• DocumentCode
    624620
  • Title

    Application of optimization model based on neural network in Softening Slope Stability by Strong Rainfall Infiltration

  • Author

    Zhigang Yang ; Dong Zhang ; Biao Deng ; Weimin Chen

  • Author_Institution
    Sch. of Archit. Eng., Nanchang Univ., Nanchang, China
  • fYear
    2013
  • fDate
    9-11 June 2013
  • Firstpage
    289
  • Lastpage
    292
  • Abstract
    Through analyzing main affecting factors of the artificial neural network model, the optimization model is established and the optimization parameters is obtain based on the method of momentum and self-adaptive of learn rate, This optimize artificial neural network is not only set up with the limited training samples, but also can improve the operating speed and study efficiency. The optimization mode of the of flood prediction of Slope Stability is build by Bedrock Softening under the Condition of Strong Rainfall Infiltration, it shows that its precision is high, and its computation is simple. The method by optimization neural network, which applied to bedrock strength decreases forecasting under the Condition of Strong Rainfall Infiltration, provides a new attempt for Prediction analysis and prove to be feasible and effective for practical experience in complex system engineering of bedrock Slope Stability.
  • Keywords
    floods; forecasting theory; geophysics computing; geotechnical engineering; learning (artificial intelligence); mechanical stability; neural nets; optimisation; rain; structural engineering computing; artificial neural network model; bedrock slope stability softening; bedrock strength; complex system engineering; flood prediction analysis; learn rate momentum method; optimization model; optimization parameters; self-adaptive learn rate; strong rainfall infiltration; Analytical models; Artificial neural networks; Biological neural networks; Rocks; Safety; Stability analysis; Training;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Intelligent Control and Information Processing (ICICIP), 2013 Fourth International Conference on
  • Conference_Location
    Beijing
  • Print_ISBN
    978-1-4673-6248-1
  • Type

    conf

  • DOI
    10.1109/ICICIP.2013.6568084
  • Filename
    6568084