For importance analysis of structural models with correlated inputs, a new method combining support vector machine regression and the single-loop Monte Carlo technique is proposed. In this new combined method, the correlated inputs are transformed to uncorrelated ones by orthogonally independent transformation first, then support vector machine regression is employed to establish the multiple-dimensional regression models involving all inputs, on which the single-loop Monte Carlo is used to obtain the importance measures of the correlated inputs. Support vector machine regression is also used to establish the one-dimensional regression model involving one concerned input. This direct support vector machine regression is proposed as a comparative method. Profiting from the high efficiency of support vector machine regression, the combined and direct methods can get the importance analysis results of the correlated inputs more efficiently than the Monte Carlo simulation. By comparing the results of combined single-loop Monte Carlo technique and those of the direct method, it is found that the former is more efficient and robust than the latter. Several examples illustrate the efficiency and accuracy of the proposed methods.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    New Importance Analysis Method for Structures with Correlated Inputs


    Beteiligte:
    Guancheng, Fang (Autor:in) / Zhenzhou, Lu (Autor:in) / Wenbin, Ruan (Autor:in)

    Erschienen in:

    Journal of Aircraft ; 53 , 3 ; 627-635


    Erscheinungsdatum :

    2016-02-25


    Format / Umfang :

    9 pages




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch