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Accounting for spatial variability in partial factor based design verifications

 Accounting for spatial variability in partial factor based design verifications
Autor(en): ORCID, ORCID
Beitrag für IABSE Congress: Structural Engineering for Future Societal Needs, Ghent, Belgium, 22-24 September 2021, veröffentlicht in , S. 1805-1812
DOI: 10.2749/ghent.2021.1805
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Traditional design and assessment approaches usually assume that e.g. material properties and environmental influences are uniform in space. However, it is well-known that such parameters can show ...
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Bibliografische Angaben

Autor(en): ORCID (Department of Structural Engineering and Building Materials, Ghent University, Ghent, Belgium)
ORCID (Department of Structural Engineering and Building Materials, Ghent University, Ghent, Belgium)
Medium: Tagungsbeitrag
Sprache(n): Englisch
Tagung: IABSE Congress: Structural Engineering for Future Societal Needs, Ghent, Belgium, 22-24 September 2021
Veröffentlicht in:
Seite(n): 1805-1812 Anzahl der Seiten (im PDF): 8
Seite(n): 1805-1812
Anzahl der Seiten (im PDF): 8
DOI: 10.2749/ghent.2021.1805
Abstrakt:

Traditional design and assessment approaches usually assume that e.g. material properties and environmental influences are uniform in space. However, it is well-known that such parameters can show considerable spatial variability. Furthermore, it has been shown that such spatial variability can significantly influence structural reliability. One way to account for spatial variability is by means of random fields. However, the use of such advanced calculations has not found its way to everyday engineering practice. Therefore, a methodology is developed in order to include spatial variability in the partial factor method in a way which is consistent with the current Eurocode format for design. This is done by introducing a separate partial factor which depends on the correlation length and the variability of the parameter under consideration. As such, an easy-to-use graph is generated, which can be applied in practice for the adjustment of partial factors to take into account spatial correlation. Finally, the proposed approach is validated by means of full-probabilistic calculations.

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