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《Journal of Vibration and Shock》 2007-01
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DAMAGE MONITORING FOR WOVEN COMPOSITE STRUCTURE BASED ON RADIAL BASE FUNCTION NEURAL NETWORK

Liu Chaoyong~1,Zheng Shijie~1,Wang Xiaoxue~2(1.Aeronautical Science Key Lab of Smart Matterials & Structures,Nanjing University of Aeronautics & Astronautics,Nanjing 2100162.Department of Building Engineering,Datong Vocational Technology College,Datong 037008)  
Firstly,due to demerits of BP neural network,such as low convergence speed and local minimum and lack of training samples,a new method for woven composite structure,using RBF(Radial Base Function) neural network,is presented,based on computational mechanics.Secondly,two SW210 fiber glass cloth reinforced composite beams are fabricated,and their modal frequencies are measured by LMS CADA-X modal analysis and test system.Thirdly,the first five flexural modal frequencies of the FEM model with four hundred and fifty-one different conditions obtained by FEM,are modified and normalized by the method given here.Then they are used to train a RBF neural network.Finally,the first five flexural experimental modal frequencies normalized by the same method mentioned above,are input to the neural network to predict the delamination location and its damage level.The results show that the method proposed is feasible,satisfactory and promising.
【Fund】: 国家自然科学基金重点项目50135030资助
【CateGory Index】: TB332
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