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dc.contributor.author
Wagner, Paul-Remo
dc.contributor.author
Marelli, Stefano
dc.contributor.author
Papaioannou, Iason
dc.contributor.author
Straub, Daniel
dc.contributor.author
Sudret, Bruno
dc.date.accessioned
2021-07-05T12:21:21Z
dc.date.available
2021-07-05T11:56:42Z
dc.date.available
2021-07-05T12:21:21Z
dc.date.issued
2021-06-29
dc.identifier.uri
http://hdl.handle.net/20.500.11850/493033
dc.identifier.doi
10.3929/ethz-b-000493033
dc.description.abstract
The assessment of structural reliability under uncertainties is a common problem in structural engineering. In a probabilistic setting, it is formalized by determining the failure probability of a system defined as the probability that the so-called limit-state function takes non-positive values. In recent years, considerable efforts have been devoted to developing algorithms that efficiently determine the failure probability. A powerful class of algorithms for reliability problems involving computationally demanding limit-state functions is the class of active learning reliability methods. These methods adaptively enhance an approximation of the limit state function resulting in considerable performance increase compared to more traditional stochastic simulation techniques. We recently proposed a new active learning reliability method based on the stochastic spectral embedding surrogate modeling technique [1]. It is based on adaptively constructing residual local spectral expansions in partitions of the parameter space with an adaptively enriched experimental design. The partitioning and enrichment rules exploit information about the local approximation accuracy and proximity to the limit state surface. In this contribution, we apply this technique to a reliability problem of a five story structural frame with 21 uncertain and mutually dependent input parameters. The frame is analyzed with the finite element method and has a reference failure probability in the order of 1e-6. With our proposed method, we consistently compute this failure probability with less than 200 evaluations of the original forward model.
en_US
dc.format
application/pdf
en_US
dc.language.iso
en
en_US
dc.rights.uri
http://creativecommons.org/licenses/by-nc/4.0/
dc.subject
Stochastic spectral embedding
en_US
dc.subject
RELIABILITY (ENGINEERING)
en_US
dc.subject
RARE EVENTS (PROBABILITY THEORY)
en_US
dc.title
An active learning reliability algorithm based on local spectral residual expansions of the limit state function
en_US
dc.type
Other Conference Item
dc.rights.license
Creative Commons Attribution-NonCommercial 4.0 International
ethz.pages.start
U 19045
en_US
ethz.size
10 p. accepted version
en_US
ethz.version.deposit
acceptedVersion
en_US
ethz.event
4th International Conference on Uncertainty Quantification in Computational Sciences and Engineering (UNCECOMP 2021)
en_US
ethz.event.location
Athens, Greece
en_US
ethz.event.date
June 28–30, 2021
en_US
ethz.notes
Conference lecture held on June 29, 2021
en_US
ethz.grant
Efficient Computational Bayesian Inversion for Risk and Uncertainty Quantification in Engineering and the Sciences
en_US
ethz.publication.status
published
en_US
ethz.leitzahl
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02115 - Dep. Bau, Umwelt und Geomatik / Dep. of Civil, Env. and Geomatic Eng.::02605 - Institut für Baustatik u. Konstruktion / Institute of Structural Engineering::03962 - Sudret, Bruno / Sudret, Bruno
en_US
ethz.leitzahl.certified
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02115 - Dep. Bau, Umwelt und Geomatik / Dep. of Civil, Env. and Geomatic Eng.::02605 - Institut für Baustatik u. Konstruktion / Institute of Structural Engineering::03962 - Sudret, Bruno / Sudret, Bruno
en_US
ethz.grant.agreementno
ETH-44 17-1
ethz.grant.fundername
ETHZ
ethz.grant.funderDoi
10.13039/501100003006
ethz.grant.program
ETH Grants
ethz.date.deposited
2021-07-05T11:56:48Z
ethz.source
FORM
ethz.eth
yes
en_US
ethz.availability
Open access
en_US
ethz.rosetta.installDate
2021-07-05T12:21:27Z
ethz.rosetta.lastUpdated
2022-03-29T10:16:01Z
ethz.rosetta.versionExported
true
ethz.COinS
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