Research for Computing the Structure Reliability Index

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The paper discusses general theoretical and practical aspects of the emerging probabilistic and non-probabilistic approaches for uncertainty treatment in finite element analysis. Based on rough set theory, a new methodology for structural reliability computation is presented. The uncertain parameters of structures are expressed by rough variables. The structure reliability index is computed by rough function and metric. It is valid, and efficient in physics and geometry. Examples of practical application are given.

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1464-1468

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July 2014

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© 2014 Trans Tech Publications Ltd. All Rights Reserved

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[1] Lin PL, Kiureghian AD, Optimization algorithms for structural reliability[J]. Structural Safety, 1991. 120(9); 161-177.

DOI: 10.1016/0167-4730(91)90041-7

Google Scholar

[2] Qiu ZP, Mueller PC, Frommer A. The new non-probabilistic criterion of failure for dynamical systems based on convex models. Math Comput Model2004; 40: 201-15.

Google Scholar

[3] D. Moens, D. Vandepitte, Recent advances in non-probabilistic approaches for non-deterministic dynamic finite element analysis, Arch. Comput. Meth. Engrg. 13 (2006) 389-464.

DOI: 10.1007/bf02736398

Google Scholar

[4] B. Moller, M. Beer, Engineering computation under uncertainty-capabilities of non-traditional models, Comput. Struct. 86 (2007) 1024-1041.

DOI: 10.1016/j.compstruc.2007.05.041

Google Scholar

[5] Z. Pawlak. Rough sets. rough function and rough calculus. In: Rough-Fuzzy Hybridization: A New Trend in Decision-Making S.K. Pal.A. Skowron. eds. ). singapore: SPringer-Verlag. (1999): 99-109.

DOI: 10.1007/978-94-011-3534-4_8

Google Scholar

[6] J. Ruokolainen, Constructive Nonstandard Analysis Without Actual Infinity. Porthania: Helsinki, (2004).

Google Scholar

[7] J. Mycielski, Analysis without actual infinity. Journal of Symbolic Logic, 46(1981): 625-33.

DOI: 10.2307/2273760

Google Scholar

[8] R. Chuaqui, P. suppes. Free-variable axiomatic foundations of infinitesimal analysis: a Fragment with finitary consistency proof. Joumal of Symbolic Logic, 60(l995): 122-159.

DOI: 10.2307/2275512

Google Scholar

[9] B. KuiPers. Qualitative simulation. Artificial Intelligence, (29)1986: 289-338.

Google Scholar

[10] H. Werthner. Qualitative Reasoning-Modeling and the Generation of Behavior. Singapore: SPringer-Verlag, (1994).

Google Scholar

[11] D.G. Li, X.E. Chen. Improvement of definitions of one-element rough funetion and binary rough function and investigation of their mathematical analysis properties. Journal of Shanxi University, 23(4)(2000): 318-321. (inChinese).

Google Scholar