Overview of Structural Reliability Analysis Methods — Part II: Sampling Methods

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