Data-driven vibration prognosis using multiple-input finite impulse response filters and application to railway-induced vibration of timber buildings

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Hofmeister, B.; Wernitz, S.; Grießmann, T.; Hübler, C.; Rolfes, R.: Data-driven vibration prognosis using multiple-input finite impulse response filters and application to railway-induced vibration of timber buildings. Hannover : Institutionelles Repositorium der Leibniz Universität Hannover, 2022, 49 S. DOI:

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Sum total of downloads: 174

With this paper, we present a vibration prognosis method based on finite impulseresponses. The impulse responses are identified using measurement data from anexisting building and consider a multiple-input/multiple-output topology.Vibration prognosis in urban buildings is becoming increasingly important, since moreand more buildings are being constructed close to urban infrastructure. Combined withmodern and ecological choices of building materials and the low vibration levelsrequired by current standards, serviceability in terms of structural dynamics becomesan issue. Sources of vibration in urban settings include railway and metro lines as wellas road traffic. This work focuses on a method especially suited to the three-dimensional vibration state encountered in modern timber buildings. Under theassumption of linear time-invariant structural dynamic behaviour, we develop a time-domain identification approach. The novelties of this contribution lie in the formulationof a numerically efficient method to identify multiple-input finite impulse response filtersand its application to measurement data of a timber building.We validate this data-driven prognosis method using measurement data from abuilding constructed from cross-laminated timber, considering the three-dimensionalvibration behaviour. The accuracy and limitations are assessed using railway-inducedvibrations as a typical source of disturbance by infrastructure. We show that vibrationdata from the foundation can be used for effective prognosis of the top floor slabsconsidering train types not included in the identification data set. Based on theprognosis method, a virtual sensor concept for long-term monitoring is presented.
License of this version: CC BY 3.0 DE
Document Type: Article
Publishing status: submittedVersion
Issue Date: 2022
Appears in Collections:Fakultät für Bauingenieurwesen und Geodäsie

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pos. country downloads
total perc.
1 image of flag of Germany Germany 99 56.90%
2 image of flag of United States United States 25 14.37%
3 image of flag of No geo information available No geo information available 6 3.45%
4 image of flag of Netherlands Netherlands 5 2.87%
5 image of flag of Czech Republic Czech Republic 4 2.30%
6 image of flag of Russian Federation Russian Federation 3 1.72%
7 image of flag of France France 3 1.72%
8 image of flag of China China 3 1.72%
9 image of flag of Canada Canada 3 1.72%
10 image of flag of Belgium Belgium 3 1.72%
    other countries 20 11.49%

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