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Detection of Structural Components in Point Clouds of Existing RC Bridges

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cam.issuedOnline2018-07-30
cam.orpheus.successThu Jan 30 10:53:54 GMT 2020 - The item has an open VoR version.
datacite.issupplementedby.doi10.5281/zenodo.1233844
dc.contributor.authorLu, Ruodan
dc.contributor.authorBrilakis, Ioannis
dc.contributor.authorMiddleton, campbell
dc.contributor.orcidBrilakis, Ioannis [0000-0003-1829-2083]
dc.contributor.orcidMiddleton, Campbell [0000-0002-9672-0680]
dc.date.accessioned2018-11-28T13:30:09Z
dc.date.available2018-11-28T13:30:09Z
dc.description.abstractThe cost and effort of modelling existing bridges from point clouds currently outweighs the perceived benefits of the resulting model. There is a pressing need to automate this process. Previous research has achieved the automatic generation of surface primitives combined with rule-based classification to create labelled cuboids and cylinders from point clouds. While these methods work well in synthetic datasets or idealized cases, they encounter huge challenges when dealing with real-world bridge point clouds, which are often unevenly distributed and suffer from occlusions. In addition, real bridge geometries are complicated. In this paper, we propose a novel top-down method to tackle these challenges for detecting slab, pier, pier cap, and girder components in reinforced concrete bridges. This method uses a slicing algorithm to separate the deck assembly from pier assemblies. It then detects and segments pier caps using their surface normal, and girders using oriented bounding boxes and density histograms. Finally, our method merges over-segments into individually labelled point clusters. The results of 10 real-world bridge point cloud experiments indicate that our method achieves very high detection performance. This is the first method of its kind to achieve robust detection performance for the four component types in reinforced concrete bridges and to directly produce labelled point clusters. Our work provides a solid foundation for future work in generating rich Industry Foundation Classes models from the labelled point clusters.
dc.identifier.doi10.17863/CAM.33339
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/286019
dc.language.isoeng
dc.publisherWiley
dc.publisher.urlhttps://onlinelibrary.wiley.com/doi/full/10.1111/mice.12407
dc.rightsAttribution 4.0 International
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.titleDetection of Structural Components in Point Clouds of Existing RC Bridges
dc.typeArticle
dcterms.dateAccepted2018-07-05
prism.endingPage22
prism.publicationNameJournal of Computer-Aided Civil and Infrastructure Engineering
prism.startingPage1
pubs.funder-project-idEuropean Commission (334241)
pubs.funder-project-idEuropean Commission FP7 Collaborative projects (CP) (31109806)
rioxxterms.licenseref.startdate2018-07-05
rioxxterms.licenseref.urihttp://creativecommons.org/licenses/by/4.0/
rioxxterms.typeJournal Article/Review
rioxxterms.versionVoR
rioxxterms.versionofrecord10.1111/mice.12407

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