Statistical Analysis of Bridge Management System Inspection Data
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Détails bibliographiques
| Auteur(s): |
Ahmed M. Abdelmaksoud
(McMaster University)
Tracy C. Becker (UC Berkeley University) Georgios P. Balomenos 
(McMaster University)
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| Médium: | papier de conférence | ||||
| Langue(s): | anglais | ||||
| Conférence: | IABSE Congress: The Evolving Metropolis, New York, NY, USA, 4-6 September 2019 | ||||
| Publié dans: | The Evolving Metropolis | ||||
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| Page(s): | 897-901 | ||||
| Nombre total de pages (du PDF): | 5 | ||||
| DOI: | 10.2749/newyork.2019.0897 | ||||
| Abstrait: |
Bridge inspection is essential for sustaining safe and well-performing transportation networks. The Ministry of Transportation of Ontario (MTO) bi-yearly inspects over 2800 bridges in Ontario, Canada. Then assigns each bridge a Bridge Condition Index (BCI) representing its performance level and required rehabilitation.As this is a time and resources consuming practice, this study explores the BCI trends which can allow a better control on inspection and maintenance scheduling. First, statistical analysis is conducted to identify the correlation of the bridge parameters with the BCI. The analysis reveals that the main parameters associated with BCI are bridge age, and time since last major and minor maintenances. Then, multivariate regression analysis is performed to establish a BCI prediction equation function of these parameters. The proposed framework can supplement existing practices for smarter inspection and maintenance scheduling. |
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| Mots-clé: |
ponts
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