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Saddlepoint approximation based structural reliability analysis with non-normal random variables

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Abstract

The saddlepoint approximation (SA) can directly estimate the probability distribution of linear performance function in non-normal variables space. Based on the property of SA, three SA based methods are developed for the structural system reliability analysis. The first method is SA based reliability bounds theory (RBT), in which SA is employed to estimate failure probability and equivalent normal reliability index for each failure mode firstly, and then RBT is employed to obtain the upper and the lower bounds of system failure probability. The second method is SA based Nataf approximation, in which SA is used to estimate the probability density function (PDF) and cumulative distribution function (CDF) for the approximately linearized performance function of each failure mode. After the PDF of each failure mode and the correlation coefficients among approximately linearized performance functions are estimated, Nataf distribution is employed to approximate the joint PDF of multiple structural system performance functions, and then the system failure probability can be estimated directly by numerical simulation using the joint PDF. The third method is SA based line sampling (LS). The standardization transformation is needed to eliminate the dimensions of variables firstly in this case. Then LS method can express the system failure probability as an arithmetic average of a set of failure probabilities of the linear performance functions, and the probabilities of the linear performance functions can be estimated by the SA in the non-normal variables space. By comparing basic concepts, implementations and results of illustrations, the following conclusions can be drawn: (1) The first method can only obtain the bounds of system failure probability and it is only acceptable for the linear limit state function; (2) the second method can give the estimation of system failure probability, and its error mostly results from the approximation of Nataf distribution for the joint PDF of the structural system performance functions and the linearization of the performance functions; (3) the SA based LS method can obtain the estimator of system failure probability, which converges to the actual value along with the increase of sample size. The SA based LS method considers the influence of nonlinearity of limit state function on the failure probability, and it is acceptable for the structural system both with a single failure mode and with multiple failure modes, therefore it has the widest applicability.

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References

  1. Pradlwarter H J, Pellissetti M F. Realistic and efficient reliability estimation for aerospace structures. Comput Method Appl M, 2005, 194(12–16): 1597–1617

    Article  MATH  Google Scholar 

  2. Zhao Y G, Ono T. A general procedure for first/second-order reliability method (FORM/SORM). Struct Saf, 1999, 21(2): 95–112

    Article  Google Scholar 

  3. Ditlevsen O. Narrow reliability bounds for structural system. J Struct Mech, 1979, 7(4): 435–451

    Google Scholar 

  4. Daniels H E. Saddlepoint approximations in statistics. Ann Math Statist, 1954, 25(4): 631–650

    Article  MATH  MathSciNet  Google Scholar 

  5. Huang B Q, Du X P. Probabilistic uncertainty analysis by mean-value first order saddlepoint approximation. Reliab Eng Syst Safe, 2008, 93(2): 325–336

    Article  MathSciNet  Google Scholar 

  6. Du X P, Sudjianto A. First order saddlepoint approximation for reliability analysis. AIAA J, 2004, 42(6): 1199–1207

    Article  Google Scholar 

  7. Du X P. Saddlepoint approximation for sequential optimization and reliability analysis. J Mech Design, 2008, 130(1): 11–21

    Article  Google Scholar 

  8. Wang S. General saddlepoint approximations in the bootstrap. Stat Probab Lett, 1992, 13(1–2): 61–66

    Article  Google Scholar 

  9. Lugannani R, Rice S O. Saddlepoint approximation for the distribution of the sum of independent random variables. Adv Appl Probab, 1980, 12(2): 475–490

    Article  MATH  MathSciNet  Google Scholar 

  10. Gouits C, Casella G. Explaining the saddlepoint approximation. Am Stat, 1999, 53(3): 216–624

    Article  Google Scholar 

  11. Huzurbazar S. Practical saddlepoint approximations. Am Stat, 1999, 53(3): 225–232

    Article  MathSciNet  Google Scholar 

  12. Gatto R, Ronchetti E. General saddlepoint approximations of marginal densities and tail probabilities. J Am Stat Assoc, 1996, 91(433): 666–673

    Article  MATH  MathSciNet  Google Scholar 

  13. Gillespie C S, Renshaw E. An improved saddlepoint approximation. Math Biosci, 2007, 208(2): 359–374

    Article  MATH  MathSciNet  Google Scholar 

  14. Der Kiureghian A, Liu P L. Structural reliability under incomplete probability information. J Eng Mech-ASCE, 1986, 112(1): 85–104

    Article  Google Scholar 

  15. Liu P L, Der Kiureghian A. Multivariate distribution models with prescribed marginals and covariances. Probabilist Eng Mech, 1986, 1(2): 105–112

    Article  Google Scholar 

  16. Li H S, Lu Z Z, Yuan X K. Nataf transformation based point estimate method. Chin Sci Bull, 2008, 53(17): 2586–2592

    Article  Google Scholar 

  17. Li H S. Research on probabilistic uncertainty analysis and design optimization methods (in Chinese). Dissertation of Masteral Degree. Xi’an: Northwestern Polytechnical University, 2008.

    Google Scholar 

  18. Schuller G I, Pradlwarter H J, Koutsourelakis P S. A comparative study of reliability estimation procedures for high dimension. In: Proseedings of the 16th ASCE Engineering Mechanics Conference. Seattle: ASCE, 2003. 1–7

    Google Scholar 

  19. Schuller G I, Pradlwarter H J, Koutsourelakis P S. A critical appraisal of reliability estimation procedures for high dimension. Probabilist Eng Mech, 2004, 19(4): 463–474

    Article  Google Scholar 

  20. Lu Z Z, Song S F, Yue Z F, et al. Reliability sensitivity method by line sampling. Struct Saf, 2008, 30(2): 517–532

    Article  Google Scholar 

  21. Au S K, Beck J L. A new adaptive importance sampling scheme for reliability calculations. Struct Saf, 1999, 21(2): 135–158

    Article  Google Scholar 

  22. Burlaga L, Sittler E, Mariani F, et al. Magnetic Loop Behind an Interplanetary Shock Voyager, Helios, and IMP 8 Observations. J Geophys Res, 1981, 86(1): 6673–6684

    Article  Google Scholar 

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Correspondence to ZhenZhou Lu.

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Song, S., Lu, Z. Saddlepoint approximation based structural reliability analysis with non-normal random variables. Sci. China Technol. Sci. 53, 566–576 (2010). https://doi.org/10.1007/s11431-009-0358-z

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