Abstract
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 Kbare 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.
Original language | English |
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Pages (from-to) | 1442-1451 |
Number of pages | 10 |
Journal | Science China Technological Sciences |
Volume | 59 |
Issue number | 9 |
DOIs | |
Publication status | Published - 1 Sept 2016 |
Externally published | Yes |
Keywords
- back propagation neural network
- concrete faced rock-fill dam
- harmony search algorithm
- parameter inversion
- parameter sensitivity analysis
ASJC Scopus subject areas
- General Materials Science
- General Engineering