An Analysis Method of Mechanical Structural Reliability Based on the Kriging Model

被引：0
|
作者：
Liu K. [1 ]
Li X.-L. [2 ]
Wang J. [3 ]
机构：
[1] School of Mechanical Engineering, Dalian University of Technology, Dalian
[2] School of Mechatronics Engineering, Harbin Institute of Technology, Harbin
[3] School of Mechanical Engineering & Automation, Northeastern University, Shenyang
来源：
Liu, Kuo (liukuo@dlut.edu.cn) | 1600年 / Northeast University卷 / 38期
关键词：
Adaptive sampling strategy; Failure probability; Kriging model; Mechanical structural reliability; Monte Carlo method;
D O I：
10.12068/j.issn.1005-3026.2017.07.019
中图分类号：
学科分类号：
摘要：
Based on an analysis of the drawbacks of the existing mechanical structure reliability sampling methods and the main factors influencing the estimation accuracy of failure probability, an analysis method of mechanical structural reliability based on the Kriging model and adaptive sampling strategy is proposed. The proposed sampling strategy combines random sampling and clustering algorithm, and ensures in probability that the new sample points locate themselves in the area that makes significant contribution to failure probability and avoids unnecessary sampling in the unimportant areas. The condition of convergence for the proposed Kriging model is deduced mainly based on the law of large numbers and central limit theorem. Two examples are adopted to illustrate the convergence process, accuracy and stability of the proposed method. The results show that the proposed method can estimate failure probability with high accuracy in the condition that the number of calls to structural performance function is small. © 2017, Editorial Department of Journal of Northeastern University. All right reserved.
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页码：1002 / 1006
页数：4
相关论文
共 12 条
  • [1] Alvarez D.A., Hurtado J.E., An efficient method for the estimation of structural reliability intervals with random sets, dependence modeling and uncertain inputs, Computers and Structures, 142, pp. 54-63, (2014)
  • [2] Blatman G., Sudret B., An adaptive algorithm to build up sparse polynomial chaos expansions for stochastic finite element analysis, Probabilistic Engineering Mechanics, 25, pp. 183-197, (2010)
  • [3] Roussouly N., Petitjean F., Salaun M., A new adaptive response surface method for reliability analysis, Probabilistic Engineering Mechanics, 32, pp. 103-115, (2013)
  • [4] Echard B., Gayton N., Lemaire M., AK-MCS: an active learning reliability method combining Kriging and monte carlo simulation, Structural Safety, 33, pp. 145-154, (2011)
  • [5] Tong C., Sun Z.-L., Yang L., Et al., An active learning reliability method based on Kriging and Monte Carlo, Acta Aeronautica et Astronautica Sinica, 36, 9, pp. 2992-3001, (2015)
  • [6] Yang D.-H., An W.-G., Li T.-J., Structural reliability analysis based on artificial neural network, Acta Armamentarii, 28, 4, pp. 495-498, (2007)
  • [7] Alibrandi U., Alani A.M., Ricciardi G., A new sampling strategy for SVM-based response surface for structural reliability analysis, Probabilistic Engineering Mechanics, 41, pp. 1-12, (2015)
  • [8] Zhang Q., Li X.-S., Analysis of structural reliability based on Kriging model, Chinese Journal of Computational Mechanics, 23, 2, pp. 175-179, (2006)
  • [9] Xiong F.F., Liu Y., Xiong Y., Et al., A double weighted stochastic response surface method for reliability analysis, Journal of Mechanical Science and Technology, 26, 8, pp. 2573-2580, (2012)
  • [10] Nguyen X.S., Sellier A., Duprat F., Et al., Adaptive response surface method based on a double weighted regression technique, Probabilistic Engineering Mechanics, 24, pp. 135-143, (2009)
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