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Evidence-theory-based reliability design optimization with parametric correlations

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Abstract

Parametric correlation exists widely in engineering problems. This paper presents an approach of evidence-theory-based design optimization (EBDO) with parametric correlations, which provides an effective computational tool for the structural reliability design involving epistemic uncertainties. According to the existing samples, the most fitting copula function is selected to formulate the joint basic probability assignment (BPA) of the correlated variables. The joint BPA is applied in the constraint reliability analysis, and an approximate technology is given to enhance the efficiency. A decoupling strategy is proposed for transforming the nested optimization of EBDO into a sequential iterative process of deterministic optimization and reliability analysis. The effectiveness of the proposed approach is demonstrated through two numerical examples and an engineering application.

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References

  • Agarwal H, Renaud JE, Preston EL, Padmanabhan D (2004) Uncertainty quantification using evidence theory in multidisciplinary design optimization. Reliab Eng Syst Saf 85(1):281–294

    Article  Google Scholar 

  • Akaike H (1974) A new look at the statistical model identification. IEEE T Automat Contr 19(6):716–723

  • Alyanak E, Grandhi R, Bae HR (2008) Gradient projection for reliability-based design optimization using evidence theory. Eng Optim 40(10):923–935

    Article  Google Scholar 

  • Bae HR, Grandhi RV, Canfield RA (2003) Structural design optimization based on reliability analysis using evidence theory (no. 2003-01-0877). SAE technical paper

  • Breitung K (1991) Probability approximations by log likelihood maximization. J Eng Mech 117(3):457–477

    Article  Google Scholar 

  • Caselton WF, Luo W (1992) Decision making with imprecise probabilities: dempster-Shafer theory and application. Water Resour Res 28(12):3071–3083

    Article  Google Scholar 

  • Dempster AP (1967) Upper and lower probabilities induced by a multivalued mapping. Ann Math Stat 38(2):325–339

    Article  MathSciNet  MATH  Google Scholar 

  • Dong W, Shah HC (1987) Vertex method for computing functions of fuzzy variables. Fuzzy Sets Syst 24(1):65–78

    Article  MathSciNet  MATH  Google Scholar 

  • Du X (2006) Uncertainty analysis with probability and evidence theories. In ASME 2006 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference (pp. 1025–1038). American Society of Mechanical Engineers

  • Du X, Chen W (2002) Sequential optimization and reliability assessment method for efficient probabilistic design. J Mech Des 126(2):871–880

    Google Scholar 

  • Elishakoff I, Colombi P (1993) Combination of probabilistic and convex models of uncertainty when scarce knowledge is present on acoustic excitation parameters. Comput Methods Appl Mech Eng 104(2):187–209

    Article  MathSciNet  MATH  Google Scholar 

  • Ferson S, Hajagos J, Berleant D, Zhang J, Tucker WT, Ginzburg L, Oberkampf W (2004) Dependence in dempster-shafer theory and probability bounds analysis. US: Sandia National Laboratories. https://ualr.edu/jdberleant/papers/Sandia04.pdf

  • Fishman G (2013) Monte Carlo: concepts, algorithms, and applications. Springer Science & Business Media, Berlin

    MATH  Google Scholar 

  • Fletcher R (2013) Practical methods of optimization. John Wiley & Sons, Hoboken

    MATH  Google Scholar 

  • Friswell MI, Mottershead JE (1995) Finite element model updating in structural dynamics. Finite element model updating in structural dynamics. Kluwer Academic Publishers, Dordrecht

    Book  MATH  Google Scholar 

  • Georgescu I (2012) Possibility theory and the risk. Springer, Berlin, p 274

    Book  MATH  Google Scholar 

  • Goda K (2010) Statistical modeling of joint probability distribution using copula: application to peak and permanent displacement seismic demands. Struct Saf 32(2):112–123

    Article  Google Scholar 

  • Grubbs F (1958) An introduction to probability theory and its applications. Technometrics 9(2):342–342

