By Wolfgang Fellin (auth.), a.o. Univ.-Prof. Dipl.-Ing. Dr. Wolfgang Fellin, em. Univ.-Prof. Dipl.-Ing. Heimo Lessmann, a.o. Univ.-Prof. Dr. Michael Oberguggenberger, Dipl.-Ing. Robert Vieider (eds.)
This quantity addresses the problem of uncertainty in civil engineering from layout to building. mess ups do take place in perform. Attributing them to a residual method chance or a defective execution of the venture doesn't safely hide the diversity of explanations. a more in-depth scrutiny of the followed layout, the engineering version, the information, the soil-construction-interaction and the version assumptions is needed. often, the uncertainties in preliminary and boundary stipulations are plentiful. present engineering perform usually leaves those matters apart, even though new medical instruments were built long ago many years that let a rational description of uncertainties of every kind, from version uncertainty to info uncertainty. it's the target of this quantity to have a severe examine present engineering possibility strategies in an effort to increase understanding of uncertainty in numerical computations, shortcomings of a strictly probabilistic safeguard thought, geotechnical types of failure mechanisms and their implications for building administration, execution, and the juristic query of accountability. moreover, a few of the new tactics for modelling uncertainty are defined. The e-book is as a result of the a collaborate attempt of mathematicians, engineers and development managers who met frequently in a submit graduate seminar on the collage of Innsbruck up to now years.
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Additional info for Analyzing Uncertainty in Civil Engineering
Geotechnik, Sonderheft:4–31, 1999. The mathematics of uncertainty: models, methods and interpretations Michael Oberguggenberger Institut f¨ ur Technische Mathematik, Geometrie und Bauinformatik, Universit¨at Innsbruck Summary. This article discusses various mathematical theories that have been put forth as tools for modelling uncertainty, among them probability, interval arithmetic, random sets, and fuzzy sets. After recalling the deﬁnitions, we stress their interpretations (semantics), axioms, interrelations as well as numerical procedures and demonstrate how the concepts are applied in practice.
For the sake of simplicity we assume in this example tan ϕ to be homogeneously distributed in space, but otherwise a random variable with statistical ﬂuctuations. Griffiths and Fenton  showed in the case of a bearing capacity problem in an undrained soil, that the spatial distribution of the undrained cohesion cu leads to a reduced capacity factor Nc , because the failure plane tends to ﬁnd its way through the weakest zones. The failure mechanism becomes asymmetric. The characteristic value of the cohesion eﬀecting the occurrence of the limit state is therefore not the spatial mean value, it is somewhat less, depending on the practically unknown spatial correlation length.
84 Characteristic values: When statistical data are available (as in our case), it is commonly agreed upon [19, 20] that the lower boundary of a (1-α)-conﬁdence interval for the mean value of ν = tan ϕ should be taken as√the “cautious estimate” of the friction coeﬃcient νk = tan ϕk : νk = ν¯ − tn−1,α/2 sν / n, where tn−1,α/2 is the (1-α/2)-percentile of the Student t-distribution with (n − 1) degrees of freedom. 1 is used, that is a 90% conﬁdence interval. 7 Fig. 3. Fits, cumulative distribution functions interval.
Analyzing Uncertainty in Civil Engineering by Wolfgang Fellin (auth.), a.o. Univ.-Prof. Dipl.-Ing. Dr. Wolfgang Fellin, em. Univ.-Prof. Dipl.-Ing. Heimo Lessmann, a.o. Univ.-Prof. Dr. Michael Oberguggenberger, Dipl.-Ing. Robert Vieider (eds.)