DocumentCode :
2337838
Title :
Damage localization for offshore platform by neural networks
Author :
Diao, Yan Song ; Li, Hua-jun ; Shi, Xiang ; Wang, Shu-Qing
Author_Institution :
Ocean Univ. of China, Qingdao, China
Volume :
8
fYear :
2005
fDate :
18-21 Aug. 2005
Firstpage :
4724
Abstract :
In this paper, a damage localization approach for offshore platform by artificial neural networks is proposed. The members of offshore platform structure are classified and separated into several layers. The decision system for the kind and layer of damaged members is established using the back-propagation networks. When inputting the change rate of normalized frequency into the decision system, the kind and layer of damaged members are determined. The decision system for the location of damaged members is established using the probabilistic neural networks. When inputting the normalized damage-signal index into the decision system, the location of damaged member is determined. Numerical simulations demonstrate that the approach can localize the damage of offshore platform with good robustness.
Keywords :
backpropagation; neural nets; structural engineering computing; artificial neural network; back-propagation network; damage localization; decision system; normalized damage-signal index; offshore platform structure; probabilistic neural network; Acoustic measurements; Acoustic signal detection; Artificial neural networks; Frequency; Inspection; Magnetic separation; Mathematical model; Neural networks; Numerical simulation; Robustness; Damage localization; back-propagation network; offshore platform; probabilistic neural network;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Machine Learning and Cybernetics, 2005. Proceedings of 2005 International Conference on
Conference_Location :
Guangzhou, China
Print_ISBN :
0-7803-9091-1
Type :
conf
DOI :
10.1109/ICMLC.2005.1527773
Filename :
1527773
Link To Document :
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