    Google Scholar 

  • Guoqiang C, Jianping T, Yourui T (2018) A reliability-based multidisciplinary design optimization method with evidence theory and probability theory. Int J Reliab Qual Saf Eng 25(01):1850003. https://doi.org/10.1142/S0218539318500031

  • Gupta MM (1992) Fuzzy set theory and its applications. Fuzzy Sets Syst 47(3):396–397

    Article  MathSciNet  Google Scholar 

  • Haldar A, Mahadevan S, Haldar A, Mahadevan S (2013) Probability, reliability and statistical methods in engineering design (haldar, mahadevan). Bautechnik 77(5):379–379

    Google Scholar 

  • Hoffman FO, Hammonds JS (1994) Propagation of uncertainty in risk assessments: the need to distinguish between uncertainty due to lack of knowledge and uncertainty due to variability. Risk Anal 14(5):707–712

    Article  Google Scholar 

  • Huang ZL, Jiang C, Zhang Z, Fang T, Han X (2017) A decoupling approach for evidence-theory-based reliability design optimization. Struct Multidiscip Optim 56(3):647–661

    Article  Google Scholar 

  • Huard D, Evin G, Favre AC, Azen SP (2006) Bayesian copula selection. Comput Stat Data Anal 51(2):809–822

    Article  MathSciNet  MATH  Google Scholar 

  • Jiang C, Han X, Lu GY, Liu J, Zhang Z, Bai YC (2011) Correlation analysis of non-probabilistic convex model and corresponding structural reliability technique. Comput Methods Appl Mech Eng 200(33):2528–2546

    Article  MATH  Google Scholar 

  • Jiang C, Han X, Liu W, Liu J, Zhang Z (2012) A hybrid reliability approach based on probability and interval for uncertain structures. J Mech Des 134(3):031001

    Article  Google Scholar 

  • Jiang C, Zhang Z, Han X, Liu J (2013) A novel evidence-theory-based reliability analysis method for structures with epistemic uncertainty. Comput Struct 129(4):1–12

    Article  Google Scholar 

  • Jiang C, Zhang W, Wang B, Han X (2014) Structural reliability analysis using a copula-function-based evidence theory model. Comput Struct 143:19–31

    Article  Google Scholar 

  • Jiang C, Zhang W, Han X, Ni BY, Song LJ (2015) A vine-copula-based reliability analysis method for structures with multidimensional correlation. J Mech Des 137(6):061405

    Article  Google Scholar 

  • Joe H (1997) Multivariate models and multivariate dependence concepts. Chapman and Hall. CRC, Boca Raton

    Book  MATH  Google Scholar 

  • Kang Z, Luo Y (2009) Non-probabilistic reliability-based topology optimization of geometrically nonlinear structures using convex models. Comput Methods Appl Mech Eng 198(41):3228–3238

    Article  MathSciNet  MATH  Google Scholar 

  • Kiureghian AD, Ditlevsen O (2009) Aleatory or epistemic? Does it matter? Struct Saf 31(2):105–112

    Article  Google Scholar 

  • Liu B (2010) Uncertain risk analysis and uncertain reliability analysis. J Uncertain Syst 4(3):163–170

    Google Scholar 

  • Melchers RE (1987) Structural reliability analysis and prediction. Wiley, Hoboken

    Google Scholar 

  • Meng D, Zhang H, Huang T (2016) A concurrent reliability optimization procedure in the earlier design phases of complex engineering systems under epistemic uncertainties. Adv Mech Eng 8(10):1687814016673976

    Article  Google Scholar 

  • Mourelatos ZP, Zhou J (2006) A design optimization method using evidence theory. J Mech Des 128(4):901–908

    Article  Google Scholar 

  • Nelsen B (2006) An introduction to copulas. Springer, New York

    MATH  Google Scholar 

  • Nikolaidis E, Dan MG, Singhal S (2004) Engineering design reliability handbook. Technometrics 48(1):156–156

    Google Scholar 

  • Noh Y, Choi KK, Du L (2008) Reliability-based design optimization of problems with correlated input variables using a Gaussian copula. Struct Multidiscip Optim 38(1):1–16

