An augmented weighted simulation method for high-dimensional reliability analysis. (November 2021)

• Record Type: Journal Article
• Authors: Meng, Zeng; Pang, Yongsheng; Zhou, Huanlin
• Is Part Of: Structural safety. Volume 93(2021)
• Publication Date: 2021-11
• Subjects: High-dimensional reliability analysis -- Weighted simulation method -- Conditional probability -- Space reduction strategy -- Augmented weighted simulation method; Structural stability -- Periodicals; Safety factor in engineering -- Periodicals; Reliability (Engineering) -- Periodicals; Constructions -- Stabilité -- Périodiques; Coefficient de sécurité en ingénierie -- Périodiques; Fiabilité -- Périodiques; 620.86
• DOI: 10.1016/j.strusafe.2021.102117
• Languages: English
• ISSNs: 0167-4730

Abstract

Highlights: A novel augmented weighted simulation method is proposed. A new optimization method is proposed to compute the threshold values. A space reduction strategy is developed. The coefficient of variation of the augmented weighted simulation method is derived. The accuracy of the proposed method is significantly improved. Abstract: In the reliability analysis of mechanical systems, sampling method is widely used due to the universality and practicability. However, the computation of high-dimensional problems encounters tremendous numerical difficulties, especially when the performance function is highly nonlinear. In this study, an augmented weighted simulation method (AWSM) is proposed in order to tackle this difficulty. The basic idea of AWSM is introducing a series of intermediate events into weighted simulation method (WSM), in which a new optimization method is constructed to reasonably determine each intermediate event. In this way, the failure event is divided to a sequence of conditional events, and the failure probability is accordingly converted to the product of conditional probabilities. Furthermore, a space reduction strategy is proposed to increase the probability of the samples generated in each conditional event, which greatly improves the sampling efficiency. Also, the coefficient of variation of AWSM is derived. Two mathematical examples and four engineering examples are tested, and the results demonstrate the efficiency and accuracy of the proposed method for high-dimensional problems.