Open Access
Issue
JNWPU
Volume 43, Number 5, October 2025
Page(s) 869 - 877
DOI https://doi.org/10.1051/jnwpu/20254350869
Published online 05 December 2025
  1. HASOFER A M, LIND N C. An exact and invariant first order reliability format[J]. Journal of Engineering Mechanics ASCE, 1974, 100(1): 111–121 [Google Scholar]
  2. KIUREGHIAN A D, DESTEFANO M. Efficient algorithm for 2nd-order reliability analysis[J]. Journal of Engineering Mechanics ASCE, 1991, 117(12): 2904–2923 [Article] [Google Scholar]
  3. AU S K, BECK J L. Estimation of small failure probabilities in high dimensions by subset simulation[J]. Probabilistic Engineering Mechanics, 2001, 16(4): 263–277 [Article] [Google Scholar]
  4. AU S K, BECK J L. Important sampling in high dimensions[J]. Structural Safety, 2003, 25(2): 139–163 [Article] [Google Scholar]
  5. PRADLWARTER H J, SCHUELLER G I, KOUTSOURELAKIS P Set al. Application of line sampling simulation method to reliability benchmark problems[J]. Structural Safety, 2007, 29(3): 208–221 [Article] [Google Scholar]
  6. DITLEVSEN O, OLESEN R, MOHR G. Solution of a class of load combination problems by directional simulation[J]. Structural Safety, 1986, 4(2): 95–109 [Article] [Google Scholar]
  7. AU S K, BECK J L. A new adaptive importance sampling scheme for reliability calculations[J]. Structural Safety, 1999, 21(2): 135–158 [Article] [Google Scholar]
  8. PAPAIOANNOU I, PAPADIMITRIOU C, STRAUB D. Sequential importance sampling for structural reliability analysis[J]. Structural Safety, 2016, 62: 66–75 [Article] [Google Scholar]
  9. DUBOURG V, SUDRET B, DEHEEGER F. Metamodel-based importance sampling for structural reliability analysis[J]. Probabilistic Engineering Mechanics, 2013, 33: 47–57 [Article] [Google Scholar]
  10. SONG Shufang, LYU Zhenzhou. Reliability sensitivity analysis based on subset simulation and importance sampling[J]. Chinese Journal of Theoretical and Applied Mechanic, 2008, 40(5): 654–662 (in Chinese) [Google Scholar]
  11. ECHARD B, GAYTON N, LEMAIRE M. AK-MCS: an active learning reliability method combining Kriging and Monte Carlo simulation[J]. Structural Safety, 2011, 33(2): 145–154 [Article] [CrossRef] [Google Scholar]
  12. FAURIAT W, GAYTON N. AK-SYS: an adaptation of the AK-MCS method for system reliability[J]. Reliability Engineering and System Safety, 2014, 123: 137–144 [Article] [Google Scholar]
  13. DUBOURG V, SUDRET B, DEHEEGER F. Metamodel-based importance sampling for structural reliability analysis[J]. Probabilistic Engineering Mechanics, 2013, 33: 47–57 [Article] [Google Scholar]
  14. WEI Xinpeng, YAO Zhongyang, BAO Wenli, et al. Evidence-theory-based reliability analysis method using active-learning Kriging model[J]. Journal of Mechanical Engineering, 2024, 60(2): 356–368 (in Chinese) [Google Scholar]
  15. ZHU X, LU Z, YUN W. An efficient method for estimating failure probability of the structure with multiple implicit failure domains by combining Meta-IS with IS-AK[J]. Reliability Engineering and System Safety, 2020, 193: 106644 [Article] [Google Scholar]
  16. ZHOU Ce, BAI Bin, YE Nan. Reliability prediction of engineering system based on adaptive particle swarm optimization support vector regression[J]. Journal of Mechanical Engineering, 2023, 59(14): 328–338 (in Chinese) [Google Scholar]
  17. HU Weifei, LIAO Jiale, GUO Yunfeiet al. Time-dependent reliability analysis based on physics-informed neutral network[J]. Journal of Mechanical Engineering, 2024, 60(13): 141–153 (in Chinese) [Google Scholar]
  18. GASSER M, SCHUELLER G I. Reliability-based optimization of structural systems[J]. Mathematical Methods of Operations Research, 1997, 46: 287–307 [Article] [Google Scholar]
  19. JENSEN H A. Structural optimization of linear dynamical systems under stochastic excitation: a moving reliability database approach[J]. Computer Methods in Applied Mechanics and Engineering, 2005, 194(12/13/14/15/16): 1757–1778 [Google Scholar]
  20. LING Chunyan, LYU Zhenzhou, YUAN Wanying. Efficient method for failure probability function[J]. Journal of National University of Defense Technology, 2018, 40(3): 159–167 (in Chinese) [Google Scholar]
  21. AU S K. Reliability-based design sensitivity by efficient simulation[J]. Computers and Structures, 2005, 83(14): 1048–1061 [Article] [Google Scholar]
  22. CHING J, HSIEH Y H. Local estimation of failure probability function and its confidence interval with maximum entropy principle[J]. Probabilistic Engineering Mechanics, 2007, 22(1): 39–49 [Article] [Google Scholar]
  23. YUAN X, LIU S, VALDEBENITO M A, et al. Efficient procedure for failure probability function estimation in augmented space[J]. Structural Safety, 2021, 92: 102104 [Article] [CrossRef] [Google Scholar]
  24. NIKOLAIDIS E, SALEEM S. Probabilistic reanalysis using Monte Carlo simulation[J]. SAE International Journal of Materials and Manufacturing, 2009, 1(1): 22–35 [Google Scholar]
  25. LI L, LU Z. A new algorithm for importance analysis of the inputs with distribution parameter uncertainty[J]. International Journal of Systems Science, 2016, 47(13): 3065–3077 [Article] [CrossRef] [Google Scholar]
  26. CHEN Zhiyuan, LI Luyi. Efficient methods for reliability sensitivity analysis of the inputs with distribution parameter uncertainty[J]. Acta Aeronautica et Astronautica Sinica, 2022, 43(9): 1–20 (in Chinese) [Google Scholar]
  27. YUAN X. Local estimation of failure probability function by weighted approach[J]. Probabilistic Engineering Mechanics, 2013, 34: 1–11 [Article] [Google Scholar]
  28. WEI P, LU Z, SONG J. Extended Monte Carlo simulation for parametric global sensitivity analysis and optimization[J]. AIAA Journal, 2014, 52(4): 867–878 [Article] [Google Scholar]
  29. ZHOU C, LI C, ZHANG H, et al. Reliability and sensitivity analysis of composite structures by an adaptive Kriging based approach[J]. Composite Structures, 2021, 278: 114682 [Article] [Google Scholar]

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