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Sensitivity of Sample for Simulation-Based Reliability Analysis Methods

Xiukai Yuan1,2,*, Jian Gu1, Shaolong Liu1

1 School of Aerospace and Engineering, Xiamen University, Xiamen, 361005, China
2 Institute for Risk and Reliability, Leibniz Universität Hannover, Hannover, Germany

* Corresponding Author: Xiukai Yuan. Email: email

(This article belongs to the Special Issue: Novel Methods for Reliability Evaluation and Optimization of Complex Mechanical Structures)

Computer Modeling in Engineering & Sciences 2021, 126(1), 331-357.


In structural reliability analysis, simulation methods are widely used. The statistical characteristics of failure probability estimate of these methods have been well investigated. In this study, the sensitivities of the failure probability estimate and its statistical characteristics with regard to sample, called ‘contribution indexes’, are proposed to measure the contribution of sample. The contribution indexes in four widely simulation methods, i.e., Monte Carlo simulation (MCS), importance sampling (IS), line sampling (LS) and subset simulation (SS) are derived and analyzed. The proposed contribution indexes of sample can provide valuable information understanding the methods deeply, and enlighten potential improvement of methods. It is found that the main differences between these investigated methods lie in the contribution indexes of the safety samples, which are the main factors to the efficiency of the methods. Moreover, numerical examples are used to validate these findings.


Cite This Article

APA Style
Yuan, X., Gu, J., Liu, S. (2021). Sensitivity of sample for simulation-based reliability analysis methods. Computer Modeling in Engineering & Sciences, 126(1), 331-357.
Vancouver Style
Yuan X, Gu J, Liu S. Sensitivity of sample for simulation-based reliability analysis methods. Comput Model Eng Sci. 2021;126(1):331-357
IEEE Style
X. Yuan, J. Gu, and S. Liu "Sensitivity of Sample for Simulation-Based Reliability Analysis Methods," Comput. Model. Eng. Sci., vol. 126, no. 1, pp. 331-357. 2021.

cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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