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
• 1999A new adaptive importance sampling scheme for reliability calculations564 cited
• 2013Metamodel-based importance sampling for structural reliability analysis535 cited
• 2011Reliability-based design optimization using kriging surrogates and subset simulation450 cited
• 2011Assessing small failure probabilities by combined subset simulation and Support Vector Machines419 cited
• 2016Assessing small failure probabilities by AK–SS: An active learning method combining Kriging and Subset Simulation412 cited
• 2019REIF: A novel active-learning function toward adaptive Kriging surrogate models for structural reliability analysis297 cited
• 2014An improved adaptive kriging-based importance technique for sampling multiple failure regions of low probability278 cited
• 2021Hybrid enhanced Monte Carlo simulation coupled with advanced machine learning approach for accurate and efficient structural reliability analysis242 cited
• 2018Structural reliability analysis and uncertainties‐based collaborative design and optimization of turbine blades using surrogate model167 cited
• 2014An efficient reliability method combining adaptive importance sampling and Kriging metamodel