By Robby Caspeele, Luc Taerwe, Dirk Proske
This e-book offers the complaints of the 14th foreign Probabilistic Workshop that used to be held in Ghent, Belgium in December 2016. Probabilistic equipment are presently of an important value for study and advancements within the box of engineering, which face demanding situations provided through new fabrics and applied sciences and quickly altering societal wishes and values. modern wishes concerning, for instance, performance-based layout, service-life layout, life-cycle research, product optimization, evaluation of latest constructions and structural robustness provide upward push to new advancements in addition to exact and essentially acceptable probabilistic and statistical engineering easy methods to aid those advancements. those court cases are a invaluable source for a person attracted to modern advancements within the box of probabilistic engineering applications.
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Extra info for 14th International Probabilistic Workshop
Imposed load, the dimensions of the structural elements or material characteristics) are deﬁned as random variables with a certain probability distribution. Usually, the probability of structural failure is required. Due to the complexity of M. Šmídová (✉) ⋅ M. cz M. cz © Springer International Publishing AG 2017 R. Caspeele et al. 1007/978-3-319-47886-9_4 45 46 M. Šmídová and M. Vořechovský the problem, it is often impossible to solve it analytically (even when the inputs have a given joint probability distribution function).
Random House, New York Thomas GAN (2015) Risk and reliability in the design of arctic offshore structures. Proc. POAC’15. Trondheim, Norway, 14–18 June 2015 Part II Structural Reliability Methods and Statistical Approaches Extrapolation, Invariance, Geometry and Subset Sampling K. Breitung Abstract In the last years the subset sampling method has often been used in reliability problems as a tool for calculating very small probabilities. The method extrapolates from an initial Monte Carlo estimate for the probability content of a failure domain found by a suitable higher level of the original limit state function.
6 SuS for the LSF g(u1 , u2 ) = ???? 2 ∕2 − |u1 ⋅ u2 | 20 0 6 -20 -3 3 0 6 -6 -6 -3 0 u1 3 6 3 -3 0 u2 -3 -6 -6 (b) SuS detects three beta points u1 Extrapolation, Invariance, Geometry and Subset Sampling 41 This might lead to a systematic underestimation of the failure probability when not all beta points are found. If now several runs are combined, there will still be a bias, the failure probability will be underestimated. It is unclear to the author how to get a good estimator of the failure probability without making some sort of geometric analysis similar to FORM/SORM by identifying the domains with high probability content in the failure domain.
14th International Probabilistic Workshop by Robby Caspeele, Luc Taerwe, Dirk Proske