A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Structural damage detection in the frequency domain using neural networks
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.
Structural damage detection in the frequency domain using neural networks
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.
Structural damage detection in the frequency domain using neural networks
Lee, Jungwhee (author) / Kim, Sungkon (author)
Journal of Intelligent Material Systems and Structures ; 18 ; 785-792
2007
8 Seiten, 24 Quellen
Article (Journal)
English
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