Quantifying the Value of Satellite-Based Pavement Monitoring in Partially Observable Stochastic Environments
Publication: Journal of Computing in Civil Engineering
Volume 37, Issue 3
Abstract
Accurate and timely assessment of pavement condition is critical to determine optimal maintenance plans. Due to the high costs of ground-based inspections, agencies often limit their monitoring to major roads and the condition of some elements of the road network remains unknown. Satellites, capable of rapidly collecting information over wide areas, can be a cost-effective alternative to monitor pavement condition. This wide coverage, however, comes at the expense of lower levels of accuracy. The objective of this study is to quantify the value of satellite-based information in optimal inspection and maintenance strategies. To account for the uncertainties associated with satellite observations, the system was modeled as a partially observable Markov decision process (POMDP) to determine optimal life-cycle inspection and maintenance policies. To estimate the value of information obtained from satellite inspections, two cases representing current pavement condition practices were simulated: (1) as an alternative to inspect highways, roads that are traditionally monitored with annual automated distress surveys, and (2) as an option to inspect local or ancillary roads, which are not typically monitored. Results indicate that satellite observations result in up to 6.5% reduction in cost if they are used to make maintenance and inspection decisions over the pavement life cycle. Savings are higher for nonmonitored roads compared to major roads that are annually inspected with automated distress surveys. Satellite information was found to become valuable at 70% level of accuracy when used in combination with more accurate systems.
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Data Availability Statement
All data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request.
Acknowledgments
The authors would like to acknowledge the Colorado Department of Transportation (CDOT) for their assistance with data inquiry. The contents of this paper do not necessarily reflect the official views or policies of CDOT.
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Journal of Computing in Civil Engineering
Volume 37 • Issue 3 • May 2023
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© 2023 American Society of Civil Engineers.
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Received: Jul 24, 2022
Accepted: Nov 18, 2022
Published online: Jan 17, 2023
Published in print: May 1, 2023
Discussion open until: Jun 17, 2023
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