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Improved long‐span bridge modeling using data‐driven identification of vehicle‐induced vibrations

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dc.contributor Háskólinn í Reykjavík
dc.contributor Reykjavik University
dc.contributor.author Cheynet, Etienne
dc.contributor.author Daniotti, Nicolò
dc.contributor.author Jakobsen, Jasna Bogunović
dc.contributor.author Snaebjornsson, Jonas Thor
dc.date.accessioned 2020-12-01T14:43:38Z
dc.date.available 2020-12-01T14:43:38Z
dc.date.issued 2020-06
dc.identifier.citation Cheynet, E., Daniotti, N., Jakobsen, J. B., & Snaebjornsson, J. (2020). Improved long-span bridge modeling using data-driven identification of vehicle-induced vibrations. Structural Control & Health Monitoring, 27(9), e2574. https://doi.org/10.1002/stc.2574
dc.identifier.issn 1545-2255
dc.identifier.issn 1545-2263 (eISSN)
dc.identifier.uri https://hdl.handle.net/20.500.11815/2265
dc.description Publisher's version (útgefin grein)
dc.description.abstract The 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.
dc.description.sponsorship Norwegian Public Roads Administration
dc.format.extent e2574
dc.language.iso en
dc.publisher Wiley
dc.relation.ispartofseries Structural Control and Health Monitoring;27(9)
dc.rights info:eu-repo/semantics/openAccess
dc.subject Mechanics of Materials
dc.subject Civil and Structural Engineering
dc.subject Building and Construction
dc.subject Ambient vibrations monitoring
dc.subject Full-scale measurements
dc.subject Long-span bridges
dc.subject Moving mass
dc.subject Traffic loading
dc.subject Aflfræði
dc.subject Byggingarverkfræði
dc.subject Burðarþolsfræði
dc.subject Mannvirkjagerð
dc.subject Titringur
dc.subject Umhverfisvöktun
dc.subject Eftirlit
dc.subject Mælingar
dc.subject Hengibrýr
dc.subject Umferðarmál
dc.subject Þyngd
dc.subject Norway
dc.subject Noregur
dc.title Improved long‐span bridge modeling using data‐driven identification of vehicle‐induced vibrations
dc.type info:eu-repo/semantics/article
dcterms.license This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
dc.description.version "Peer Reviewed"
dc.identifier.doi 10.1002/stc.2574
dc.contributor.department Verkfræðideild (HR)
dc.contributor.department Department of Engineering (RU)
dc.contributor.school Tæknisvið (HR)
dc.contributor.school School of Technology (RU)

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