Wind load estimation on bridges using latent force models enriched with environmental data

Oyvind W. Petersen, Ole Øiseth, E. Lourens

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

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Abstract

An application of inverse force identification of wind loads on bridges is presented. This contribution explores the extension of latent force models (LFMs) in Kalman filters. Specifically, it is shown how LFMs can be enriched with environmental information from wind data in order to realistically reflect the underlying physics behind the wind loads. This is demonstrated in a case study of a long-span suspension bridge equipped with a structural monitoring system, where an extensive data set of 103 time series of 30-minute events is used. The results show that the estimation of modal wind loads and modal response states is stable. Moreover, optimization of LFMs with maximum likelihood methods shows that optimized solutions match well with the actual (measured) wind load conditions. The work elevates the prospects of physics-informed LFMs with interpretable hyperparameters.
Original languageEnglish
Title of host publicationProceedings of ISMA2022 International Conference on Noise and Vibration Engineering
Number of pages15
Publication statusPublished - 2022
EventISMA2022, International Conference on Noise and Vibration Engineering - Leuven, Belgium
Duration: 12 Sept 202214 Sept 2022
Conference number: 30

Conference

ConferenceISMA2022, International Conference on Noise and Vibration Engineering
Abbreviated titleISMA2022
Country/TerritoryBelgium
CityLeuven
Period12/09/2214/09/22

Bibliographical note

Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care
Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

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