Journal ArticleDOI
A new parallel adaptive structural reliability analysis method based on importance sampling and K-medoids clustering
TLDR
RBIK strives to rapidly enable the Kriging model to satisfy the GCC rather than focusing on a single candidate sample, which is the most obvious difference between RBIK and other adaptive structural reliability analysis methods.About:
This article is published in Reliability Engineering & System Safety.The article was published on 2022-02-01. It has received 15 citations till now. The article focuses on the topics: Kriging & Cluster analysis.read more
Citations
More filters
Journal ArticleDOI
Parallel adaptive Bayesian quadrature for rare event estimation
TL;DR: In this paper , a parallel adaptive Bayesian quadrature (PABQ) method is proposed for probabilistic reliability analysis, where the Monte Carlo method is replaced with an importance ball sampling technique to reduce the sample size needed for rare failure event estimation.
Journal ArticleDOI
Adaptive structural reliability analysis method based on confidence interval squeezing
TL;DR: In this article , a confidence interval squeezing (CIS) method was proposed to improve the estimation accuracy of failure probability rapidly rather than paying too much attention to the state of a single candidate sample, which is the primary difference between CIS method and other learning functions.
Journal ArticleDOI
Adaptive Kriging-based failure probability estimation for multiple responses
TL;DR: In this paper , the adaptive Kriging-Monte Carlo simulation for multiple responses (AK-MCS-m) and adaptive kriging generalized subset simulation (AKGSS) were proposed to estimate multiple responses in the same system one by one.
Journal ArticleDOI
Reliability analysis with cross-entropy based adaptive Markov chain importance sampling and control variates
TL;DR: In this paper , the authors proposed a method of improving the performance of cross-entropy based Gaussian mixture importance sampling, and compared its performance with the recent advancements, using Markov Chain Monte Carlo (MCMC) sampling.
Journal ArticleDOI
Structural reliability with credibility based on the non-probabilistic set-theoretic analysis
Yu Yan,Xiaojun Wang,Yunlong Li +2 more
TL;DR: In this article , a structural reliability analysis method based on the non-probabilistic credible set model is proposed, which overcomes the high requirement for the sample size of structural uncertain parameters.
References
More filters
Journal ArticleDOI
Aleatory or epistemic? Does it matter?
TL;DR: In this article, the sources and characters of uncertainties in engineering modeling for risk and reliability analyses are discussed, and they are generally categorized as either aleatory or epistemic, if the modeler sees a possibility to reduce them by gathering more data or by refining models.
Journal ArticleDOI
AK-MCS: An active learning reliability method combining Kriging and Monte Carlo Simulation
TL;DR: An iterative approach based on Monte Carlo Simulation and Kriging metamodel to assess the reliability of structures in a more efficient way and is shown to be very efficient as the probability of failure obtained with AK-MCS is very accurate and this, for only a small number of calls to the performance function.
Journal ArticleDOI
Efficient Global Reliability Analysis for Nonlinear Implicit Performance Functions
TL;DR: This paper develops an efficient reliability analysis method that accurately characterizes the limit state throughout the random variable space and is both accurate for any arbitrarily shaped limit state and computationally efficient even for expensive response functions.
Journal ArticleDOI
A new look at the response surface approach for reliability analysis
TL;DR: In this article, a polynomial approximation of actual limit states in the reliability analysis is used to reduce the number of analyses required by using closed-form mechanical models to predict the behavior of complex structural systems.
Journal ArticleDOI
A combined Importance Sampling and Kriging reliability method for small failure probabilities with time-demanding numerical models
TL;DR: An original and easily implementable method called AK-IS for active learning and Kriging-based Importance Sampling, based on the AK-MCS algorithm, that enables the correction or validation of the FORM approximation with only a very few mechanical model computations.
Related Papers (5)
Clustering for Probability Density Functions by New -Medoids Method
Determining the Best Clustering Number of K-Means Based on Bootstrap Sampling
Lianmin Yu,Changyin Zhou +1 more