Parameter sensitivity and inversion analysis of a concrete faced rock-fill dam based on HS-BPNN algorithm

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• 作者：PengMing Sun ; TengFei Bao ; ChongShi Gu ; Ming Jiang…
• 关键词：parameter sensitivity analysis ; harmony search algorithm ; back propagation neural network ; parameter inversion ; concrete faced rock ; fill dam
• 刊名：SCIENCE CHINA Technological Sciences
• 出版年：2016
• 出版时间：September 2016
• 年：2016
• 卷：59
• 期：9
• 页码：1442-1451
• 全文大小：670 KB
• 刊物类别：Engineering
• 刊物主题：Chinese Library of Science
  Engineering, general
  
• 出版者：Science China Press, co-published with Springer
• ISSN：1869-1900
• 卷排序：59

文摘

Considering the complex nonlinear relationship between the material parameters of a concrete faced rock-fill dam (CFRD) and its displacements, the harmony search (HS) algorithm is used to optimize the back propagation neural network (BPNN), and the HS-BPNN algorithm is formed and applied for the inversion analysis of the parameters of rock-fill materials. The sensitivity of the parameters in the Duncan and Chang’s E-B model is analyzed using the orthogonal test design. The case study shows that the parameters φ₀, K, R_f, and K_b are sensitive to the deformation of the rock-fill dam and the inversion analysis for these parameters is performed by the HS-BPNN algorithm. Compared with the traditional BPNN, the HS-BPNN algorithm exhibits the advantages of high convergence precision, fast convergence rate, and strong stability.