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Influences on Determining Structural Reliability

 Influences on Determining Structural Reliability
Autor(en): ,
Beitrag für IABSE Symposium: Engineering the Future, Vancouver, Canada, 21-23 September 2017, veröffentlicht in , S. 1328-1335
DOI: 10.2749/vancouver.2017.1328
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The influences to investigate when trying to assess the reliability of structures are manifold. Even if all the relevant stochastic parameters and data for establishing a typical reliability model ...
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Bibliografische Angaben

Autor(en): (Hertle Ingenieure, Gräfelfing & BVPI e.V., Berlin, Germany)
(BREHM Bauconsult GmbH, Bensheim & BVPI e.V., Berlin, Germany)
Medium: Tagungsbeitrag
Sprache(n): Englisch
Tagung: IABSE Symposium: Engineering the Future, Vancouver, Canada, 21-23 September 2017
Veröffentlicht in:
Seite(n): 1328-1335 Anzahl der Seiten (im PDF): 8
Seite(n): 1328-1335
Anzahl der Seiten (im PDF): 8
Jahr: 2017
DOI: 10.2749/vancouver.2017.1328
Abstrakt:

The influences to investigate when trying to assess the reliability of structures are manifold. Even if all the relevant stochastic parameters and data for establishing a typical reliability model are known, there are uncertainties to be considered which cannot be covered by probabilistic methods only. In a probabilistic analysis, the basic variables of a design problem, such as e.g. material strength or load effects, have to be modelled as random variables. From there, the failure probability of a mem- ber can be determined by use of methods of reliability analysis.

This relatively straightforward procedure reaches its limits when additional influences like the sim- plification and/or linearization of the design problem, the model-reality-antagonism, the paper- building site transformation or human error are to be considered. Especially the three latter are not tangible by stochastic methods only. As the nature of these issues is predominantly human, individ- ual competence or its opposite deprive a purely mathematical approach to achieving sufficient reli- ability.

Stichwörter:
Zuverlässigkeit Menschliche Fehler Modellunsicherheit