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Erschienen in: Structural and Multidisciplinary Optimization 1/2018

13.01.2018 | RESEARCH PAPER

Safety life analysis under the required failure possibility constraint for structure involving fuzzy uncertainty

verfasst von: Chongqing Fan, Zhenzhou Lu, Yan Shi

Erschienen in: Structural and Multidisciplinary Optimization | Ausgabe 1/2018

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Abstract

For ensuring safety service of structure under fuzzy uncertainty, some efficient methods are proposed for analyzing safety life under the constraint that the actual time-dependent failure possibility (TDFP) less than the target failure possibility. The direct dichotomy method is firstly established to solve the safety life. Since the direct dichotomy method needs to iterate the TDFP at all searching points of the safety life and results in large computational cost, the equivalent constraint method (ECM) is established to solve the safety life. In ECM, the equivalence between the constraint of the actual TDFP and the equivalent constraint of the lower boundary of the minimum of the response function is strictly proved by the reduction to absurdity. By equivalently replacing the constraint of the actual TDFP with that of the lower boundary of the minimum of the output response, the computational cost for estimating the safety life is greatly reduced. Two solutions of the safety life based on the ECM are established. One is ECM based dichotomy method. The other is ECM based Newton method, where a simplified derivative solution is deduced to reduce the computational cost. After the implementations of solving the safety life are given in detail, several examples are used to verify the rationality of the established safety life analysis model and the efficiency of the methods for solving safety life.

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Metadaten
Titel
Safety life analysis under the required failure possibility constraint for structure involving fuzzy uncertainty
verfasst von
Chongqing Fan
Zhenzhou Lu
Yan Shi
Publikationsdatum
13.01.2018
Verlag
Springer Berlin Heidelberg
Erschienen in
Structural and Multidisciplinary Optimization / Ausgabe 1/2018
Print ISSN: 1615-147X
Elektronische ISSN: 1615-1488
DOI
https://doi.org/10.1007/s00158-017-1896-9

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