• DocumentCode
    1882200
  • Title

    The Prediction of Mechanical Properties of Cement Soil Based on PSO-SVM

  • Author

    Wang, Jiehao ; Xing, Yan ; Cheng, Liuyong ; Qin, Feihu ; Ma, Tianran

  • Author_Institution
    Sch. of Mech. & Civil Eng., China Univ. of Min. & Technol., Xuzhou, China
  • fYear
    2010
  • fDate
    10-12 Dec. 2010
  • Firstpage
    1
  • Lastpage
    4
  • Abstract
    The analysis of mechanical properties of cement soil is the problem concerned greatly by the design-constructors. This paper uses SVM to establish the nonlinear relation between the parameters and unconfined compressive strength of cement soil. At the same time, this paper uses PSO to get the global optimization of SVM, which avoids the blindness of artificial selection and improves the prediction accuracy of model. We apply PSO-SVM to the prediction of unconfined compressive strength, and then make a comparison with traditional BP-NN. The result shows that the prediction accuracy of PSO-SVM is much higher than BP-NN. Therefore, it is feasible and efficiency using PSO-SVM to predict the unconfined compressive strength of cement soil.
  • Keywords
    cements (building materials); compressive strength; mechanical engineering computing; particle swarm optimisation; soil; support vector machines; BP-NN; PSO-SVM; backpropagation neural network; cement soil mechanical properties; compressive strength; particle swarm optimisation; support vector machine; Accuracy; Artificial neural networks; Kernel; Mechanical factors; Soil; Support vector machines; Training;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Computational Intelligence and Software Engineering (CiSE), 2010 International Conference on
  • Conference_Location
    Wuhan
  • Print_ISBN
    978-1-4244-5391-7
  • Electronic_ISBN
    978-1-4244-5392-4
  • Type

    conf

  • DOI
    10.1109/CISE.2010.5677256
  • Filename
    5677256