By Wolfgang Fellin, Heimo Lessmann, Michael Oberguggenberger, Robert Vieider
This quantity comprehensively addresses the problem of uncertainty in civil engineering, from layout to development. present engineering perform frequently leaves uncertainty matters apart, although new clinical instruments were constructed some time past a long time that permit a rational description of uncertainties of every kind, from version uncertainty to info uncertainty. it's the goal of this quantity to take a severe examine present engineering probability suggestions which will bring up expertise of uncertainty in numerical computations, shortcomings of a strictly probabilistic defense thought, geotechnical types of failure and their development implications, real building, and obligation. additionally, some of the new strategies for modelling uncertainty are defined. The ebook is as a result a collaborate attempt of mathematicians, engineers and development managers who met frequently in a post-graduate seminar on the college of Innsbruck in past times years.
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Additional info for Analyzing Uncertainty in Civil Engineering
Does that mean that a 36 Michael Oberguggenberger and Wolfgang Fellin speciﬁc dam under consideration will fail once in the next 1700 years? Of course, for an assessment of the individual failure rate, individual data like local yearly precipitation averages etc. are needed. If these data are lacking, the collective frequency is a highly questionable estimate for the individual frequency. Often only a mixture of individual and collective data is available. The necessity of estimating the individual risk from the collective risk is a well known problem for insurance premium calculations, especially in property and casualty insurance.
This input in turn is processed numerically and should deliver an output describing the behavior of the structure under investigation plus an assessment of the output uncertainty. Thus, models of the data uncertainty should reﬂect and incorporate the level of information available on the data and, second, must be able to propagate it through numerical computations and deliver an output whose uncertainty is formulated in the same terms. In addition, the uncertainty models need correspondence rules themselves, that is, well-deﬁned semantics.
The question of parameter variability is intricate enough. It can be attributed to a number of causes. g. in soil mechanics, when the laboratory test destroys the history of a material with memory). There are ﬂuctuations due to spatial inhomogeneity, errors made by assigning parameter status to state variables; and variability arising from the fact that parameters have to carry the burden of model insuﬃciency. The available information on data uncertainty may range from frequency distributions obtained from large samples, values from small samples or single measurements, interval bounds, to experts’ point estimates and educated guesses from experience.
Analyzing Uncertainty in Civil Engineering by Wolfgang Fellin, Heimo Lessmann, Michael Oberguggenberger, Robert Vieider