Open Access
Volume 40, Number 6, December 2022
Page(s) 1212 - 1222
Published online 10 February 2023
  1. YUN W Y, LU Z Z, HE P F, et al. An efficient method for estimating the parameter global reliability sensitivity analysis by innovative single-loop process and embedded Kriging model[J]. Mechanical Systems and Signal Processing, 2019, 133: 106288 [Article] [CrossRef] [Google Scholar]
  2. GUO J, DU X P. Sensitivity analysis with mixture of epistemic and aleatory uncertainties[J]. AIAA Journal, 2007, 45(9): 2337–2349 [Article] [CrossRef] [Google Scholar]
  3. TORⅡ A J, NOVOTNY A A. A priori error estimates for local reliability-based sensitivity analysis with Monte Carlo simulation[J]. Reliability Engineering & System Safety, 2021, 213(3): 107749 [CrossRef] [Google Scholar]
  4. LI L Y, LU Z Z. Regional importance effect analysis of the input variables on failure probability[J]. Computers & Structures, 2013, 125: 74–85 [CrossRef] [Google Scholar]
  5. SALTELLI A. Sensitivity analysis for importance assessment[J]. Risk Analysis, 2002, 22(3): 579–590 [Article] [CrossRef] [Google Scholar]
  6. SOBOL I M. Sensitivity estimates for nonlinear mathematical models[J]. Mathematical and Computer Modelling, 1993, 1(4): 407–414 [Google Scholar]
  7. BORGONOVO E. A new uncertainty importance measure[J]. Reliability Engineering & System Safety, 2007, 92(6): 771–784 [CrossRef] [Google Scholar]
  8. HAO W R, LU Z Z, LI L Y. A new interpretation and validation of variance based importance measures for models with correlated inputs[J]. Computer Physics Communications, 2013, 184(5): 1401–1413 [Article] [CrossRef] [Google Scholar]
  9. SUO Bin, ZENG Chao, CHENG Yongsheng, et al. New index for reliability sensitivity analysis under epistemic uncertainty[J]. Acta Aeronautica et Astronautica Sinica, 2013, 34(7): 1605–1615 [Article] (in Chinese) [Google Scholar]
  10. WANG L, XIONG C, WANG X, et al. A dimension-wise method and its improvement for multidisciplinary interval uncertainty analysis[J]. Applied Mathematical Modelling, 2018, 59: 680–695 [Article] [CrossRef] [Google Scholar]
  11. PEREZ-CANEDO B, CONCEPCION-MORALES E R. A method to find the unique optimal fuzzy value of fully fuzzy linear programming problems with inequality constraints having unrestricted L-R fuzzy parameters and decision variables[J]. Expert Systems with Applications, 2019, 123: 256–269 [Article] [CrossRef] [Google Scholar]
  12. SANKARARAMAN S, MAHADEVAN S. Separating the contributions of variability and parameter uncertainty in probability distributions[J]. Reliability Engineering & System Safety, 2013, 112: 187–199 [CrossRef] [Google Scholar]
  13. LI L Y, LU Z 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]
  14. LI L Y, LU Z Z, FENG J, et al. Moment-independent importance measure of basic variable and its state dependent parameter solution[J]. Structural Safety, 2012, 38: 40–47 [Article] [CrossRef] [Google Scholar]
  15. WANG P, LU Z Z, TANGZ C. An application of the Kriging method in global sensitivity analysis with parameter uncertainty[J]. Applied Mathematical Modelling, 2013, 37(9): 6543–6555 [Article] [CrossRef] [Google Scholar]
  16. LU J, DARMOFAL D L. Higher-dimensional integration with Gaussian weight for applications in probabilistic design[J]. Siam Journal on Scientific Computing, 2004, 26(2): 613–624 [CrossRef] [Google Scholar]
  17. HUANG X Z, LI Y X, ZHANG Y M, et al. A new direct second-order reliability analysis method[J]. Applied Mathematical Modelling, 2018, 55: 68–80 [CrossRef] [Google Scholar]
  18. XU J, LU Z H. Evaluation of moments of performance functions based on efficient cubature formulation[J]. Journal of Engineering Mechanics, 2017, 143(8): 06017007 [CrossRef] [Google Scholar]
  19. LIU Y S, LI L Y, ZHOU C C, et al. Efficient multivariate sensitivity analysis for dynamic models based on cubature formula[J]. Engineering Structures, 2020, 206: 110164 [CrossRef] [Google Scholar]
  20. ZHANG X B, LU Z H, CHENG K, et al. A novel reliability sensitivity analysis method based on directional sampling and Monte Carlo simulation[J]. Journal of Risk and Reliability, 2020, 234(4): 622–635 [Google Scholar]

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