Journal Article10.1016/J.APM.2019.05.038
Reliability sensitivity analysis method based on subset simulation hybrid techniques
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TL;DR: The results revealed that the proposed method efficiently and accurately solves rare-event, system-level, and real-world engineering problems with explicit and implicit limit state functions.
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About: This article is published in Applied Mathematical Modelling. The article was published on 01 Nov 2019. The article focuses on the topics: Subset simulation & Importance sampling.
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Sensitivity Analysis in Probabilistic Structural Design: A Comparison of Selected Techniques
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Estimation of small failure probability using generalized subset simulation
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Mechanism reliability and sensitivity analysis method using truncated and correlated normal variables
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Random and multi-super-ellipsoidal variables hybrid reliability analysis based on a novel active learning Kriging model
TL;DR: The effectiveness and precision of the proposed method are validated by four practical applications and the Monte Carlo Sampling is performed for the hybrid reliability problem with random and multi-super-ellipsoidal variables to evaluate the maximum failure probability.
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Estimation of Small Failure Probabilities in High Dimensions by Subset Simulation
Siu-Kui Au,James L. Beck +1 more
TL;DR: In this article, a set simulation approach is proposed to compute small failure probabilities encountered in reliability analysis of engineering systems, which can be expressed as a product of larger conditional failure probabilities by introducing intermediate failure events.
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Andrea Saltelli,Stefano Tarantola,Francesca Campolongo,Marco Ratto +3 more
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TL;DR: In this paper, the authors present a method for sensitivity analysis of a fish population model using Monte Carlo filtering and variance-based methods, which is based on the Bayesian uncertainty estimation.
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AK-MCS: An active learning reliability method combining Kriging and Monte Carlo Simulation
TL;DR: An iterative approach based on Monte Carlo Simulation and Kriging metamodel to assess the reliability of structures in a more efficient way and is shown to be very efficient as the probability of failure obtained with AK-MCS is very accurate and this, for only a small number of calls to the performance function.
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