• DocumentCode
    1896173
  • Title

    Artificial Neural Network´s Application in Intelligent Prediction of Surface Settlement Induced by Foundation Pit Excavation

  • Author

    Yu Jun ; Chen Haiming

  • Author_Institution
    Sch. of Civil Eng. & Archit., Central Sounth Univ., Changsha, China
  • Volume
    1
  • fYear
    2009
  • fDate
    10-11 Oct. 2009
  • Firstpage
    303
  • Lastpage
    305
  • Abstract
    Surface settlement will take place while foundation pit building and it is one of important problems in foundation pit engineering. The influencing factors are complex and there are stochastic and fuzzy properties in them, which have brought difficulties to the ground settlement predication. The traditional methods are inadequate when they are used to analyze and predict the surface settlement. In this paper, the authors established an intelligent ground settlement analysis method and solved these problems. Practices show that the accuracy of the method can meet the application requirements, with some merits, such as, high efficiency, use of convenience, suitable for engineering applications, and so on, so the research results have certain engineering and technical value.
  • Keywords
    excavators; foundations; geotechnical engineering; neural nets; artificial neural network; foundation pit building; foundation pit engineering; foundation pit excavation; intelligent ground settlement analysis; intelligent prediction; surface settlement; Artificial intelligence; Artificial neural networks; Civil engineering; Computer architecture; Educational technology; Electronic mail; Intelligent networks; Intelligent structures; Intelligent systems; Surface fitting; artificial neural network; foundation pit; ground settlement; intelligent analysis;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Intelligent Computation Technology and Automation, 2009. ICICTA '09. Second International Conference on
  • Conference_Location
    Changsha, Hunan
  • Print_ISBN
    978-0-7695-3804-4
  • Type

    conf

  • DOI
    10.1109/ICICTA.2009.80
  • Filename
    5287650