Structural damage detection in the frequency domain using neural networks

被引:61
作者
Lee, Jungwhee
Kim, Sungkon
机构
[1] RIST, Steel Struct Res Lab, Gyeonggi 445813, South Korea
[2] Seoul Natl Univ Technol, Dept Struct Engn, Seoul 139743, South Korea
关键词
damage detection; frequency response function (FRF); strain frequency response function (SFRF); signal anomaly index (SAI); pattern recognition; neural network (NN);
D O I
10.1177/1045389X06073640
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
A bi-level damage detection algorithm that utilizes dynamic responses of the structure as input and neural network (NN) as a pattern classifier is presented. The signal anomaly index (SAI) is proposed to express the amount of changes in the shape of frequency response functions (FRFs) or strain frequency response function (SFRF). SAI is calculated by using the acceleration and dynamic strain responses acquired from intact and damaged states of the structure. In a bi-level damage identification algorithm, first the presence of damage is identified from the magnitude of the SAI value. Then the location of the damage is identified using the pattern recognition capability of the NN. The proposed algorithm is applied to an experimental model bridge to demonstrate the feasibility of the algorithm. Numerically simulated signals are used for training the NN, and experimentally acquired signals are used to test the NN. The results of this example application suggest that the SAI based pattern recognition approach may be applied to the structural health monitoring system for a real bridge.
引用
收藏
页码:785 / 792
页数:8
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