Application of RBF Neural Network in Dam Deformation Prediction

Abstract:

Dam deformation is a multivariate complicated and nonlinear problem, it’s unable to establish accurate mathematical model.A dam deformation prediction model based on RBF neural network was constructed in this paper to enhance prediction accuracy. Three closely related factors in dam deformation are hydraulic components, temperature component and aging components ,they were selected as Input vector of RBF neural network, dam deformation measured value as a model target output. In Matlab 2011b simulation software,50 groups Fengman dam quantitative observation data from 2012 to 2013 as the sample data,45 groups were used in RBF neural network model training, other 5 groups were used in testing for the model. The simulation result shows that testing value is very close to the true value in this method, the average relative error close to 3%. Effectiveness of the dam deformation prediction based on RBF neural network is Verified by experiments.

• Periodical: Applied Mechanics and Materials (Volumes 675-677)
• Pages: 261-264
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.675-677.261
• Online since: October 2014
• Authors: Guang Hua Xu*
• Keywords: DAM, Displacement, Prediction, RBF Neural Network

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