Bayesian structural identification of a long suspension bridge considering temperature and traffic load effects
- Submitting institution
-
University of Exeter
- Unit of assessment
- 12 - Engineering
- Output identifier
- 5119
- Type
- D - Journal article
- DOI
-
10.1177/1475921718794299
- Title of journal
- Structural Health Monitoring
- Article number
- -
- First page
- 1310
- Volume
- 18
- Issue
- 4
- ISSN
- 1475-9217
- Open access status
- Compliant
- Month of publication
- September
- Year of publication
- 2018
- URL
-
-
- Supplementary information
-
https://journals.sagepub.com/doi/figure/10.1177/1475921718794299
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
-
5
- Research group(s)
-
D - Dynamics and Control
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- Research funded by EP/F035403/1 (£448k) lead to many outputs including this, which won the best paper award in the conference of Structural Health Monitoring of Intelligent Infrastructure 2017. It is the first demonstration of Bayesian structural identification on a real operational structure overcoming modelling errors. The innovative mathematical framework from the field of Statistics has been propagated and further developed into civil engineering by the authors for the first time.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -