Sevieri, G;
De Falco, A;
(2020)
Dynamic structural health monitoring for concrete gravity dams based on the Bayesian inference.
Journal of Civil Structural Health Monitoring
10.1007/s13349-020-00380-w.
(In press).
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Abstract
The preservation of concrete dams is a key issue for researchers and practitioners in dam engineering because of the important role played by these infrastructures in the sustainability of our society. Since most of existing concrete dams were designed without considering their dynamic behaviour, monitoring their structural health is fundamental in achieving proper safety levels. Structural Health Monitoring systems based on ambient vibrations are thus crucial. However, the high computational burden related to numerical models and the numerous uncertainties affecting the results have so far prevented structural health monitoring systems for concrete dams from being developed. This study presents a framework for the dynamic structural health monitoring of concrete gravity dams in the Bayesian setting. The proposed approach has a relatively low computational burden, and detects damage and reduces uncertainties in predicting the structural behaviour of dams, thus improving the reliability of the structural health monitoring system itself. The application of the proposed procedure to an Italian concrete gravity dam demonstrates its feasibility in real cases.
| Type: | Article |
|---|---|
| Title: | Dynamic structural health monitoring for concrete gravity dams based on the Bayesian inference |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1007/s13349-020-00380-w |
| Publisher version: | https://doi.org/10.1007/s13349-020-00380-w |
| Language: | English |
| Additional information: | © The Author(s) 2020. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
| Keywords: | Concrete dams · Structural health monitoring · General polynomial chaos expansion · Bayesian inference · Ambient vibrations · Operational modal analysis |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Civil, Environ and Geomatic Eng |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10092304 |
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