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A general failure-pursuing sampling framework for surrogate-based reliability analysis · Study

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ArticleA general failure-pursuing sampling framework for surrogate-based reliability analysis

Chen Jiang2018

Article2018

A general failure-pursuing sampling framework for surrogate-based reliability analysis

Chen Jiang · Haobo Qiu · Zan Yang · Liming Chen · Liang Gao · Peigen Li

163 citations·0/0 files stored·Reliability Engineering & System Safety

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Probabilistic and Robust Engineering Design Reliability and Maintenance Optimization Advanced Multi-Objective Optimization Algorithms

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References

1974Exact and Invariant Second-Moment Code Format 2001Estimation of small failure probabilities in high dimensions by subset simulation 1978Structural reliability under combined random load sequences 2011AK-MCS: An active learning reliability method combining Kriging and Monte Carlo Simulation 2008Efficient Global Reliability Analysis for Nonlinear Implicit Performance Functions 2004Sequential Optimization and Reliability Assessment Method for Efficient Probabilistic Design 2012A combined Importance Sampling and Kriging reliability method for small failure probabilities with time-demanding numerical models 2009Benchmark study of numerical methods for reliability-based design optimization

Cited by

2019An active learning reliability method combining Kriging constructed with exploration and exploitation of failure region and subset simulation 2019Adaptive kriging-based efficient reliability method for structural systems with multiple failure modes and mixed variables 2019Real-time estimation error-guided active learning Kriging method for time-dependent reliability analysis 2022Recent Advances in Surrogate Modeling Methods for Uncertainty Quantification and Propagation

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Details

Year2018

Typearticle

Published2018-11-08

VenueReliability Engineering & System Safety

ISSN-L0951-8320

Languageen

Citations163

Files0 public / 0

ShelfProbabilistic and Robust Engineering Design

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doi10.1016/j.ress.2018.11.002

openalex_workW2900325101

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