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

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.

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

Automation in Construction ◽

10.1016/j.autcon.2015.10.001 ◽

2016 ◽

Vol 62 ◽

pp. 24-44 ◽

Cited By ~ 58

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

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A hierarchical deep convolutional neural network and gated recurrent unit framework for structural damage detection

Information Sciences ◽

10.1016/j.ins.2020.05.090 ◽

2020 ◽

Vol 540 ◽

pp. 117-130 ◽

Cited By ~ 1

Author(s):

Jianxi Yang ◽

Likai Zhang ◽

Cen Chen ◽

Yangfan Li ◽

Ren Li ◽

...

Keyword(s):

Neural Network ◽

Damage Detection ◽

Convolutional Neural Network ◽

Structural Damage ◽

Deep Convolutional Neural Network ◽

Structural Damage Detection ◽

Gated Recurrent Unit

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Structural Damage Detection by Integrating Data Fusion and Probabilistic Neural Network

Advances in Structural Engineering ◽

10.1260/136943306778812787 ◽

2006 ◽

Vol 9 (4) ◽

pp. 445-458 ◽

Cited By ~ 28

Author(s):

Shao-Fei Jiang ◽

Chun-Ming Zhang ◽

C. G. Koh

Keyword(s):

Neural Network ◽

Data Fusion ◽

Damage Detection ◽

Structural Damage ◽

Probabilistic Neural Network ◽

Structural Damage Detection

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

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.243-249.5475 ◽

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.

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