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Revised regional importance measures in the presence of epistemic and aleatory uncertainties

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Abstract

Two revised regional importance measures (RIMs), that is, revised contribution to variance of sample mean (RCVSM) and revised contribution to variance of sample variance (RCVSV), are defined herein by using the revised means of sample mean and sample variance, which vary with the reduced range of the epistemic parameter. The RCVSM and RCVSV can be computed by the same set of samples, thus no extra computational cost is introduced with respect to the computations of CVSM and CVSV. From the plots of RCVSM and RCVSV, accurate quantitative information on variance reductions of sample mean and sample variance can be read because of reduced upper bound of the range of the epistemic parameter. For general form of quadratic polynomial output, the analytical solutions of the original and the revised RIMs are given. Numerical example is employed and results demonstrate that the analytical results are consistent and accurate. An engineering example is applied to testify the validity and rationality of the revised RIMs, which can give instructions to the engineers about how to reduce variance of sample mean and sample variance by reducing the range of epistemic parameters.

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References

  1. Saltelli A, Tarantola S, Camplongo F, et al. Sensitivity Analysis in Practice: A Guide to Assessing Scientific Models. New York: Wiley, 2004

    Google Scholar 

  2. Hamby D M. A review of techniques for parameter sensitivity analysis of environmental models. Environ Monit Assess, 1994, 32: 135–154

    Article  Google Scholar 

  3. Ben-Haim Y. Convex models of uncertainty in radial pulse bucking of shells. J Appl Mech, 1993, 60(3): 683–688

    Article  MATH  Google Scholar 

  4. Ben-Haim Y. A non-probabilistic concept of reliability. Struct Saf, 1994, 14(4): 227–245

    Article  Google Scholar 

  5. Ben-Haim Y. A non-probabilistic measure of reliability of linear systems based on expansion of convex models. Struct Saf, 1995, 17(2): 91–109

    Article  Google Scholar 

  6. Elishakoff I. Essay on uncertainties in elastic and viscoelastic structures: from A. M. Freudenthal’s criticisms to modern convex modeling. Comput Struct, 1995, 56(6): 871–895

    Article  MATH  Google Scholar 

  7. Elishakoff I. Discussion on: A non-probabilistic concept of reliability. Struct Saf, 1995, 17(2): 195–199

    MathSciNet  Google Scholar 

  8. Cremona C, Gao Y. The possibilistic reliability theory: theoretical aspects and applications. Struct Saf, 1997, 19(2): 173–201

    Article  Google Scholar 

  9. Guo S X, Lu Z Z, Feng Y S. A non-probabilistic model of structural reliability based on interval analysis. Chin J Comput Mech, 2001, 18(1): 56–60

    Google Scholar 

  10. Guo S X, Zhang L, Li Y. Procedures for computing the non-probabilistic reliability index of uncertain in structures. Chin J Comput Mech, 2005, 22(2): 227–231

    Google Scholar 

  11. Qiu Z P, Mueller P C, Frommer A. The new nonprobabilistic criterion of failure for dynamical systems based on convex models. Math Comput Model, 2004, 20(1–2): 201–215

    Article  MathSciNet  Google Scholar 

  12. Kang Z, Bai S. On robust design optimization of truss structures with bounded uncertainties. Struct Multidisc Optim, 2013, 47(5): 699–714

    Article  MATH  MathSciNet  Google Scholar 

  13. Van Griensven A, Meixner T, Grunwald S et al. A global sensitivity analysis tool for the parameters of multi-variable catchment models. J Hydrol 2006, 324: 10–23

    Article  Google Scholar 

  14. Morris M D. Factorial sampling plans for preliminary computational experiments. Technometrics, 1991, 33: 161–174

    Article  Google Scholar 

  15. Helton J C, Davis F J. Latin hypercube sampling and the propagation of uncertainty in analyses of complex systems. Reliab Eng Syst Saf 2003, 81: 23–69

    Article  Google Scholar 

  16. Helton J C, Davis F J. Sampling-based methods. In: Saltelli A, Chan K, Scott E M, eds. Sensitivity Analysis. New York: Wiley, 2000. 101–153

    Google Scholar 

  17. Saltelli A, Marivoet J. Non-parametric statistics in sensitivity analysis for model output: A comparison of selected techniques. Reliab Eng Syst Saf, 1990, 28: 229–253

    Article  Google Scholar 

  18. Sobol’ I M. Sensitivity analysis for non-linear mathematical models. Math Model Comput Exp, 1993, 1: 407–414

    MATH  MathSciNet  Google Scholar 

  19. Sobol I M. Global sensitivity indices for nonlinear mathematical models and their Monte Carlo estimates. Mat Comput Simul, 2001, 55(1): 221–280

    MathSciNet  Google Scholar 

  20. Iman R L, Hora S C. A robust measure of uncertainty importance for use in fault tree system analysis. Risk Anal, 1990, 10(3): 401–406

    Article  Google Scholar 

  21. Castillo E, Minguez R, Castillo C. Sensitivity analysis in optimization and reliability problems. Reliab Eng Syst Saf, 2008, 93(12): 1788–1800

    Article  Google Scholar 

  22. Chun M H, Han S J, Tak, N I. An uncertainty importance measure using a distance metric for the change in a cumulative distribution function. Reliab Eng Syst Saf, 2000, 70(3): 313–321

    Article  Google Scholar 

  23. Liu H B, Chen W, Sudjianto A. Relative entropy based method for probabilistic sensitivity analysis in engineering design. J Mech Des, 2006, 128(3): 326–333

    Article  Google Scholar 

  24. Borgonovo E. A new uncertainty importance measure. Reliab Eng Syst Saf, 2007, 92(6): 771–784

    Article  Google Scholar 

  25. Millwater H, Singh G, Cortina M. Development of localized probabilistic sensitivity method to determine random variable regional importance. Reliab Eng Syst Saf, 2012, 107: 3–15

    Article  Google Scholar 

  26. Sinclair J. Response to the PSACOIN Level S exercise. PSACOIN Level S Intercomparison. Nucl Energy Agency, OECD, 1993

    Google Scholar 

  27. Bolado-Lavin R, Castaings W, Tarantola S. Contribution to the sample mean plot for graphical and numerical sensitivity analysis. Reliab Eng Syst Saf, 2009, 94: 1041–1049

    Article  Google Scholar 

  28. Tarantola S, Kopustinskas V, Bolado-Lavin R, et al. Sensitivity analysis using contribution to sample variance plot: Application to a water hammer model. Reliab Eng Syst Saf, 2012, 99: 62–73

    Article  Google Scholar 

  29. Krzykacz-Hausmann B. An approximate sensitivity analysis of results from complex computer models in the presence of epistemic and aleatory uncertainties. Reliab Eng Syst Saf, 2006, 91(10–11): 1210–1218

    Article  Google Scholar 

  30. He S Q, Wang S. Structural Reliability Analysis and Design (in Chinese). Beijing: National Defense Industry Press, 1993. 33–39

    Google Scholar 

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Correspondence to Lei Cheng.

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Cheng, L., Lu, Z. & Wu, D. Revised regional importance measures in the presence of epistemic and aleatory uncertainties. Sci. China Phys. Mech. Astron. 58, 1–11 (2015). https://doi.org/10.1007/s11433-014-5527-9

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  • DOI: https://doi.org/10.1007/s11433-014-5527-9

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