• DocumentCode
    3303530
  • Title

    SVM in Predicting the Deformation of Deep Foundation Pit in Soft Soil Area

  • Author

    Sun, Fuxue

  • Author_Institution
    Coll. of Archit. & Civil Eng., Wenzhou Univ., Wenzhou, China
  • fYear
    2010
  • fDate
    24-25 April 2010
  • Firstpage
    761
  • Lastpage
    763
  • Abstract
    Based on the measured deformation data series, future deformation value of deep foundation pit was predicted using Support Vector Machine (SVM) model in soft soil area. Gauss kernel function, Sequential minimal optimization arithmetic, and the parameter value of C andε were determined by testing. By using the method in example, results are shown to be in good agreement with measured data and laws reported in paper, and illustrates that SVM could perform well in solving fuzzy geotechnical engineering problem similar to deformation prediction. As another act, the method and conclusion can be considered as reference for colleagues.
  • Keywords
    Area measurement; Arithmetic; Deformable models; Gaussian processes; Kernel; Performance evaluation; Predictive models; Sequential analysis; Soil measurements; Support vector machines; deep foundation pit; deformation prediction; soft soil area; support vector machine;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Machine Vision and Human-Machine Interface (MVHI), 2010 International Conference on
  • Conference_Location
    Kaifeng, China
  • Print_ISBN
    978-1-4244-6595-8
  • Electronic_ISBN
    978-1-4244-6596-5
  • Type

    conf

  • DOI
    10.1109/MVHI.2010.167
  • Filename
    5532473