A method using the support vector machine (SVM) to detect local damages in a building structure with the limited number of sensors is proposed. The SVM is a powerful pattern recognition tool applicable to complicated classification problems. The method is verified to have capability to identify not only the location of damage but also the magnitude of damage with satisfactory accuracy. In our proposed method, feature vectors derived from the modal frequency patterns are used. The feature vectors contain the information on the location and magnitude of damages. As the method does not require modal shapes, typically only two vibration sensors are enough for detecting input and output signals to obtain the modal frequencies. The support vector machines trained for single damage is also effective for detecting damage in multiple stories.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Quantitative damage diagnosis of shear structures using support vector machine


    Weitere Titelangaben:

    KSCE J Civ Eng


    Beteiligte:
    Mita, Akira (Autor:in) / Hagiwara, Hiromi (Autor:in)

    Erschienen in:

    Erscheinungsdatum :

    01.11.2003


    Format / Umfang :

    7 pages




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch





    Railway Turnout Fault Diagnosis Based on Support Vector Machine

    He, You Min ;Zhao, Hui Bing ;Tian, Jian | Trans Tech Publications | 2014


    Application of Support Vector Machine in Transformer Fault Diagnosis

    Xiaohui, W. / Jiong, L. / Yongchun, L. | British Library Online Contents | 2007


    Fault Diagnosis of Engine Based on Support Vector Machine

    Hao, T. / Liangsheng, Q. | British Library Online Contents | 2007