Parameterized Logistic Models for Bridge Inspection and Maintenance Scheduling
Publication: Journal of Bridge Engineering
Volume 26, Issue 10
Abstract
Proper inspection and maintenance schedules are integral to bridge functionality and safety; however, they also pose challenges in light of budget and resource limitations. As such, bridge management systems (BMSs) are always concerned with finding the best deterioration and maintenance models to optimize scheduling. The current work proposes parameterized logistic models that can capture bridge deterioration and the effect of maintenance interventions. Given a handful of easy-to-track bridge parameters, such as age, time since last major maintenance, and location, the proposed models predict the probability of a bridge (or group of bridges) to need repair throughout its service life. Combined with the appropriate probability threshold, obtained from life-cycle cost analysis, this allows for the optimization of inspection frequency and helps in maintenance planning. The results indicate that the proposed models predict the bridge condition more accurately compared to the Markov Chains models adopted by many North American BMSs. Finally, the application of the parameterized logistic models is demonstrated through a case study.
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Acknowledgments
The authors gratefully acknowledge the support from the Ontario Early Researcher Award and start-up fund provided by the Faculty of Engineering at McMaster University. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the sponsor. The authors also thankfully acknowledge Mr. Hao Zhang, P.Eng. (City of Toronto), for his constructive feedback on bridge inspection and maintenance practices in the Province of Ontario, Canada.
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Information & Authors
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Published In
Journal of Bridge Engineering
Volume 26 • Issue 10 • October 2021
Copyright
© 2021 American Society of Civil Engineers.
History
Received: Oct 27, 2020
Accepted: Jun 11, 2021
Published online: Jul 23, 2021
Published in print: Oct 1, 2021
Discussion open until: Dec 23, 2021
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