Probabilistic Life-Cycle Connectivity Assessment of Transportation Networks Using Deep Learning
Publication: Journal of Bridge Engineering
Volume 28, Issue 9
Abstract
Bridges and pavements are two major infrastructure components of a transportation network providing mobility of freight and commodities for economic vitality and access to a range of users as social benefits. However, the lack of a comprehensive infrastructure management system incorporating bridges and pavements inhibits accurate performance prediction, optimal maintenance actions, and the associated use of shrinking budgets. This paper presents an integrated probabilistic life-cycle connectivity framework for the performance analysis of transportation networks containing bridges and asphalt pavements by considering flexural and shear failure modes for prestressed concrete and steel bridges and four failure modes, including international roughness index, rut depth, alligator cracking, and transverse cracking, for asphalt pavements. In this framework, neural network–based deep learning models are used to assess the probabilistic performance of transportation networks and to provide guidance for the associated maintenance strategies. An existing transportation network consisting of bridges and asphalt pavement segments is selected to investigate its life-cycle connectivity reliability and component importance using the matrix-based system reliability method. Results show that the consideration of asphalt pavement failure probability has a significant effect on the probability of transportation network connectivity.
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Data Availability Statement
Some or all data, models, or codes used during the study were provided by a third party (LTPP database and bridge information). Direct requests for these materials may be made to the provider as indicated in the Acknowledgments.
Acknowledgments
The authors are grateful to the publicly accessible database provided by the LTPP program supported by the FHWA and bridge information provided by the Pennsylvania Department of Transportation (PennDOT).
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Published In
Journal of Bridge Engineering
Volume 28 • Issue 9 • September 2023
Copyright
© 2023 American Society of Civil Engineers.
History
Received: Oct 26, 2022
Accepted: May 30, 2023
Published online: Jul 13, 2023
Published in print: Sep 1, 2023
Discussion open until: Dec 13, 2023
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