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AK–TSAGL: A two-stage hybrid algorithm combining global exploration and local exploitation based on active learning for structural reliability analysis

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References

2011AK-MCS: An active learning reliability method combining Kriging and Monte Carlo Simulation2,048 cited
2008Efficient Global Reliability Analysis for Nonlinear Implicit Performance Functions1,141 cited
2012A combined Importance Sampling and Kriging reliability method for small failure probabilities with time-demanding numerical models692 cited
2016LIF: A new Kriging based learning function and its application to structural reliability analysis444 cited
2015A new learning function for Kriging and its applications to solve reliability problems in engineering400 cited
2021Machine learning-based methods in structural reliability analysis: A review370 cited
2019REIF: A novel active-learning function toward adaptive Kriging surrogate models for structural reliability analysis297 cited
2004A response surface method based on weighted regression for structural reliability analysis289 cited
2022A review and assessment of importance sampling methods for reliability analysis273 cited
2018AK-MCSi: A Kriging-based method to deal with small failure probabilities and time-consuming models218 cited
2014An active learning kriging model for hybrid reliability analysis with both random and interval variables213 cited
2010An efficient response surface method using moving least squares approximation for structural reliability analysis192 cited
2014Efficient structural reliability analysis method based on advanced Kriging model

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188 cited

2018ESC: an efficient error-based stopping criterion for kriging-based reliability analysis methods180 cited

2019Structural reliability analysis based on ensemble learning of surrogate models146 cited

2019AK-ARBIS: An improved AK-MCS based on the adaptive radial-based importance sampling for small failure probability135 cited

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2025Two-stage failure probability function estimation method based on improved cross-entropy importance sampling and adaptive Kriging12 cited
2025An active-learning method based on hierarchical Kriging model for multi-fidelity reliability analysis9 cited