In order to overcome the disadvantages of high computational complexity and inconvenience when forecasting deformations of surrounding rock by using support vector machine of standard form (Vapnik SVM), a new deformation prediction method based on least squares support vector machine (LS-SVM) was presented. By using this method, the excavated rock mass was regarded as a time-dependent system with high uncertainty and a sliding time window was employed at first to select learning examples, then the examples obtained was used for training the corresponding LS-SVM prediction model. Finally the proposed method was applied to forecast the surrounding rock deformations of Xuejiazhuang Tunnel. The result shows that the method has relatively high prediction accuracy and therefore it is a feasible deformation prediction method with low computational complexity.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Predicting Deformations of Tunnel Surrounding Rock by Using Least Squares Support Vector Machine


    Beteiligte:
    Li, Xiaolong (Autor:in) / Wang, Fuming (Autor:in) / Cai, Yingchun (Autor:in)


    Erscheinungsdatum :

    15.02.2011


    Format / Umfang :

    62011-01-01 pages




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Sparse Least Squares Support Vector Machine and Its Applications

    Song, H.-y. / Gui, W.-h. / Yang, C.-h. | British Library Online Contents | 2008


    Traffic forecasting using least squares support vector machines

    Zhang, Yang / Liu, Yuncai | Taylor & Francis Verlag | 2009


    Double Mode Control Based on Least Squares Support Vector Machine

    Yan, G. / Liang, X. / Long, Z. et al. | British Library Online Contents | 2011



    Ship heading control based on backstepping and Least squares support vector machine

    Xie, Shuo / Chu, Xiumin / Liu, Chenguang et al. | IEEE | 2017