• DocumentCode
    3365906
  • Title

    Development of deformation prediction system of large bridge based on MATLAB and radial basis function neural network

  • Author

    Tian, Linya ; Wang, Gang ; Yu, Xiaotao

  • Author_Institution
    Coll. of Geosci. & Eng., Hohai Univ., Nanjing, China
  • fYear
    2010
  • fDate
    26-28 June 2010
  • Firstpage
    4613
  • Lastpage
    4616
  • Abstract
    Affected by many factors, deformation of large bridge will take place in the course of usage, so the development of deformation prediction system will contribute to the maintenance and management of large bridge. As the multiple-input and multiple-output characteristic of the RBF neural network, which also has the nature of universal approximation and optimal approximation, MATLAB combined with RBF network was choosed to develope the deformation prediction system of large bridge. The improved algorithm of RBF network parameters was studied, some key issues in the system development were solved, the choice of input factors and the preprocessing method of sampled data were discussed, the main function of the system was elaborated. This system has some popularization and application values to the deformation prediction and safety supervising of other similar projects.
  • Keywords
    bridges (structures); condition monitoring; deformation; mathematics computing; radial basis function networks; structural engineering computing; MATLAB; deformation prediction system development; large bridge; optimal approximation; radial basis function neural network; sampled data preprocessing method; universal approximation; Bridges; Data preprocessing; Educational institutions; Electronic mail; Engineering management; Geology; MATLAB; Neural networks; Radial basis function networks; Safety; bridge; deformation predication system; development; matlab; radial basis function neural network;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Mechanic Automation and Control Engineering (MACE), 2010 International Conference on
  • Conference_Location
    Wuhan
  • Print_ISBN
    978-1-4244-7737-1
  • Type

    conf

  • DOI
    10.1109/MACE.2010.5536600
  • Filename
    5536600