An efficient hybrid reliability analysis method based on active learning Kriging model and multimodal‐optimization‐based importance sampling

2021 ◽  
Author(s):  
Tai Wang ◽  
Xufeng Yang ◽  
Mi Caiying
Keyword(s):  
Active Learning ◽  
Reliability Analysis ◽  
Importance Sampling ◽  
Kriging Model ◽  
Multimodal Optimization ◽  
Analysis Method ◽  
Hybrid Reliability

scholarly journals Probability and convex set hybrid reliability analysis based on active learning Kriging model

2015 ◽  
Vol 39 (14) ◽  
pp. 3954-3971 ◽  
Author(s):  
Xufeng Yang ◽  
Yongshou Liu ◽  
Yishang Zhang ◽  
Zhufeng Yue
Keyword(s):  
Active Learning ◽  
Reliability Analysis ◽  
Convex Set ◽  
Kriging Model ◽  
Hybrid Reliability

Evidence-Theory-Based Reliability Analysis Through Kriging Surrogate Model

2021 ◽  
Vol 144 (3) ◽  
Author(s):  
Dequan Zhang ◽  
Yunfei Liang ◽  
Lixiong Cao ◽  
Jie Liu ◽  
Xu Han
Keyword(s):  
Active Learning ◽  
Reliability Analysis ◽  
Surrogate Model ◽  
Evidence Theory ◽  
Kriging Model ◽  
High Approximation ◽  
Analysis Method ◽  
Performance Function ◽  
Intersection Points ◽  
Sample Points

Abstract It is generally understood that intractable computational intensity stemming from repeatedly calling performance function when evaluating the contribution of joint focal elements hinders the application of evidence theory in practical engineering. In order to promote the practicability of evidence theory for the reliability evaluation of engineering structures, an efficient reliability analysis method based on the active learning Kriging model is proposed in this study. To start with, a basic variable is selected according to basic probability assignment (BPA) of evidence variables to divide the evidence space into sub-evidence spaces. Intersection points between the performance function and the sub-evidence spaces are then determined by solving the univariate root-finding problem. Sample points are randomly identified to enhance the accuracy of the subsequently established surrogate model. Initial Kriging model with high approximation accuracy is subsequently established through these intersection points and additional sample points generated by Latin hypercube sampling. An active learning function is employed to sequentially refine the Kriging model with minimal sample points. As a result, belief (Bel) measure and plausibility (Pl) measure are derived efficiently via the surrogate model in the evidence-theory-based reliability analysis. The currently proposed analysis method is exemplified with three numerical examples to demonstrate the efficiency and is applied to reliability analysis of positioning accuracy for an industrial robot.

An active learning kriging model for hybrid reliability analysis with both random and interval variables

2014 ◽  
Vol 51 (5) ◽  
pp. 1003-1016 ◽  
Author(s):  
Xufeng Yang ◽  
Yongshou Liu ◽  
Yi Gao ◽  
Yishang Zhang ◽  
Zongzhan Gao
Keyword(s):  
Active Learning ◽  
Reliability Analysis ◽  
Kriging Model ◽  
Interval Variables ◽  
Hybrid Reliability
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