Fuzzy analysis as alternative to stochastic methods — theoretical aspects

Moller B., Beer M., Liedscher M.

A realistic and reliable numerical simulation demands suitable computational models and applicable data models for the structural design parameters. Structural design parameters are in general non-deterministic, i.e. uncertain. The choice of an appropriate uncertainty model for describing selected structural design parameters depends on the characteristic of the available information. Besides the most often used probabilistic models and the related stochastic analysis techniques newer uncertainty models offer the chance taking account of non-stochastic uncertainty that frequently appears in engineering problems. The uncertainty model fuzziness and the algorithm of the fuzzy structural analysis is presented in this paper. The uncertainty quantification of real-world data for the uncertainty models fuzziness and randomness is discussed by the way of examples. The differences and advantages of uncertainty models randomness and fuzziness and its simulation techniques are addressed.

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Table of contents :
Uncertainty model fuzziness……Page 2
Uncertainty Quantification……Page 4
Information type III — single uncertain measured value ……Page 5
Randomness……Page 6
-level discretization of fuzzy values……Page 7
-level optimization……Page 8
Modified evolution strategy……Page 9
Robustness investigations……Page 11
Fuzzy Cluster Design……Page 12
Conclusions……Page 13