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 language | English |
|---|---|
| Title of host publication | Proceedings of ISMA2022 International Conference on Noise and Vibration Engineering |
| Number of pages | 15 |
| Publication status | Published - 2022 |
| Event | ISMA2022, International Conference on Noise and Vibration Engineering - Leuven, Belgium Duration: 12 Sept 2022 → 14 Sept 2022 Conference number: 30 |
Conference
| Conference | ISMA2022, International Conference on Noise and Vibration Engineering |
|---|---|
| Abbreviated title | ISMA2022 |
| Country/Territory | Belgium |
| City | Leuven |
| Period | 12/09/22 → 14/09/22 |
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-careOtherwise 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.