Engineering and Systems > Home > Uncertainties and Reliability of Multiphysical Systems > Optimization and Reliability > Article
ChangWu Huang
INSA Rouen
Abdelkhalak El Hami
INSA Rouen
Bouchaïb Radi
LIMII FST
Settat - Morocco
Published on 9 February 2017 DOI : 10.21494/ISTE.OP.2017.0116
In Part II of the overview of structural reliability analysis methods, the category of sampling methods is reviewed. The basic Monte Carlo simulation is the foundation for sampling methods of reliability analysis. Sampling methods can evaluate the failure probability defined by both explicit and implicit performance function. With sufficient number of samples, simulation methods can give accurate results. However, for complex problem the computational cost is expensive. Thus, based on variance reduction techniques, some variants of basic Monte Carlo simulation method are proposed to reduce the computational cost. Monte Carlo simulation and its variants, including importance sampling, adaptive sampling, Latin hypercube sampling, directional simulation, and subset simulation, are presented and summarized in this paper.
In Part II of the overview of structural reliability analysis methods, the category of sampling methods is reviewed. The basic Monte Carlo simulation is the foundation for sampling methods of reliability analysis. Sampling methods can evaluate the failure probability defined by both explicit and implicit performance function. With sufficient number of samples, simulation methods can give accurate results. However, for complex problem the computational cost is expensive. Thus, based on variance reduction techniques, some variants of basic Monte Carlo simulation method are proposed to reduce the computational cost. Monte Carlo simulation and its variants, including importance sampling, adaptive sampling, Latin hypercube sampling, directional simulation, and subset simulation, are presented and summarized in this paper.
Reliability Analysis Sampling Methods Monte Carlo simulation Variance Reduction
Reliability Analysis Sampling Methods Monte Carlo simulation Variance Reduction