Title: Targeted random sampling: a new approach for efficient reliability estimation for complex systems
Authors: Michael D. Shields; V.S. Sundar
Addresses: Department of Civil Engineering, Johns Hopkins University, Baltimore, MD, USA ' Department of Civil Engineering, Johns Hopkins University, Baltimore, MD, USA
Abstract: A novel approach, targeted random sampling, is presented for estimating failure probabilities for systems with complex limit states. The method, underpinned by the refined stratified sampling concept by Shields et al., refines the sampling strata in the vicinity of the limit state to concentrate samples near the limit state and accurately resolve the failure domain in a very small number of samples - even for problems with strongly non-linear limit states. The method is compared with importance sampling and subset simulation. It produces very accurate estimates for complex problems where the importance sampling density is difficult, or impossible, to identify and is shown to converge much more rapidly than subset simulation for problems with moderate dimension, producing very accurate estimates with greatly reduced coefficient of variation in a fraction of the number of samples. Some challenges in the method are discussed including the extension to high dimensional reliability assessment.
Keywords: reliability analysis; variance reduction; refined stratified sampling; Monte Carlo simulation; targeted random sampling; reliability estimation; complex systems; failure probabilities; reliability assessment; importance sampling; subset simulation.
International Journal of Reliability and Safety, 2015 Vol.9 No.2/3, pp.174 - 190
Available online: 26 Oct 2015 *Full-text access for editors Access for subscribers Purchase this article Comment on this article