Article2021

An Active Kriging-Based Learning Method for Hybrid Reliability Analysis

Chengning Zhou · Ning‐Cong Xiao · Ming Jian Zuo · Wei Gao

20 citations0/0 files storedIEEE Transactions on Reliability

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Probabilistic and Robust Engineering Design Reliability and Maintenance Optimization Geotechnical Engineering and Analysis

Abstract

In this article, we propose an active kriging-based learning method for hybrid reliability analysis (HRA) with random and interval variables. An improved sampling strategy is proposed to target the sampling areas. Samples with maximum responses greater than 0 and minimum responses less than 0 are selected and regarded as the candidate samples; then, aU-based learning function is developed in which multiple samples of the interval are considered instead of one particular sample. To terminate the proposed method, a hybrid convergence criterion is proposed. Finally, an improved optimization strategy based on the DIRECT algorithm is developed for the Monte Carlo simulation conducted for the HRA. The performance of the proposed method is demonstrated by four numerical cases. The results illustrate that the proposed method is accurate and efficient for HRA.

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References

1975Probability Concepts in Engineering Planning and Design 2011AK-MCS: An active learning reliability method combining Kriging and Monte Carlo Simulation 2001Reliability Assessment Using Stochastic Finite Element Analysis 2016Assessing small failure probabilities by AK–SS: An active learning method combining Kriging and Subset Simulation 2017A new adaptive sequential sampling method to construct surrogate models for efficient reliability analysis 2017An efficient reliability method combining adaptive Support Vector Machine and Monte Carlo Simulation 2007Structural reliability analysis using Monte Carlo simulation and neural networks 2014An active learning kriging model for hybrid reliability analysis with both random and interval variables

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