Smart pothole detection system using vehicle-mounted sensors and machine learning

Anaissi, A; Khoa, NLD; Rakotoarivelo, T; Alamdari, MM; Wang, Y

Smart pothole detection system using vehicle-mounted sensors and machine learning

Anaissi, A Khoa, NLD Rakotoarivelo, T Alamdari, MM Wang, Y

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Publisher:: SPRINGER HEIDELBERG
Publication Type:: Journal Article
Citation:: Journal of Civil Structural Health Monitoring, 2019, 9, (1), pp. 91-102
Issue Date:: 2019-02-13

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Field	Value	Language
dc.contributor.author	Anaissi, A
dc.contributor.author	Khoa, NLD
dc.contributor.author	Rakotoarivelo, T
dc.contributor.author	Alamdari, MM
dc.contributor.author	Wang, Y https://orcid.org/0000-0002-6815-0879
dc.date.accessioned	2022-07-14T04:23:19Z
dc.date.available	2022-07-14T04:23:19Z
dc.date.issued	2019-02-13
dc.identifier.citation	Journal of Civil Structural Health Monitoring, 2019, 9, (1), pp. 91-102
dc.identifier.issn	2190-5452
dc.identifier.issn	2190-5479
dc.identifier.uri	http://hdl.handle.net/10453/158901
dc.description.abstract	Road networks are critical assets supporting economies and communities. Despite budget and time constraints, road authorities strive to maintain them to ensure safety, ongoing service, and economic productivity. This paper proposes a virtual road network inspector (VRNI), which continuously monitors road conditions and provides decision support to managers and engineers. VRNI uses acceleration data from vehicle-mounted sensors to assess road conditions. It proposes a novel road damage detection method based on two adaptive one-class support vector machine models, which were applied on the vertical and lateral acceleration data. We evaluated this method on data from a real deployment on school buses in New South Wales, Australia. Experimental results show that our method consistently detects 97.5% of the road damage with a 4% false alarm rate that relate to benign anomalies such as expansion joints.
dc.language	English
dc.publisher	SPRINGER HEIDELBERG
dc.relation.ispartof	Journal of Civil Structural Health Monitoring
dc.relation.isbasedon	10.1007/s13349-019-00323-0
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0905 Civil Engineering
dc.title	Smart pothole detection system using vehicle-mounted sensors and machine learning
dc.type	Journal Article
utslib.citation.volume	9
utslib.for	0905 Civil Engineering
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/A/DRsch The Data Science Institute
utslib.copyright.status	closed_access	*
dc.date.updated	2022-07-14T04:22:59Z
pubs.issue	1
pubs.publication-status	Published
pubs.volume	9
utslib.citation.issue	1

Abstract:

Road networks are critical assets supporting economies and communities. Despite budget and time constraints, road authorities strive to maintain them to ensure safety, ongoing service, and economic productivity. This paper proposes a virtual road network inspector (VRNI), which continuously monitors road conditions and provides decision support to managers and engineers. VRNI uses acceleration data from vehicle-mounted sensors to assess road conditions. It proposes a novel road damage detection method based on two adaptive one-class support vector machine models, which were applied on the vertical and lateral acceleration data. We evaluated this method on data from a real deployment on school buses in New South Wales, Australia. Experimental results show that our method consistently detects 97.5% of the road damage with a 4% false alarm rate that relate to benign anomalies such as expansion joints.

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http://hdl.handle.net/10453/158901