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Failure Probability Estimation Using Asymptotic Sampling and Its Dependence upon the Selected Sampling Scheme

Failure Probability Estimation Using Asymptotic Sampling and Its Dependence upon the Selected... AbstractThe article examines the use of Asymptotic Sampling (AS) for the estimation of failure probability. The AS algorithm requires samples of multidimensional Gaussian random vectors, which may be obtained by many alternative means that influence the performance of the AS method. Several reliability problems (test functions) have been selected in order to test AS with various sampling schemes: (i) Monte Carlo designs; (ii) LHS designs optimized using the Periodic Audze-Eglājs (PAE) criterion; (iii) designs prepared using Sobol’ sequences. All results are compared with the exact failure probability value. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Transactions of the VŠB – Technical University of Ostrava, Civil Engineering Series. de Gruyter

Failure Probability Estimation Using Asymptotic Sampling and Its Dependence upon the Selected Sampling Scheme

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References (20)

Publisher
de Gruyter
Copyright
© 2017 Magdalena Martinásková et al., published by De Gruyter Open
ISSN
1804-4824
eISSN
1804-4824
DOI
10.1515/tvsb-2017-0029
Publisher site
See Article on Publisher Site

Abstract

AbstractThe article examines the use of Asymptotic Sampling (AS) for the estimation of failure probability. The AS algorithm requires samples of multidimensional Gaussian random vectors, which may be obtained by many alternative means that influence the performance of the AS method. Several reliability problems (test functions) have been selected in order to test AS with various sampling schemes: (i) Monte Carlo designs; (ii) LHS designs optimized using the Periodic Audze-Eglājs (PAE) criterion; (iii) designs prepared using Sobol’ sequences. All results are compared with the exact failure probability value.

Journal

Transactions of the VŠB – Technical University of Ostrava, Civil Engineering Series.de Gruyter

Published: Dec 1, 2017

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