A probabilistic damage identification approach for structures with uncertainties under unknown input

被引:44
|
作者
Zhang, Kun [1 ]
Li, Hui [1 ]
Duan, Zhongdong [1 ]
Law, S. S. [2 ]
机构
[1] Harbin Inst Technol, Sch Civil Engn, Harbin 150090, Peoples R China
[2] Hong Kong Polytech Univ, Dept Civil & Struct Engn, Kowloon, Hong Kong, Peoples R China
关键词
Damage identification; Input identification; Dynamic response sensitivity; Uncertainties; Probabilistic method; GENETIC FUZZY SYSTEM; TIME HISTORY; PARAMETERS; DOMAIN;
D O I
10.1016/j.ymssp.2010.10.017
中图分类号
TH [机械、仪表工业];
学科分类号
0802 ;
摘要
To avoid the false positives of damages in the deterministic identification method induced by uncertainties in measurement noise, a probabilistic method is proposed to identify damages of the structures with uncertainties under unknown input. The proposed probabilistic method is developed from a deterministic simultaneous identification method of structural physical parameters and input based on dynamic response sensitivity. The deterministic simultaneous identification method is first derived. The effect of uncertainties caused by measurement noise on the identified parameters is then investigated. The statistical parameters of the identified structural parameters are calculated. The damage index is derived from the statistical parameters of the physical parameters of intact and damaged structure. The probability of identified damage, defined as the probability of identified structural stiffness smaller than that of intact structure, is further derived using the probability method. A twelve-story building and a nine-bay three-dimensional frame structure are, respectively, analyzed numerically and experimentally using the proposed method. The research results indicate that the probabilistic simultaneous identification method for damage and input can decrease the false positives of damages in contrast with the deterministic method under intensive measurement noise, and it can also achieve an accurate identification for structural unknown input. (C) 2010 Elsevier Ltd. All rights reserved.
引用
收藏
页码:1126 / 1145
页数:20
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