05
收稿时间:2004-12-20

Detection of structural damage through changes in frequency
ZHU Hong-ping HE Bo CHEN Xiao-qiang.Detection of Structural Damage Through Changes in Frequency[J].Wuhan University Journal of Natural Sciences,2005,10(6):1069-1073.
Authors:Zhu Hong-ping  He Bo  Chen Xiao-qiang
Institution:(1) School fo Civil Engineering and Mechanics, Huazhong University of Science and Technology, 430074 Wuhan, Hubei, China
Abstract:Among all the structural vibration characteristics, natural frequencies are relatively simple and accurate to measure, and provide the structural global damage informalion. In this paper, the feasibility of using only natural frequencies to identify structural damage is exploited by adopting two usual approaches, namely, sensitivity analysis and neural networks. S, ome aspects of damage detection such as the problem of incomplete modal test data and robustness of detection are considered. A laboratory tested 3 storey frame is used to demonstrate the possibility of frequency-based damage detection techniques. The numerical results show that the damaged element can be correctly localized and the content of damage can be identified with relatively high degree of accuracy by using the changes in frequencies.
Keywords:damage detection  changes in frequency  sensitivity analysis  neural network
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