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dc.contributor.authorAgapaki, Eva
dc.contributor.authorBrilakis, Ioannis
dc.date.accessioned2021-06-28T23:30:37Z
dc.date.available2021-06-28T23:30:37Z
dc.date.issued2021-11
dc.identifier.issn0733-9364
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/324515
dc.description.abstractThis paper devises, implements and benchmarks a novel framework, named CLOI, that can accurately generate individual labelled point clusters of the most important shapes of existing industrial facilities with minimal manual effort in a generic point-level format. CLOI employs a combination of deep learning and geometric methods to segment the points into classes and individual instances. The current geometric digital twin generation from point cloud data in commercial software is a tedious, manual process. Experiments with our CLOI framework reveal that the method can reliably segment complex and incomplete point clouds of industrial facilities, yielding 82% class segmentation accuracy. Compared to the current state-of-practice, the proposed framework can realize estimated time-savings of 30% on average. CLOI is the first framework of its kind to have achieved geometric digital twinning for the most important objects of industrial factories. It provides the foundation for further research on the generation of semantically enriched digital twins of the built environment.
dc.description.sponsorshipAVEVA
dc.languageen
dc.publisherAmerican Society of Civil Engineers (ASCE)
dc.rightsAll rights reserved
dc.titleCLOI: An Automated Benchmark Framework for Generating Geometric Digital Twins of Industrial Facilities
dc.typeArticle
prism.endingPage04021145
prism.issueIdentifier11
prism.publicationDate2021
prism.publicationNameJournal of Construction Engineering and Management
prism.startingPage04021145
prism.volume147
dc.identifier.doi10.17863/CAM.71968
dcterms.dateAccepted2021-06-24
rioxxterms.versionofrecord10.1061/(asce)co.1943-7862.0002171
rioxxterms.versionAM
rioxxterms.licenseref.urihttp://www.rioxx.net/licenses/all-rights-reserved
rioxxterms.licenseref.startdate2021-11
dc.contributor.orcidAgapaki, Eva [0000-0002-2962-9203]
dc.contributor.orcidBrilakis, Ioannis [0000-0003-1829-2083]
dc.identifier.eissn1943-7862
rioxxterms.typeJournal Article/Review
pubs.funder-project-idInnovate UK (104795)
pubs.funder-project-idEngineering and Physical Sciences Research Council (EP/S02302X/1)
pubs.funder-project-idEuropean Commission Horizon 2020 (H2020) Marie Sk?odowska-Curie actions (860555)
pubs.funder-project-idAustralian Research Council (DP170104613)
pubs.funder-project-idEngineering and Physical Sciences Research Council (2439669)
pubs.funder-project-idEuropean Commission Horizon 2020 (H2020) Industrial Leadership (IL) (958398)
pubs.funder-project-idEuropean Commission Horizon 2020 (H2020) Industrial Leadership (IL) (955269)
cam.orpheus.counter13
rioxxterms.freetoread.startdate2024-06-28


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