Volume 38, Number 2, April 2020
|Page(s)||412 - 419|
|Published online||17 July 2020|
Structural Reliability Algorithms of Kriging Model Based on Improved Learning Strategy
School of Power and Energy, Northwestern Polytechnical University, Xi'an 710072, China
Aiming at the problems of implicit and highly nonlinear limit state function in the process of reliability analysis of mechanical products, a reliability analysis method of mechanical structures based on Kriging model and improved EGO active learning strategy is proposed. For the problem that the traditional EGO method cannot effectively select points in the limit state surface region, an improved EGO method is proposed. By dealing with the predicted values of sample point model with absolute values and assume that the distribution state of response values remains the same, the work focus of active learning selection points is moved to the vicinity, where the points are with larger prediction variance or close to the limit state surface. Three examples show that, compared with the classical active learning method, the proposed method has good global and local search ability, and can estimate the exact failure probability value under the condition of less calculation of the limit state function.
Key words: structural reliability / Kriging model / active learning function / Monte Carlo method / failure probability / algorithm
关键字 : 结构可靠性 / Kriging模型 / 主动学习 / Monte Carlo方法 / 失效概率 / 算法
© 2019 Journal of Northwestern Polytechnical University. All rights reserved.
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