AKSE: A novel adaptive Kriging method combining sampling region scheme and error-based stopping criterion for structural reliability analysis

2022 ◽  
Vol 219 ◽  
pp. 108214
Author(s):  
Jinsheng Wang ◽  
Guoji Xu ◽  
Yongle Li ◽  
Ahsan Kareem
Keyword(s):  
Reliability Analysis ◽  
Structural Reliability ◽  
Kriging Method ◽  
Stopping Criterion ◽  
Structural Reliability Analysis ◽  
Adaptive Kriging

An active learning method combining adaptive kriging and weighted penalty for structural reliability analysis

2021 ◽  
pp. 1748006X2110161
Author(s):  
Xiongxiong You ◽  
Mengya Zhang ◽  
Diyin Tang ◽  
Zhanwen Niu
Keyword(s):  
Active Learning ◽  
Reliability Analysis ◽  
Penalty Function ◽  
Structural Reliability ◽  
Learning Method ◽  
Structural Reliability Analysis ◽  
Highly Nonlinear ◽  
Active Learning Method ◽  
Sample Points ◽  
Adaptive Kriging

Reducing the surrogate model-based method computation without loss of prediction accuracy remains a significant challenge in structural reliability analysis. The unbalanced probability density, important information in critical region and information redundancy of added sample points are ignored in most of traditional surrogate-based methods, resulting in heavy computational burden. In this work, an active learning combining adaptive Kriging method and weighted penalty (AK-WP) is proposed to analyze the reliability of engineering structures. Firstly, an active learning and weighted penalty function (WPLF) is the result of integrating active learning method, weighted function and penalty function, which is proposed to find the most probable point (MPP). Meanwhile, to avoid redundant information, the best suitable MPP is determined by a proposed distance law established between the found MPP and the existing design of experiment (DoE). Secondly, the Kriging model is refined according to best suitable MPP in each iteration. Thirdly, the failure probability is estimated by the Monte Carlo sample points from the n-ball domain until the convergence condition is satisfied. The accuracy and efficiency of the proposed method are demonstrated by some numerical examples including the highly nonlinear, the small probability problems and implicit function, as well as a real engineering application.

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