An artificial neural network-based response surface method for reliability analyses of c-φ slopes with spatially variable soil

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• 作者：Su-xun Shu 舒苏荀 ; Wen-hui Gong 龚文惠
• 关键词：slope reliability ; spatial variability ; artificial neural network ; Latin hypercube sampling ; random finite element method
• 刊名：China Ocean Engineering
• 出版年：2016
• 出版时间：March 2016
• 年：2016
• 卷：30
• 期：1
• 页码：113-122
• 全文大小：514 KB
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• 作者单位：Su-xun Shu 舒苏荀 (1)
  Wen-hui Gong 龚文惠 (1)
  
  1. School of Civil Engineering and Mechanics, Huazhong University of Science and Technology, Wuhan, 430074, China
  
• 刊物类别：Engineering
• 刊物主题：Oceanography
  Chinese Library of Science
  Offshore Engineering
  Geoengineering, Foundations, Hydraulics
  
• 出版者：Chinese Ocean Engineering Society, co-published with Springer

文摘

This paper presents an artificial neural network (ANN)-based response surface method that can be used to predict the failure probability of c-φ slopes with spatially variable soil. In this method, the Latin hypercube sampling technique is adopted to generate input datasets for establishing an ANN model; the random finite element method is then utilized to calculate the corresponding output datasets considering the spatial variability of soil properties; and finally, an ANN model is trained to construct the response surface of failure probability and obtain an approximate function that incorporates the relevant variables. The results of the illustrated example indicate that the proposed method provides credible and accurate estimations of failure probability. As a result, the obtained approximate function can be used as an alternative to the specific analysis process in c-φ slope reliability analyses. Key words slope reliability spatial variability artificial neural network Latin hypercube sampling random finite element method