Research on the Application of Radial Basis Function Neural Network to Structural Reliability Analysis of Gravity Dam

Abstract:

The performance function tends to be implicit or nonlinear in the evaluation of gravity dam reliability, making it difficult to apply some classical methods, such as JC method, Monte-Carlo simulation etc., as they are supposed to be too time-consuming. One possible solution to this problem may be the introduction of artificial neural networks, among which RBF is featured with faster convergence, better precision and can realize global convergence to some extent. In this paper, the application of RBF in gravity dam reliability is investigated, with some examples presented to convince that its reasonable to put it into use.

• Periodical: Applied Mechanics and Materials (Volumes 496-500)
• Pages: 2505-2510
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.496-500.2505
• Online since: January 2014
• Authors: Yun Ji, Xiao Qing Liu, Tong Chun Li, Shuo Li
• Keywords: Gravity Dam, Radial Basis Function (RBF), Structural Reliability

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