Open Access
Volume 41, Number 1, February 2023
Page(s) 105 - 114
Published online 02 June 2023
  1. FAES M, MOENS D. Recent trends in the modeling and quantification of non-probabilistic uncertainty[J]. Archives of Computational Methods in Engineering, 2020, 27: 633–671 [Article] [Google Scholar]
  2. VALDEBENITO M A, JENSEN H A, HERNANDEZ H B, et al. Sensitivity estimation of failure probability applying line sampling[J]. Reliability Engineering & System Safety, 2018, 171: 99–111 [Google Scholar]
  3. YUAN X, FAES M, VALDEBENITO M, et al. Efficient imprecise reliability analysis using the augmented space integral[J]. Reliability Engineering & System Safety, 2021, 210: 107477 [Google Scholar]
  4. VALDEBENITO M A, SCHUELLER G I. A survey on approaches for reliability-based optimization[J]. Structural and Multidisciplinary Optimization, 2010, 42(5): 645–663 [Article] [CrossRef] [Google Scholar]
  5. RACKWITZ R, FLESSLER B. Structural reliability under combined random load sequences[J]. Computers & Structures, 1978, 9(5): 489–494 [Google Scholar]
  6. METROPOLIS N. The beginning of the monte carlo method[J]. Los Alamos Science, 1987, 14: 125–130 [Google Scholar]
  7. YUAN X, LIU S, FAES M, et al. An efficient importance sampling approach for reliability analysis of time-variant structures subject to time-dependent stochastic load[J]. Mechanical Systems and Signal Processing, 2021, 159: 107699 [Article] [CrossRef] [Google Scholar]
  8. AU S K, BECK J L, et al. Estimation of small failure probabilities in high dimensions by subset simulation[J]. Probabilistic Engineering Mechanics, 2001, 16(4): 263–277 [Article] [Google Scholar]
  9. VALDEBENITO M, WEI P, SONG J, et al. Failure probability estimation of a class of series systems by multidomain line sampling[J]. Reliability Engineering & System Safety, 2021, 213: 107673 [Google Scholar]
  10. GASSER M, SCHUELLER G I. Reliability-based optimization of structural systems[J]. Mathematical Methods of Operations Research, 1997, 46(3): 287–307 [Article] [Google Scholar]
  11. JENSEN H A. Structural optimization of linear dynamical systems under stochastic excitation: a moving reliability database approach[J]. Computer Methods in Applied Mechanics & Engineering, 2005, 194(12/13/14/15/16): 1757–1778 [CrossRef] [Google Scholar]
  12. KAYMAZ I. Application of Kriging method to structural reliability problems[J]. Structural Safety, 2005, 27(2): 133–151 [Article] [CrossRef] [Google Scholar]
  13. CORTES C, VAPNIK V. Support-vector networks[J]. Machine Learning, 1995, 20(3): 273–297 [Google Scholar]
  14. 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]
  15. ZOU T, MAHADEVAN S. A direct decoupling approach for efficient reliability-based design optimization[J]. Structural and Multidisciplinary Optimization, 2006, 31: 190 [Article] [CrossRef] [Google Scholar]
  16. YUAN X. Local estimation of failure probability function by weighted approach[J]. Probabilistic Engineering Mechanics, 2013, 34: 1–11 [Article] [Google Scholar]
  17. YUAN X, ZHENG Z, ZHANG B. Augmented line sampling for approximation of failure probability function in reliability-based analysis[J]. Applied Mathematical Modelling, 2020, 80: 895–910 [Article] [Google Scholar]
  18. SONG J, WEI P, VALDEBENITO M A, et al. Non-intrusive imprecise stochastic simulation by line sampling[J]. Structural Safety, 2020, 84: 101936 [Article] [CrossRef] [Google Scholar]
  19. AU S K. Reliability-based design sensitivity by efficient simulation[J]. Computers & Structures, 2005, 83(14): 1048–1061 [CrossRef] [Google Scholar]
  20. 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]
  21. CHING J, HSIEH Y H. Approximate reliability-based optimization using a three-step approach based on subset simulation[J]. Journal of Engineering Mechanics, 2007, 133(4): 481–493 [Article] [CrossRef] [Google Scholar]
  22. YUAN X, LIU S, VALDEBENITO M A, et al. Efficient framework for failure probability function estimation in augmented space[J]. Structural Safety, 2021, 92(3): 102104 [CrossRef] [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.