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

Peng Ming Sun, Teng Fei Bao, Chong Shi Gu, Ming Jiang, Tian Wang, Wen Zhong Shi

Research output: Journal article publicationJournal articleAcademic researchpeer-review

21 Citations (Scopus)


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 languageEnglish
Pages (from-to)1442-1451
Number of pages10
JournalScience China Technological Sciences
Issue number9
Publication statusPublished - 1 Sep 2016
Externally publishedYes


  • back propagation neural network
  • concrete faced rock-fill dam
  • harmony search algorithm
  • parameter inversion
  • parameter sensitivity analysis

ASJC Scopus subject areas

  • Materials Science(all)
  • Engineering(all)


Dive into the research topics of 'Parameter sensitivity and inversion analysis of a concrete faced rock-fill dam based on HS-BPNN algorithm'. Together they form a unique fingerprint.

Cite this