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• W4285324563 abstract "Statistical moment-based methods are highly prioritized in stochastic system analysis and structural reliability assessment due to their simplicity and high efficiency. However, previously published studies have not dealt with the summary of these methods that have their own unique performance. In this work, to clarify the applicability of several typical statistical moment-based methods to different practical engineering situations, five statistical moment-based methods, including the full factorial numerical integration(FFNI), the univariate dimension reduction method(UDRM), the bivariate dimension reduction method (BDRM), the sparse grid numerical integration(SGNI) and the quasi-Monte Carlo method(QMC), have been compared by several numerical examples. Comparison of results shows that: 1) The UDRM maintains the highest efficiency in all the examples, also it keeps high accuracy when the performance function has low nonlinearity; 2) The effect of interaction terms is considered in the BDRM and the SGNI to a certain extent. The SGNI performs fairly high accuracy and efficiency for the high-nonlinear situation. As for the BDRM, the computing cost of bivariate component functions will increase remarkably with the increase of dimension; 3) While the FFNI exhibits advantages in precision, the curse of dimensionality will occur when the function has high dimension; 4) The QMC among these candidate methods may apply most to high-dimensional problems." @default.
• W4285324563 created "2022-07-14" @default.
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• W4285324563 date "2021-12-03" @default.
• W4285324563 modified "2023-09-26" @default.
• W4285324563 title "Performance Comparison of Several Statistical Moment-based Methods" @default.
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• W4285324563 doi "https://doi.org/10.1145/3518781.3519218" @default.
• W4285324563 hasPublicationYear "2021" @default.
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