A refined subset simulation for the reliability analysis using the subset control variate

• Published in: Structural safety Vol. 87; p. 102002
• Main Authors: Abdollahi, Azam, Azhdary Moghaddam, Mehdi, Hashemi Monfared, Seyed Arman, Rashki, Mohsen, Li, Yong
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier Ltd 01.11.2020; Elsevier BV
• Subjects: Algorithms; Coefficient of variation; Computer simulation; Control variate technique; Failure analysis; Failure probability; Mathematical analysis; Monte Carlo simulation; Reliability analysis; Reliability engineering; Robustness (mathematics); Statistical analysis; Subset simulation; Subset simulation; Control variate technique; Coefficient of variation; Failure probability
• ISSN: 0167-4730; 1879-3355
• DOI: 10.1016/j.strusafe.2020.102002

•The proposed SCV method refines subset simulation by using control variates technique.•Assess the efficiency of SCV through a new formulation for coefficient of variation.•The proposed approach overcomes the convectional subset simulation drawbacks.•Assessed the efficiency of SCV method on some challenging examples. The reliability analysis of an engineering system with a small failure probability (Pf) and a complex-geometry performance function is a major challenge in the probabilistic engineering mechanics for which the subset simulation (SS) is a promising addressing algorithm. However, SS is potentially problematic when solving complex problems. This has triggered an interest in improving and reformulating the SS. This paper presents the subset control variate (SCV) technique, a novel approach to reformulate the conventional SS, and provides the statistical properties such as coefficient of variation (c.o.v) of the estimate of Pf using SCV. SCV enhances and generalizes the original SS. It improves the conventional SS formulation and can employ different sampling approaches in the SS to properly find the most probable failure domain with complex and misleading geometry. The proposed method’s capabilities are compared with those of the conventional SS and examined by solving several numerical and practical problems with challenging performance functions. The results, validated by the Monte Carlo Simulation (MCS), show that the SCV improved the SS robustness for solving highly nonlinear problems involving misleading performance functions and its c.o.v is less than that of the original SS for all the numerical examples considered in this study.