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dc.date.accessioned2023-03-09T16:07:53Z
dc.date.available2023-04-29T22:45:55Z
dc.date.created2022-10-13T09:00:13Z
dc.date.issued2022
dc.identifier.citationHafver, Andreas Agrell, Christian Vanem, Erik . Environmental contours as Voronoi cells. Extremes. 2022, 25(3), 451-486
dc.identifier.urihttp://hdl.handle.net/10852/101081
dc.description.abstractEnvironmental contours are widely used as basis for design of structures exposed to environmental loads. The basic idea of the method is to decouple the environmental description from the structural response. This is done by establishing an envelope of joint extreme values representing critical environmental conditions, such that any structure tolerating loads on this envelope will have a failure probability smaller than a prescribed value. Specifically, given an n-dimensional random variable X and a target probability of failure pe, an environmental contour is the boundary of a set B⊂Rn with the following property: For any failure set F⊂Rn, if F does not intersect the interior of B, then the probability of failure, P(X∈F), is bounded above by pe. We work under the assumption that failure sets are convex, which is relevant for many real-world applications. In this paper, we show that such environmental contours may be regarded as boundaries of Voronoi cells. This geometric interpretation leads to new theoretical insights and suggests a simple novel construction algorithm that guarantees the desired probabilistic properties. The method is illustrated with examples in two and three dimensions, but the results extend to environmental contours in arbitrary dimensions. Inspired by the Voronoi-Delaunay duality in the numerical discrete scenario, we are also able to derive an analytical representation where the environmental contour is considered as a differentiable manifold, and a criterion for its existence is established.
dc.languageEN
dc.titleEnvironmental contours as Voronoi cells
dc.title.alternativeENEngelskEnglishEnvironmental contours as Voronoi cells
dc.typeJournal article
dc.creator.authorHafver, Andreas
dc.creator.authorAgrell, Christian
dc.creator.authorVanem, Erik
cristin.unitcode185,15,13,25
cristin.unitnameStatistikk og Data Science
cristin.ispublishedtrue
cristin.fulltextoriginal
cristin.fulltextpostprint
cristin.qualitycode1
dc.identifier.cristin2061003
dc.identifier.bibliographiccitationinfo:ofi/fmt:kev:mtx:ctx&ctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.jtitle=Extremes&rft.volume=25&rft.spage=451&rft.date=2022
dc.identifier.jtitleExtremes
dc.identifier.volume25
dc.identifier.issue3
dc.identifier.startpage451
dc.identifier.endpage486
dc.identifier.doihttps://doi.org/10.1007/s10687-022-00437-7
dc.type.documentTidsskriftartikkel
dc.type.peerreviewedPeer reviewed
dc.source.issn1386-1999
dc.type.versionAcceptedVersion


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