TY - JOUR
T1 - Hybrid multiplicative dimension reduction method for uncertainty analysis of engineering structures
AU - Li, Haihe
AU - Wang, Pan
AU - Chang, Qi
AU - Zhou, Changcong
AU - Yue, Zhufeng
N1 - Publisher Copyright:
© IMechE 2020.
PY - 2021/2
Y1 - 2021/2
N2 - For uncertainty analysis of high-dimensional complex engineering problems, this article proposes a hybrid multiplicative dimension reduction method based on the existent multiplicative dimension reduction method. It uses the multiplicative dimension reduction method to approximate the original high-dimensional performance function which is sufficiently smooth and has a small high-order derivative as the product of a series of one-dimensional functions, and then uses this approximation to calculate the statistical moments of the function. Then the variance-based global sensitivity index is employed to identify the important variables, and the identified important variables are subjected to bivariate decomposition approximation. Combined with the univariate multiplicative dimension reduction method, the hybrid decomposition approximation is obtained. Compared with the existing method, the proposed method is more accurate than the univariate decomposition approximation when used for uncertainty analysis of engineering models and needs less computational efforts than the bivariate decomposition. In the end, a numerical example and two engineering applications are tested to verify the effectiveness of the proposed method.
AB - For uncertainty analysis of high-dimensional complex engineering problems, this article proposes a hybrid multiplicative dimension reduction method based on the existent multiplicative dimension reduction method. It uses the multiplicative dimension reduction method to approximate the original high-dimensional performance function which is sufficiently smooth and has a small high-order derivative as the product of a series of one-dimensional functions, and then uses this approximation to calculate the statistical moments of the function. Then the variance-based global sensitivity index is employed to identify the important variables, and the identified important variables are subjected to bivariate decomposition approximation. Combined with the univariate multiplicative dimension reduction method, the hybrid decomposition approximation is obtained. Compared with the existing method, the proposed method is more accurate than the univariate decomposition approximation when used for uncertainty analysis of engineering models and needs less computational efforts than the bivariate decomposition. In the end, a numerical example and two engineering applications are tested to verify the effectiveness of the proposed method.
KW - dimension reduction
KW - Multiplicative dimension reduction method
KW - sensitivity index
KW - statistical moments
KW - uncertainty analysis
UR - http://www.scopus.com/inward/record.url?scp=85086880372&partnerID=8YFLogxK
U2 - 10.1177/1748006X20929973
DO - 10.1177/1748006X20929973
M3 - 文章
AN - SCOPUS:85086880372
SN - 1748-006X
VL - 235
SP - 144
EP - 155
JO - Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability
JF - Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability
IS - 1
ER -