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Towards a digital twin-based intelligent decision support for road maintenance

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cam.depositDate2022-06-27
cam.orpheus.counter77*
dc.contributor.authorConsilvio, Alice
dc.contributor.authorHernández, José Solís
dc.contributor.authorChen, Weiwei
dc.contributor.authorBrilakis, Ioannis
dc.contributor.authorBartoccini, Luca
dc.contributor.authorGennaro, Federico Di
dc.contributor.authorWelie, Mara van
dc.contributor.orcidChen, Weiwei [0000-0003-3359-0556]
dc.date.accessioned2022-06-28T23:30:25Z
dc.date.available2022-06-28T23:30:25Z
dc.date.updated2022-06-27T16:43:41Z
dc.description.abstractThe digitalisation, automation and robotisation of road inspection and maintenance technologies make it possible to collect bigger volumes of data and additional types of information about road infrastructure. Methodologies and tools to support road asset management decision-making are needed to exploit this new information, progressing towards predictive maintenance and improving different aspects of road asset management. This study presents a Digital Twin-based Decision Support Tool to assist road operators in road inspection, maintenance and upgrade. The goal of the paper is twofold. First, the architecture of the Digital Twin-based Decision Support Tool is presented, describing the main components and functionalities. The system is based on a Digital Twin (DT) that mirrors real road assets to integrate different sources of data and support the processing of low-level data into high-level information. The decision support tool (DST) is able to analyse the collected information and compute the road pavement condition to derive optimal intervention plans, addressing road section conditions, human and technical resources and other external constraints. Second, the application of the proposed architecture to road pavement maintenance is described, considering the Italian highway A24 and its connections with Rome´s ring road, managed by Strada dei Parchi SpA. Road pavement data, such as the International Roughness Index (IRI) and the Sideway Force Coefficient (SFC), are integrated into the DT to be analysed through Artificial Intelligence-clustering techniques to perform the sectioning and clustering of road sections according to their status and quality index. The paper shows the benefits derived from the integration of DT technologies with DSTs for improving processes of road maintenance.
dc.description.sponsorshipThis project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 955269.
dc.identifier.doi10.17863/CAM.85824
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/338411
dc.language.isoeng
dc.publisher.departmentDepartment of Engineering
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.titleTowards a digital twin-based intelligent decision support for road maintenance
dc.typeConference Object
dcterms.dateAccepted2022-06-21
pubs.conference-finish-date2022-09-16
pubs.conference-nameAIIT 3rd International Conference on Transport Infrastructure and Systems (TIS ROMA 2022)
pubs.conference-start-date2022-09-15
pubs.funder-project-idEuropean Commission Horizon 2020 (H2020) Industrial Leadership (IL) (955269)
pubs.licence-display-nameApollo Repository Deposit Licence Agreement
pubs.licence-identifierapollo-deposit-licence-2-1
rioxxterms.versionVoR
rioxxterms.versionofrecord10.17863/CAM.85824

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