Solid Mechanics and Vehicle Conceptual Design

Hybrid Structure Reliability Method Combining Optimized Kriging Model and Importance Sampling

  • LIU Zhan ,
  • ZHANG Jianguo ,
  • WANG Cancan ,
  • TAN Chunlin ,
  • SUN Jing
Expand
  • 1. School of Reliability and Systems Engineering, Beihang University, Beijing 100191, China;
    2. Beijing Institute of Spacecraft Overall Design, China Academy of Space Technology, Beijing 100094, China;
    3. Beijing Satellite Manufacturer, Beijing 100094, China

Received date: 2012-07-24

  Revised date: 2013-01-03

  Online published: 2013-01-09

Supported by

National Basic Research Program of China (2013CB733000)

Abstract

In structural reliability analysis, a polynomial function is usually used to approach the implicit limit state function. But the limit state function is likely to be implicit and highly nonlinear for complex aeronautic and astronautic structures. The calculation may not converge if the simulation of the polynomial function is not accurate enough. In order to improve the accuracy, efficiency, and convergency, a reliability method combining the approved Kriging model and importance sampling is proposed in this paper. Firstly, the parameter of Kriging model is optimized using the artificial bee colony algorithm. Then the implicit limit state function is fitted with the optimized Kriging model, and the sampling center is revised constantly by importance sampling to improve gradually the fitting accuracy. Finally, the reliability is solved combining the Kriging model and the parsing algorithm such as the first order reliability method (FORM) or second order reliability method (SORM). This method improves the accuracy and convergency of reliability calculations with highly nonlinear limit state functions, and has high computing efficiency.

Cite this article

LIU Zhan , ZHANG Jianguo , WANG Cancan , TAN Chunlin , SUN Jing . Hybrid Structure Reliability Method Combining Optimized Kriging Model and Importance Sampling[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2013 , 34(6) : 1347 -1355 . DOI: 10.7527/S1000-6893.2013.0235

References

[1] Melchers R E. Structural reliability analysis and predictions. New York: Wiley, 1999: 124-156.

[2] Nowak A S, Collins K R. Reliability of structures. Boston: McGraw-Hill, 2000: 32-35.

[3] Faravelli L. Response surface approach for reliability analysis. Journal of Engineering Mechanics, 1989, 115(12): 2763-2781.

[4] Zhang J G, Su D, Liu Y W. Reliability analysis and optimization of mechanical products. Beijing: Publishing House of Electronics Industry, 2007: 29-115. (in Chinese) 张建国, 苏多, 刘英卫. 机械产品可靠性分析与优化. 北京: 电子工业出版社, 2007: 29-115.

[5] Jiang S H, Li D Q, Phoon K. A comparative study of response surface method and stochastic response surface method for structural relisbility analysis. Engineering Journal of Wuhan University, 2012, 45(1): 46-53. (in Chinese) 蒋水华, 李典庆, 方国光. 结构可靠度分析的响应面法和随机响应面法的比较. 武汉大学学报: 工学版, 2012, 45(1): 46-53.

[6] Kaymaz I. Application of Kriging method to structural reliability problems. Structural Safety, 2005, 27(2): 133-151.

[7] Luo X F, Li X, Zhou J, et al. A Kriging-based hybrid optimization algorithm for slope reliability analysis. Structural Safety, 2012, 34(1): 401-406.

[8] Wang B, Sun Q. Structural reliability computation based on Kriging mode. Computer Simulation, 2011, 28(2): 113-116. (in Chinese) 汪保, 孙秦. 改进的Kriging模型的可靠度计算. 计算机仿真, 2011, 28(2): 113-116.

[9] Akay B, Karaboga D. A modified artificial bee colony algorithm for real-parameter optimization. Information Sciences, 2012, 192: 120-142.

[10] Karaboga D. An idea based on honey bee swarm for numerical optimization. Erciyes University Technical Report TR06, 2005.

[11] Liu L, Wang T Y. Support vector machine optimization based on artificial bee colony algorithm. Journal of Tianjin University, 2011, 44(9): 803-809. (in Chinese) 刘路, 王太勇. 基于人工蜂群算法的支持向量机优化. 天津大学学报, 2011, 44(9): 803-809.

[12] Lophaven S N, Nielsen H B, Sondergaard J. DACE, a matlab Kriging toolbox. Technical Report IMM-TR-2002-12, 2002.

[13] Lophaven S N, Nielsen H B, Sondergaard J. Aspects of the matlab toolbox DACE. Technical Report IMM-REP-2002-13, 2002.

[14] Li Y Y, Zhang D S. The analysis of plane frame structure displacement reliability based on MATLAB. Journal of Jiaying University: Natural Science, 2011, 29(2): 45-48. (in Chinese) 李远瑛, 张德生. 基于MATLAB的结构可靠度分析方法研究. 嘉应学院学报: 自然科学版, 2011, 29(2): 45-48.

[15] Xiao Y L, Su G S, Gaussian process importance sampling method for structural reliability analysis. Water Power, 2010, 36(12): 31-34. (in Chinese) 肖义龙, 苏国韶. 结构可靠度分析的高斯过程重要抽样方法. 水力发电, 2010, 36(12): 31-34.

[16] Bucher C G, Bourgand U. A fast and efficient response surface approach for structural reliability problems. Struct Safety, 1990, 7(1): 57-66.

[17] Romero V J, Swiler L P, Giunta A A. Construction of response surfaces based on progressive-lattice-sampling experimental designs with application to uncertainty propagation. Structural Safety, 2004, 26(2): 201-219.

[18] Giunta A, McFarland J, Swiler L, et al. The promise and peril of uncertainty quantification using response surface approximations. Structures and Infrastructure Engineering, 2006, 2(3): 175-189.

[19] Echard B, Gayton N, Lemaire M. AK-MCS: an active learning reliability method combining Kriging and Monte Carlo simulation. Structural Safety, 2011, 33(2): 145-154.

Outlines

/