Use of Relevance Vector Machine in Structural Reliability Analysis

Zhou, Changcong; Lü, Zhenzhou; Yuan, Xiukai

doi:10.2514/1.c031950

Cited by 22 publications

(7 citation statements)

References 31 publications

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“…In this section, an exponent nonlinear function is validated [31], which is expressed by Eq. ( 15) as, g (x) = exp (0.2x 1 + 1.4) − x 2 (15) where all basic random variables are independent with each other and obey standard normal distribution.…”

Section: Case Ii: Exponent Nonlinear Functionmentioning

confidence: 99%

Reliability Analysis Based on Optimization Random Forest Model and MCMC

Yang¹,

Ren²

2020

Computer Modeling in Engineering &Amp; Sciences

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Based on the rapid simulation of Markov Chain on samples in failure region, a novel method of reliability analysis combining Monte Carlo Markov Chain (MCMC) with random forest algorithm was proposed. Firstly, a series of samples distributing around limit state function are generated by MCMC. Then, the samples were taken as training data to establish the random forest model. Afterwards, Monte Carlo simulation was used to evaluate the failure probability. Finally, examples demonstrate the proposed method possesses higher computational efficiency and accuracy.

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Section: Case Ii: Exponent Nonlinear Functionmentioning

confidence: 99%

Reliability Analysis Based on Optimization Random Forest Model and MCMC

Yang¹,

Ren²

2020

Computer Modeling in Engineering &Amp; Sciences

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“…So far, an increasing number of researches [33][34][35] proven that RVM holds significant potential in reliability analysis. Zhou et al [36,37] were the first combine RVM with IS for application in reliability analysis. Drawing inspiration from ALK-MCS, Li et al [38] proposed RVM-MCS.…”

Section: Introductionmentioning

confidence: 99%

A novel two-stage reliability analysis method combining improved cross-entropy adaptive sampling and relevant vector machine

Fan,

Yang,

Liu

2024

Preprint

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The computational burden becomes unbearable when reliability analysis involves time-consuming finite element analysis, especially for rare events. Therefore, reducing the number of performance function calls is the only way to improve computing efficiency. This paper proposes a novel reliability analysis method that combines relevant vector machine (RVM) and improved cross-entropy adaptive sampling (iCE). In this method, RVM is employed to approximate the limit state surface and iCE is performed based on the constructed RVM. To guarantee the precision of RVM, the first level samples and the last level samples of iCE are used as candidate samples and the last level samples are regenerated along with the RVM updates. To prevent unnecessary updates of RVM, the proposed method considers the positions of the samples in the current design of experiment. In addition, based on the statistical properties of RVM and iCE, an error-based stopping criterion is proposed. The accuracy and efficiency of the proposed method were validated through four benchmark examples. Finally, the proposed method is applied to engineering problems which are working in extreme environment.

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“…However, RVM involves the selection of a kernel function similar to SVM and parameter tuning is unavoidable if the selected kernel has a free parameter(s). RVM has also been successfully applied for SRA [39][40][41]. Besides RVM, several modifications of SVM were also attempted for improved SRA, e.g., the applications of least squares support vector machine (LS-SVM) for regression [42,43], particle filter-SVR [37], extended SVR [44], Bayesian SVR [45] and support vector density-based importance sampling method [46].…”

Section: Introductionmentioning

confidence: 99%