Journal ArticleDOI
An efficient Kriging-based subset simulation method for hybrid reliability analysis under random and interval variables with small failure probability
TLDR
In this method, Kriging metamodel is employed to replace the true performance function, and it is smartly updated based on the samples in the first and last levels of subset simulation (SS) to achieve the smart update.Abstract:
This paper proposes an efficient Kriging-based subset simulation (KSS) method for hybrid reliability analysis under random and interval variables (HRA-RI) with small failure probability. In this method, Kriging metamodel is employed to replace the true performance function, and it is smartly updated based on the samples in the first and last levels of subset simulation (SS). To achieve the smart update, a new update strategy is developed to search out samples located around the projection outlines on the limit-state surface. Meanwhile, the number of samples in each level of SS is adaptively adjusted according to the coefficients of variation of estimated failure probabilities. Besides, to quantify the Kriging metamodel uncertainty in the estimation of the upper and lower bounds of the small failure probability, two uncertainty functions are defined and the corresponding termination conditions are developed to control Kriging update. The performance of KSS is tested by four examples. Results indicate that KSS is accurate and efficient for HRA-RI with small failure probability.read more
Citations
More filters
Journal ArticleDOI
A system active learning Kriging method for system reliability-based design optimization with a multiple response model
Mi Xiao,Jinhao Zhang,Liang Gao +2 more
TL;DR: The results indicate that SALK can locally approximate the limit-state surfaces around the finalSRBDO solution and efficiently reduce the computational cost on the refinement of the region far from the final SR BDO solution.
Journal ArticleDOI
Novel probabilistic model for searching most probable point in structural reliability analysis
TL;DR: The results of the numerical study illustrate that the proposed probabilistic model provides an efficient approach to obtain the MPP which is simpler and more accurate than the usual FORM and FOSAM; particularly for reliability problems with non-normal random variables.
Journal ArticleDOI
Advanced intelligence frameworks for predicting maximum pitting corrosion depth in oil and gas pipelines
Mohamed El Amine Ben Seghier,Behrooze Keshtegar,Mohammed Taleb-Berrouane,Rouzbeh Abbassi,Nguyen-Thoi Trung +4 more
TL;DR: In this article, different advanced approaches using Artificial Intelligence (AI) models were applied, including Artificial Neural Network (ANN), M5 Tree (M5Tree), Multivariate Adaptive Regression Splines (MARS), Locally Weighted Polynomials (LWP), Kriging (KR), and Extreme Learning Machines (ELM).
Journal ArticleDOI
Maximizing natural frequencies of inhomogeneous cellular structures by Kriging-assisted multiscale topology optimization
Yan Zhang,Liang Gao,Mi Xiao +2 more
TL;DR: The results indicate that the multiscale cellular structures obtained by the proposed method show higher natural frequency compared with the monoscale macrostructural and microstructural designs.
Journal ArticleDOI
Multilevel nested reliability-based design optimization with hybrid intelligent regression for operating assembly relationship
TL;DR: The efforts of this study provide the efficient method and model to optimally design the complex operating assembly relationship, and thereby enrich mechanical reliability method.
References
More filters
Journal ArticleDOI
Estimation of Small Failure Probabilities in High Dimensions by Subset Simulation
Siu-Kui Au,James L. Beck +1 more
TL;DR: In this article, a set simulation approach is proposed to compute small failure probabilities encountered in reliability analysis of engineering systems, which can be expressed as a product of larger conditional failure probabilities by introducing intermediate failure events.
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
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.
Journal ArticleDOI
Adaptive Designs of Experiments for Accurate Approximation of a Target Region
TL;DR: An iterative strategy to build designs of experiments is proposed, which is based on an explicit trade-off between reduction of global uncertainty and exploration of the regions of interest, which shows that a substantial reduction of error can be achieved in the crucial regions.