A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Autonomous condition monitoring-based pavement management system
Abstract Due to high operation cost of dedicated inspection vehicles, conventional pavement management systems (PMS) suffer from limited data quantity collected from periodic inspections. However, increasing market penetration of connected autonomous vehicles (CAVs) offers opportunities to monitor pavement conditions more frequently through sensors, including vision cameras and accelerometers, originally installed for autonomous driving. In this paper, we proposed an autonomous condition monitoring-based pavement management system (ACM-PMS) with real-time data collection using CAVs traveling voluntarily. We presented a novel mathematical framework to evaluate potential benefits of ACM-PMS in reducing social costs for both users and agency, systematically accounting for its unique three advantages: (i) large amount of condition data increases prediction model accuracy; (ii) aggregated measurement of current facility condition improves inspection accuracy; (iii) agency can perform maintenance activities at optimal timings, achieving continuous-time and condition-based policies. Results of numerical examples confirm that ACM-PMS significantly reduces the social cost of conventional PMS.
Highlights Propose an initial concept of an autonomous condition monitoring-based pavement management system (ACM-PMS) Identify and evaluate potential benefits of the ACM-PMS Compare between a conventional pavement management system and the ACM-PMS Measure information values according to the accuracies of prediction and inspection Analyze a real-world expressway in South Korea
Autonomous condition monitoring-based pavement management system
Abstract Due to high operation cost of dedicated inspection vehicles, conventional pavement management systems (PMS) suffer from limited data quantity collected from periodic inspections. However, increasing market penetration of connected autonomous vehicles (CAVs) offers opportunities to monitor pavement conditions more frequently through sensors, including vision cameras and accelerometers, originally installed for autonomous driving. In this paper, we proposed an autonomous condition monitoring-based pavement management system (ACM-PMS) with real-time data collection using CAVs traveling voluntarily. We presented a novel mathematical framework to evaluate potential benefits of ACM-PMS in reducing social costs for both users and agency, systematically accounting for its unique three advantages: (i) large amount of condition data increases prediction model accuracy; (ii) aggregated measurement of current facility condition improves inspection accuracy; (iii) agency can perform maintenance activities at optimal timings, achieving continuous-time and condition-based policies. Results of numerical examples confirm that ACM-PMS significantly reduces the social cost of conventional PMS.
Highlights Propose an initial concept of an autonomous condition monitoring-based pavement management system (ACM-PMS) Identify and evaluate potential benefits of the ACM-PMS Compare between a conventional pavement management system and the ACM-PMS Measure information values according to the accuracies of prediction and inspection Analyze a real-world expressway in South Korea
Autonomous condition monitoring-based pavement management system
Shon, Heeseung (author) / Cho, Chung-Suk (author) / Byon, Young-Ji (author) / Lee, Jinwoo (author)
2022-03-21
Article (Journal)
Electronic Resource
English
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