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Markov Chain Monte Carlo Simulation Methods for Structural Reliability Analysis

15 citations0/0 files storedProcedia Engineering

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Probabilistic and Robust Engineering Design Reliability and Maintenance Optimization Markov Chains and Monte Carlo Methods

Abstract

Two Markov chain Monte Carlo simulation methods for reliability estimation, subset simulation and the moving particles algorithm, are compared. To this end, both low-dimensional and high-dimensional test cases are considered. The investigation sheds light on the avoidance of correlated samples in both algorithms, notably on the necessity of a burn-in period and the influence of a metamodel.

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References

2001Estimation of small failure probabilities in high dimensions by subset simulation 1987Second‐Order Reliability Approximations 1987A critical appraisal of methods to determine failure probabilities 2015MCMC algorithms for Subset Simulation 2008Geometric insight into the challenges of solving high-dimensional reliability problems 2010An adaptive directional importance sampling method for structural reliability 2011Simulation and Estimation of Extreme Quantiles and Extreme Probabilities 2015Moving particles: A parallel optimal Multilevel Splitting method with application in quantiles estimation and meta-model based algorithms

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