Data-driven modeling of modal parameters of long-span bridges under environmental and operational variation
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Détails bibliographiques
| Auteur(s): |
Sunjoong Kim
(University of Illinois at Urbana-Champaign, IL, USA)
Billie F. (Jr) Spencer (University of Illinois at Urbana-Champaign, IL, USA) Ho-Kyung Kim (University of Illinois at Urbana-Champaign, IL, USA) Se-Jin Kim (University of Illinois at Urbana-Champaign, IL, USA) Doyun Hwang (DM Engineering, Seoul, Korea) |
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| Médium: | papier de conférence | ||||
| Langue(s): | anglais | ||||
| Conférence: | IABSE Conference: Risk Intelligence of Infrastructures, Seoul, South Korea, 9-10 November 2020 | ||||
| Publié dans: | IABSE Conference Seoul 2020 | ||||
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| Page(s): | 170-173 | ||||
| Nombre total de pages (du PDF): | 4 | ||||
| DOI: | 10.2749/seoul.2020.170 | ||||
| Abstrait: |
This study develops the multivariate model of modal parameters under the high variability of structural responses and environmental conditions. The automated operational modal analysis procedure is implemented by synthesizing the algorithms of output-only system identification and density-based clustering algorithm. The Gaussian Process Regression is applied to accumulated modal estimates as well as corresponding environmental/operational conditions for examining the high degree of nonlinear variation in these monitoring data. The performance of the developed model is demonstrated for one-to-one regressions for multivariate structural health monitoring outputs in the presence of environmental and operational variation. |
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