A Two-Step Structural Damage Detection Approach Based on Wavelet Packet Analysis and Neural Network

2006 ◽  
Author(s):  
Yan-song Diao ◽  
Hua-jun Li ◽  
Yan Wang
Keyword(s):  
Neural Network ◽  
Damage Detection ◽  
Structural Damage ◽  
Wavelet Packet ◽  
Structural Damage Detection ◽  
Wavelet Packet Analysis ◽  
Detection Approach

Structural Damage Identification Using Wavelet Packet Analysis and Neural Network

2006 ◽  
Vol 324-325 ◽  
pp. 205-208
Author(s):  
Qing Guo Fei ◽  
Ai Qun Li ◽  
Chang Qing Miao ◽  
Zhi Jun Li
Keyword(s):  
Neural Network ◽  
Structural Damage ◽  
Damage Identification ◽  
Wavelet Packet ◽  
Uniform Design ◽  
Network Models ◽  
Sampling Technique ◽  
Wavelet Packet Analysis ◽  
Neural Network Models ◽  
Network Training

This paper describes a study on damage identification using wavelet packet analysis and neural networks. The identification procedure could be divided into three steps. First, structure responses are decomposed into wavelet packet components. Then, the component energies are used to define damage feature and to train neural network models. Finally, in combination with the feature of the damaged structure response, the trained models are employed to determine the occurrence, the location and the qualification of the damage. The emphasis of this study is put on multi-damage case. Relevant issues are studied in detail especially the selection of training samples for multi-damage identification oriented neural network training. A frame model is utilized in the simulation cases to study the sampling techniques and the multi-damage identification. Uniform design is determined to be the most suitable sampling technique through simulation results. Identifications of multi-damage cases of the frame including different levels of damage at various locations are investigated. The results show that damages are successfully identified in all cases.

An intelligent structural damage detection approach based on self-powered wireless sensor data

2016 ◽  
Vol 62 ◽  
pp. 24-44 ◽  
Author(s):  
Amir H. Alavi ◽  
Hassene Hasni ◽  
Nizar Lajnef ◽  
Karim Chatti ◽  
Fred Faridazar
Keyword(s):  
Damage Detection ◽  
Structural Damage ◽  
Sensor Data ◽  
Wireless Sensor ◽  
Structural Damage Detection ◽  
Detection Approach ◽  
Self Powered

Structural Damage Detection Using Parameters Combined with Changes in Flexibility Based on BP Neural Networks

2011 ◽  
Vol 243-249 ◽  
pp. 5475-5480
Author(s):  
Zhang Jun
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Damage Detection ◽  
Bp Neural Network ◽  
Structural Damage ◽  
The State ◽  
Structural Damage Detection ◽  
Bp Neural Networks ◽  
Simply Supported ◽  
Modal Flexibility

Modals of BP neural networks with different inputs and outputs are presented for different damage detecting schemes. To identify locations of structural damages, the regular vectors of changes in modal flexibility are looked on as inputs of the networks, and the state of localized damage are as outputs. To identify extents of structural damage, parameters combined with changes in flexibility and the square changes in frequency are as inputs of the networks, and the state of damage extents are as outputs. Examples of a simply supported beam and a plate show that the BP neural network modal can detect damage of structures in quantitative terms.

Structural Damage Detection Using an Iterative Neural Network

2000 ◽  
Vol 11 (1) ◽  
pp. 32-42 ◽  
Author(s):  
C. C. Chang ◽  
T. Y. P. Chang ◽  
Y. G. Xu ◽  
M. L. Wang
Keyword(s):  
Neural Network ◽  
Damage Detection ◽  
Structural Damage ◽  
Structural Damage Detection

Structural Damage Detection using Deep Convolutional Neural Network and Transfer Learning

2019 ◽  
Vol 23 (10) ◽  
pp. 4493-4502 ◽  
Author(s):  
Chuncheng Feng ◽  
Hua Zhang ◽  
Shuang Wang ◽  
Yonglong Li ◽  
Haoran Wang ◽  
...  
Keyword(s):  
Neural Network ◽  
Damage Detection ◽  
Convolutional Neural Network ◽  
Transfer Learning ◽  
Structural Damage ◽  
Deep Convolutional Neural Network ◽  
Structural Damage Detection

A structural damage detection approach using train-bridge interaction analysis and soft computing methods

2014 ◽  
Vol 13 (5) ◽  
pp. 869-890 ◽  
Author(s):  
Xingwen He ◽  
Mitsuo Kawatani ◽  
Toshiro Hayashikawa ◽  
Chul-Woo Kim ◽  
F. Necati Catbas ◽  
...  
Keyword(s):  
Damage Detection ◽  
Soft Computing ◽  
Structural Damage ◽  
Interaction Analysis ◽  
Computing Methods ◽  
Structural Damage Detection ◽  
Detection Approach ◽  
Soft Computing Methods
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