A stochastic process discretization method combing active learning Kriging model for efficient time-variant reliability analysis

2021 ◽  
Vol 384 ◽  
pp. 113990
Author(s):  
Dequan Zhang ◽  
Pengfei Zhou ◽  
Chen Jiang ◽  
Meide Yang ◽  
Xu Han ◽  
...  
Keyword(s):  
Stochastic Process ◽  
Active Learning ◽  
Reliability Analysis ◽  
Kriging Model ◽  
Discretization Method

scholarly journals An Efficient Reliability Analysis Method Combining Improved EIF Active Learning Mechanism and Kriging Metamodel

2018 ◽  
Vol 2018 ◽  
pp. 1-9
Author(s):  
Dawei Zhang ◽  
Xiaohua Wu ◽  
Weilin Li ◽  
Xiaofeng Lv
Keyword(s):  
Active Learning ◽  
Reliability Analysis ◽  
Kriging Model ◽  
Expected Improvement ◽  
Learning Mechanisms ◽  
Learning Mechanism ◽  
Engineering Problems ◽  
Real Performance ◽  
Practical Engineering ◽  
Expected Improvement Function

Complex implicit performance functions widely exist in many engineering problems. The reliability analysis of these problems has always been a challenge. Using surrogate model instead of real performance function is one of the methods to solve this kind of problem. Kriging is one of the surrogate models with precise interpolation technique. In order to make the kriging model achieve higher accuracy using a small number of samples, i.e., improve its practicability and feasibility in practical engineering problems, some active learning equations are wildly studied. Expected improvement function (EIF) is one of them. However, the EIF has a great disadvantage in selecting the added sample point. Therefore, a joint active learning mechanism, J-EIF, is proposed to obtain the ideal added point. The J-EIF active learning mechanism combines the two active learning mechanisms and makes full use of the characters of kriging model. It overcomes the shortcoming of EIF active learning mechanism in the selection of added sample points. Then, using Monte Carlo Simulation (MCS) results as a reference, the reliability of two examples is estimated. The results are discussed showing that the learning efficiency and accuracy of the improved EIF are both higher than those of the traditional EIF.

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