Improving operational safety of transport and handling machinery based on automobile chassis by forming fleets of machinery with specific level of reliability

Repin, Sergei; Zazykin, Andrei; Maksimov, Sergei

doi:10.1016/j.trpro.2020.10.070

Cited by 12 publications

(1 citation statement)

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“…The purpose of reliability analysis is to estimate the failure probability of a structural system during normal operation under given conditions. At present, reliability analysis has received widespread attention in engineering applications, such as mechanical systems, 6 infrastructure engineering, 7 automobiles, 8 and industrial robots. 9 Estimating failure probability (P f ) based on a mechanical model is the key to slope reliability analysis.…”

Section: Introductionmentioning

confidence: 99%

Copula‐based importance sampling method for efficient slope reliability analysis

Zhou

Xiao

Jiang

et al. 2023

Num Anal Meth Geomechanics

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Efficient slope reliability analysis that accounts for the interdependencies of random variables is challenging. In this study, an efficient reliability method is proposed based on importance sampling (IS) and copulas. First, the IS method based on the design point is introduced to construct the marginal IS density. Copulas are subsequently adopted to capture the interdependencies and establish both the initial joint distributions and the joint IS densities of random variable pairs. The IS algorithm is modified to consider the dependencies through not only the estimation of the probability distribution but also random sampling. Finally, a framework of the copula‐based IS method is constructed. The applicability, accuracy, efficiency, and robustness of the proposed method were validated by a classical slope case. The results showed that with the proposed method, slope reliability could be efficiently assessed with a relatively high accuracy. The dependencies of the variables significantly affected the IS and final slope reliability, whereas the IS density had a significantly lower effect. The proposed method combines the advantages of IS and copulas well. Interdependencies of the variables can be properly characterized during sampling. Moreover, the proposed method was shown to be robust regardless of the system failure probability level. It can be further applied to improve the random finite element method in the future.

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