Parameter sensitivity and inversion analysis of a concrete faced rock-fill dam based on HS-BPNN algorithm
Parameter sensitivity and inversion analysis of a concrete faced rock-fill dam based on HS-BPNN algorithm作者机构：College of Water-conservancy and Hydropower Hohai University Nanjing 210098 China State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering Hohai University Nanjing 210098 China National Engineering Research Center of Water Resources Efficient Utilization and Engineering Safety Hohai University Nanjing 210098 China Hydrochina Huadong Engineering Corporation Hangzhou 310014 China
出 版 物：《Science China(Technological Sciences)》 (中国科学（技术科学英文版）)
年 卷 期：2016年第59卷第9期
页 面：1442-1451页
核心收录：
学科分类：081504[工学-水利水电工程] 08[工学] 0815[工学-水利工程]
基 金：supported by the National Natural Science Foundation of China(Grant Nos.51579086,51479054,51379068&51139001) Jiangsu Natural Science Foundation(Grant No.BK20140039) the Priority Academic Program Development of Jiangsu Higher Education Institutions(Grant No.YS11001)
主 题：parameter sensitivity analysis harmony search algorithm back propagation neural network parameter inversion concrete faced rock-fill dam
摘 要：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, Rf, and Kb 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.
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