Road pavement health monitoring system using smartphone sensing with a two-stage machine learning model

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dc.contributor.authorZhao, Kaien
dc.contributor.authorXu, Shuoshuoen
dc.contributor.authorLoney, Jamesen
dc.contributor.authorVisentin, Andreaen
dc.contributor.authorLi, Zilien
dc.contributor.funderScience Foundation Irelanden
dc.contributor.funderEuropean Regional Development Funden
dc.date.accessioned2024-09-09T13:07:20Z
dc.date.available2024-09-09T13:07:20Z
dc.date.issued2024-08-17en
dc.description.abstractDrive-by road pavement monitoring, using smartphone sensing, has faced longstanding challenges in adoption due to low accuracy and limited applicability. This stems from significant uncertainties during data collection in real-world scenarios, making it prohibitively difficult in applying conventional machine learning models to the detection of road pavement anomalies. This paper presents a two-stage machine learning approach that extracts potential anomalies from the dataset and classifies them into four typical road feature categories. Unlike time-series data analysis, this approach transforms time-series into geospatial series, allowing the analysis to be time-independent thereby capable of detecting road anomalies regardless of driving speeds. Additionally, a framework for a road pavement health monitoring system is proposed to collect data, integrate the machine learning engine, and visualise road anomalies. The developed system was tested on two shuttle buses with normal smartphones, which achieved 87% overall accuracy compared against manual inspection.en
dc.description.sponsorshipe EU Commission Recovery and Resilience Facility under the Science Foundation Ireland Future Digital Challenge Grant Number 22/NCF/FD/10932, and secondarily by SFI, Grant number 12/ RC/2289-P2 co-funded under the European Regional Development Fund.en
dc.description.statusPeer revieweden
dc.description.versionPublished Versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.articleid105664en
dc.identifier.citationZhao, K., Xu, S., Loney, J., Visentin, A. and Li, Z. (2024) ‘Road pavement health monitoring system using smartphone sensing with a two-stage machine learning model’, Automation in Construction, 167, p. 105664. Available at: https://doi.org/10.1016/j.autcon.2024.105664en
dc.identifier.doihttps://doi.org/10.1016/j.autcon.2024.105664en
dc.identifier.endpage15en
dc.identifier.issn0926-5805en
dc.identifier.journaltitleAutomation in Constructionen
dc.identifier.startpage1en
dc.identifier.urihttps://hdl.handle.net/10468/16308
dc.identifier.volume167en
dc.language.isoenen
dc.publisherElsevieren
dc.relation.ispartofAutomation in Constructionen
dc.relation.projectinfo:eu-repo/grantAgreement/SFI/National Challenge Fund::Future Digital Challenge/22/NCF/FD/10932/IE/‘Road Phone’ - Road pavement condition monitoring using smartphone sensing at the community level/en
dc.relation.projectinfo:eu-repo/grantAgreement/SFI/National Challenge Fund::Future Digital Challenge/22/NCF/FD/10932/IE/‘Road Phone’ - Road pavement condition monitoring using smartphone sensing at the community level/en
dc.relation.projectinfo:eu-repo/grantAgreement/SFI/National Challenge Fund::Future Digital Challenge/22/NCF/FD/10932/IE/‘Road Phone’ - Road pavement condition monitoring using smartphone sensing at the community level/en
dc.relation.projectinfo:eu-repo/grantAgreement/SFI/SFI Research Centres/12/RC/2289/IE/INSIGHT - Irelands Big Data and Analytics Research Centre/en
dc.rights© 2024 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).en
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.subjectDrive-by road pavement monitoringen
dc.subjectStructural health monitoringen
dc.subjectSmartphone sensingen
dc.subject2-stage machine learningen
dc.titleRoad pavement health monitoring system using smartphone sensing with a two-stage machine learning modelen
dc.typeArticle (peer-reviewed)en
dc.typejournal-articleen
oaire.citation.volume167en
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