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dc.contributor.authorCheynet, Etienne
dc.contributor.authorDaniotti, Nicolo
dc.contributor.authorJakobsen, Jasna Bogunovic
dc.contributor.authorSnæbjörnsson, Jonas Thor
dc.date.accessioned2021-03-12T10:08:32Z
dc.date.available2021-03-12T10:08:32Z
dc.date.created2020-05-29T15:01:30Z
dc.date.issued2020
dc.identifier.issn1545-2255
dc.identifier.urihttps://hdl.handle.net/11250/2733093
dc.description.abstractThe paper introduces a procedure to automatically identify key vehicle characteristics from vibrations data collected on a suspension bridge. The primary goal is to apply a model of the dynamic displacement response of a long‐span suspension bridge to traffic loading, suitable for automatic identification of the vehicle passage over the bridge. The second goal is to improve the estimation of the structural damping of the bridge deck by utilizing the free‐decay displacement response induced by the passing vehicles. The vehicles responsible for a significant bridge vertical response are first identified using an outlier analysis and a clustering algorithm. Utilizing a moving mass model, the equivalent mass and speed of each vehicle, as well as its arrival time, are assessed in a least‐squares sense. The computed vertical displacement response shows a remarkably good agreement with the full‐scale data in terms of peak values and root‐mean‐square values of the displacement histories. The data acquired on the Lysefjord Bridge (Norway) indicate that the contribution of heavy traffic loading to the combined effects of wind and traffic excitation may be significant even at mean wind speeds above 10 m s−1. The critical damping ratios of the most significant vibrational modes of the Lysefjord Bridge are studied for low wind velocities, using the time‐decomposition technique and the traffic‐induced free‐decay response of the bridge deck. The structural damping ratios estimated this way are found to be more accurate than those obtained with an automated covariance‐driven stochastic subspace identification algorithm applied to the same dataset.en_US
dc.language.isoengen_US
dc.publisherWileyen_US
dc.rightsNavngivelse 4.0 Internasjonal*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/deed.no*
dc.titleImproved long-span bridge modeling using data-driven identification of vehicle-induced vibrationsen_US
dc.typeJournal articleen_US
dc.typePeer revieweden_US
dc.description.versionpublishedVersionen_US
dc.rights.holderCopyright 2020 The Authors.en_US
dc.source.articlenumbere2574en_US
cristin.ispublishedtrue
cristin.fulltextoriginal
cristin.qualitycode1
dc.identifier.doihttps://doi.org/10.1002/stc.2574
dc.identifier.cristin1813267
dc.source.journalStructural Control and Health Monitoringen_US
dc.subject.nsiVDP::Bygg-, anleggs- og transportteknologi: 532en_US
dc.subject.nsiVDP::Building, construction and transport technology: 532en_US
dc.subject.nsiVDP::Bygg-, anleggs- og transportteknologi: 532en_US
dc.subject.nsiVDP::Building, construction and transport technology: 532en_US
dc.identifier.citationStructural Control and Health Monitoring. 2020, 27 (9), e2574.en_US
dc.source.volume27en_US
dc.source.issue9en_US


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Navngivelse 4.0 Internasjonal
Except where otherwise noted, this item's license is described as Navngivelse 4.0 Internasjonal