Structural damage alarming using auto-associative neural network technique: Exploration of environment-tolerant capacity and setup of alarming threshold

2011 ◽  
Vol 25 (5) ◽  
pp. 1508-1526 ◽  
Author(s):  
H.F. Zhou ◽  
Y.Q. Ni ◽  
J.M. Ko
Keyword(s):  
Neural Network ◽  
Structural Damage ◽  
Damage Alarming ◽  
Auto Associative Neural Network

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.

scholarly journals Identification of Crack Length and Angle at the Center Weld Seam of Offshore Platforms Using a Neural Network Approach

2020 ◽  
Vol 8 (1) ◽  
pp. 40 ◽  
Author(s):  
Qingxi Yang ◽  
Gongbo Li ◽  
Weilei Mu ◽  
Guijie Liu ◽  
Hailiang Sun
Keyword(s):  
Neural Network ◽  
Crack Length ◽  
Structural Damage ◽  
Evaluation Method ◽  
Weld Seam ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Offshore Platforms ◽  
Neural Network Approach ◽  
External Interference

The reconstruction algorithm for the probabilistic inspection of damage (RAPID) is aimed at localizing structural damage via the signal difference coefficient (SDC) between the signals of the present and reference conditions. However, tomography is only capable of presenting the approximate location and not the length and angle of defects. Therefore, a new quantitative evaluation method called the multiple back propagation neural network (Multi-BPNN) is proposed in this work. The Multi-BPNN employs SDC values as input variables and outputs the predicted length and angle, with each output node depending on an individual hidden layer. The cracks of different lengths and angles at the center weld seam of offshore platforms are simulated numerically. The SDC values of the simulations and experiments were normalized for each sample to eliminate external interference in the experiments. Then, the normalized simulation data were employed to train the proposed neural network. The results of the simulations and experimental verification indicated that the Multi-BPNN can effectively predict crack length and angle, and has better stability and generalization capacity than the multi-input to multi-output back propagation neural network.

Influence of Input Parameters on the Performance of an Artificial Neural Network Used to Detect Structural Damage

2010 ◽  
Author(s):  
Jesus Daniel Villalba ◽  
Ivan Dario Gomez ◽  
Jose Elias Laier ◽  
Theodore E. Simos ◽  
George Psihoyios ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Structural Damage ◽  
Input Parameters ◽  
Artificial Neural

scholarly journals Construction of an Artificial Neural Network-Based Method to Detect Structural Damage

2020 ◽  
Author(s):  
Francisco Casanova-del-Angel ◽  
Daniel Hernández-Galicia ◽  
Xochicale-Rojas Hugo Alberto
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Structural Damage ◽  
Artificial Neural
Sign in / Sign up

Export Citation Format

Share Document