• DocumentCode
    483322
  • Title

    Study on Parameter Distribution in Structure Reliability Analysis: Machine Learning Algorithm and Application

  • Author

    Wan, Yi ; Zhang, Yangu

  • Author_Institution
    Coll. of Comput. Sci. & Eng., Wenzhou Univ. Wenzhou, Wenzhou
  • fYear
    2009
  • fDate
    23-25 Jan. 2009
  • Firstpage
    833
  • Lastpage
    836
  • Abstract
    The discrimination of parameter probability distribution type is the key to structure reliability analysis. A support vector machine (SVM) intelligent recognition model of probability distribution law is presented aiming at traditional method disadvantage. The intelligent recognition model of probability distribution is constructed by SVM algorithm realization, network design and feature extraction, inward stress probability distribution type of a stem structural member is recognized by the model, recognition result is Weibull distribution, SVM has a good generalization ability and clustering ability by comparison between network recognition result and regression analysis, the experiment result shows total recognition rate achieved 98.25%, it provides a good new method for structure reliability analysis.
  • Keywords
    feature extraction; generalisation (artificial intelligence); reliability; statistical distributions; structural engineering computing; support vector machines; Weibull distribution; feature extraction; intelligent recognition model; inward stress probability distribution type; machine learning algorithm; parameter distribution; probability distribution law; stem structural member; structure reliability analysis; support vector machine; Algorithm design and analysis; Clustering algorithms; Feature extraction; Intelligent networks; Intelligent structures; Learning systems; Machine learning algorithms; Probability distribution; Stress; Support vector machines; SVM; intelligent recognition model; probability distribution law; structure reliability;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Knowledge Discovery and Data Mining, 2009. WKDD 2009. Second International Workshop on
  • Conference_Location
    Moscow
  • Print_ISBN
    978-0-7695-3543-2
  • Type

    conf

  • DOI
    10.1109/WKDD.2009.169
  • Filename
    4772064