• DocumentCode
    2074815
  • Title

    Prediction method for the deformation of deep foundation pit based on neural network algorithm optimized by particle swarm

  • Author

    Tan, Guojin ; Liu, Hanbing ; Cheng, Yongchun ; Liu, Bin ; Zhang, Yin

  • Author_Institution
    Coll. of Transp., Jilin Univ., Changchun, China
  • fYear
    2011
  • fDate
    16-18 Dec. 2011
  • Firstpage
    1407
  • Lastpage
    1410
  • Abstract
    Prediction of the deformation is an important means of the construction parameter adjustment and the construction safety of deep foundation pit. Combining particle swarm optimization with neural network algorithm, The prediction method for the deformation of deep foundation pit based on neural network algorithm optimized by particle swarm is established. In order to improve the prediction accuracy and prediction efficiency of the neural network algorithm, The initial weights and the initial threshold value of neural network model are optimized by using particle swarm optimization. In the neural network model, the deformation of foundation pit is predicted by the optimized initial weights and the optimized initial threshold value. Relying on the practical engineering, the effectiveness and practicality of the method proposed in this paper are verified.
  • Keywords
    deformation; foundations; neural nets; particle swarm optimisation; structural engineering computing; construction parameter adjustment; construction safety; deep foundation pit; deformation prediction method; initial threshold value; initial weight; neural network algorithm; particle swarm optimization; prediction accuracy; prediction efficiency; Deformable models; Monitoring; Optimization; Particle swarm optimization; Prediction algorithms; Predictive models; Training; deformation prediction; foundation pit; neural network; particle swarm optimization;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Transportation, Mechanical, and Electrical Engineering (TMEE), 2011 International Conference on
  • Conference_Location
    Changchun
  • Print_ISBN
    978-1-4577-1700-0
  • Type

    conf

  • DOI
    10.1109/TMEE.2011.6199470
  • Filename
    6199470