    Article  Google Scholar 

  • Noh Y, Choi KK, Lee I (2010) Identification of marginal and joint CDFs using Bayesian method for RBDO. Struct Multidiscip Optim 40(1–6):35–51

    Article  MathSciNet  MATH  Google Scholar 

  • Qiu Z, Yang D, Elishakoff I (2008) Probabilistic interval reliability of structural systems. Int J Solids Struct 45(10):2850–2860

    Article  MATH  Google Scholar 

  • Sentz K, Ferson S (2002) Combination of evidence in Dempster-Shafer theory Vol. 4015. Sandia National Laboratories, Albuquerque

    Book  Google Scholar 

  • Shafer G (1976) A mathematical theory of evidence. Princeton university press, Princeton

    MATH  Google Scholar 

  • Sheynkin Y, Jung M, Yoo P, Schulsinger D, Komaroff E (2005) Increase in scrotal temperature in laptop computer users. J Urol 20(2):452–455

    Google Scholar 

  • Simon C, Weber P, Levrat E (2007) Bayesian networks and evidence theory to model complex systems reliability. J Comput 2(1):33–43

    Article  MathSciNet  Google Scholar 

  • Sklar M (1959) Fonctions de repartition an dimensions et leurs marges. Publ Inst Statist Univ Paris 8:229–231

    MathSciNet  MATH  Google Scholar 

  • Srivastava RK, Deb K, Tulshyan R (2013) An evolutionary algorithm based approach to design optimization using evidence theory. J Mech Des 135(8):1885–1886

    Article  Google Scholar 

  • Stewart DE (2000) Rigid-body dynamics with friction and impact. SIAM Rev 42(1):3–39

    Article  MathSciNet  MATH  Google Scholar 

  • Sun B, Ma W, Zhao H (2014) Decision-theoretic rough fuzzy set model and application. Inf Sci 283(5):180–196

    Article  MathSciNet  MATH  Google Scholar 

  • Tang XS, Li DQ, Zhou CB, Phoon KK, Zhang LM (2013) Impact of copulas for modeling bivariate distributions on system reliability. Struct Saf 44:80–90

    Article  Google Scholar 

  • Thanedar PB, Kodiyalam S (1992) Structural optimization using probabilistic constraints. Struct Optim 4(3–4):236–240

    Article  Google Scholar 

  • Tzvieli A (1990) Possibility theory: an approach to computerized processing of uncertainty. J Assoc Inf Sci Technol 41(2):153–154

    Google Scholar 

  • Warren-Hicks W, Hart A (2010) Application of uncertainty analysis to ecological risks of pesticides. Setac Crc Press, Boca Raton

    Book  Google Scholar 

  • Yao W, Chen X, Ouyang Q, Van Tooren M (2013) A reliability-based multidisciplinary design optimization procedure based on combined probability and evidence theory. Struct Multidiscip Optim 48(2):339–354

    Article  MathSciNet  Google Scholar 

  • Zhang Z, Jiang C, Wang GG, Han X (2015) First and second order approximate reliability analysis methods using evidence theory. Reliab Eng Syst Saf 137:40–49

    Article  Google Scholar 

  • Zhang Z, Jiang C, Ruan XX, Guan FJ (2018) A novel evidence theory model dealing with correlated variables and the corresponding structural reliability analysis method. Struct Multidiscip Optim 57(4):1749–1764. https://doi.org/10.1007/s00158-017-1843-9

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Acknowledgements

Supported by the Major Program of National Natural Science Foundation of China (51490662), the Foundation for Innovative Research Groups of the National Natural Science Foundation of China (51621004), the National Science Fund for Distinguished Young Scholars (51725502), the Natural Science Foundation of Hunan Province of China (2017JJ2012), and the Educational Commission of Hunan Province of China (17A036).

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Huang, Z.L., Jiang, C., Zhang, Z. et al. Evidence-theory-based reliability design optimization with parametric correlations. Struct Multidisc Optim 60, 565–580 (2019). https://doi.org/10.1007/s00158-019-02225-7

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