Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Global Sensitivity Analysis of Uncertain Input Variables in Structural Models
AbstractIn this paper, a global sensitivity analysis based on variance is performed to study how the input uncertainty contributes to the output. In order to obtain the sensitivity indices efficiently and accurately, the sparse grid integration is introduced in accordance with the fact that variance-based sensitivity indices can be viewed as nested expressions of expectation operator and variance operator. The proposed method based on the sparse grid integration offers a viable tool for global sensitivity analysis of models with independent or correlated inputs. The paper also discusses methods for decomposing the variance contribution when inputs are correlated. Application in several examples shows that the advantage of the sparse grid integration in estimating integrals is well inherited by the proposed method, which is able to ensure the accuracy of the sensitivity analysis while keeping the computational burden under control.
Global Sensitivity Analysis of Uncertain Input Variables in Structural Models
AbstractIn this paper, a global sensitivity analysis based on variance is performed to study how the input uncertainty contributes to the output. In order to obtain the sensitivity indices efficiently and accurately, the sparse grid integration is introduced in accordance with the fact that variance-based sensitivity indices can be viewed as nested expressions of expectation operator and variance operator. The proposed method based on the sparse grid integration offers a viable tool for global sensitivity analysis of models with independent or correlated inputs. The paper also discusses methods for decomposing the variance contribution when inputs are correlated. Application in several examples shows that the advantage of the sparse grid integration in estimating integrals is well inherited by the proposed method, which is able to ensure the accuracy of the sensitivity analysis while keeping the computational burden under control.
Global Sensitivity Analysis of Uncertain Input Variables in Structural Models
Tang, Chenghu (Autor:in) / Liu, Fuchao / Zhou, Changcong / Wang, Wenxuan
2017
Aufsatz (Zeitschrift)
Englisch
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