• DocumentCode
    2999015
  • Title

    Using Neural Network to Determine Rockfill Parameters of Duncan E-B Model

  • Author

    Yan, Zhang ; Qiao, Yan ; Xia, Yang ; Chang-bin, Wu

  • Author_Institution
    Hydropower & Environ. Coll., China Three Gorges Univ. CTGU, Yichang, China
  • fYear
    2010
  • fDate
    25-27 June 2010
  • Firstpage
    2502
  • Lastpage
    2506
  • Abstract
    Duncan E-B model has been widely used in engineering practice. And how to determine the parameters of the model accurately and easily is the main step to ensure the reliability of the results. Based on BP neural network in MATLAB toolbox, this paper aim to establish the non-linear relation between the density γ, porosity e and the constitutive model parameter K (Kb, Rf, Q0) which is difficult to be determined, and through the model above, forecast the parameters of E-B model for one certain project. And the results meet the project accuracy requirements, Which provides a new method to determine the parameters of constitutive model.
  • Keywords
    backpropagation; dams; geotechnical engineering; neural nets; reliability; rocks; structural engineering computing; BP neural network; Duncan E-B model; MATLAB toolbox; concrete face rockfill dam construction; rockfill parameters; Analytical models; Artificial neural networks; Mathematical model; Predictive models; Strain; Stress; Training; Duncan E-B model; MATLAB; forecast; neural network; parameters of model;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Electrical and Control Engineering (ICECE), 2010 International Conference on
  • Conference_Location
    Wuhan
  • Print_ISBN
    978-1-4244-6880-5
  • Type

    conf

  • DOI
    10.1109/iCECE.2010.619
  • Filename
    5630824