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《Sichuan Building Science》 2014-01
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Study on prediction of tunnel deformation based on optimal neural network

CUI Lingyue;The first Engineering Co. ,Ltd. of China Railway 18th Bureau Group;  
Prediction of neural network has been a contemporary key scientific problem in tunnel academia and engineering. Traditional neural network depends on weight initialization. Different weight initialization may lead to various forecast results. In addition,there are great random and blindness for choice of initial value. It would result in oscillation and non-convergence for network. According to classic superiority of genetic algorithm,initial value of neural network is optimized. Training sample is dealt with and nonlinear model of forest was performed. It would avoid excessive dependence of weight initialization and inducing calculation error. The research results suggest that GA-BP neural network be fit to forecast of deformation of highway tunnel in fault fracture zone. The data prediction is accordable to that of field monitoring. It demonstrates feasibility of engineering application for neural network based on optimization of genetic algorithm. The method could improve prediction precision and avoid personal error. Most of tunnel deformation appears after 12 days from excavation. Monitoring frequency should be strengthened to avoid collapse of tunnel induced by large deformation during construction.
【CateGory Index】: U456.3
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