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Detection of Structural Components in Point Clouds of Existing RC Bridges

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cam.issuedOnline2018-07-30
dc.contributor.authorLu, R
dc.contributor.authorBrilakis, I
dc.contributor.authorMiddleton, CR
dc.contributor.orcidBrilakis, Ioannis [0000-0003-1829-2083]
dc.contributor.orcidMiddleton, Campbell [0000-0002-9672-0680]
dc.date.accessioned2018-09-17T11:23:20Z
dc.date.available2018-09-17T11:23:20Z
dc.date.issued2019
dc.description.abstract<jats:title>Abstract</jats:title><jats:p>The cost and effort of modeling 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 labeled cuboids and cylinders from point clouds. Although these methods work well in synthetic data sets 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 article, 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 oversegments into individually labeled 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 labeled point clusters. Our work provides a solid foundation for future work in generating rich Industry Foundation Classes models from the labeled point clusters.</jats:p>
dc.description.sponsorshipSeeBridge, Trimble
dc.identifier.doi10.17863/CAM.27655
dc.identifier.eissn1467-8667
dc.identifier.issn1093-9687
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/280287
dc.language.isoeng
dc.publisherWiley
dc.publisher.urlhttp://dx.doi.org/10.1111/mice.12407
dc.rightsAttribution 4.0 International
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subject4005 Civil Engineering
dc.subject40 Engineering
dc.titleDetection of Structural Components in Point Clouds of Existing RC Bridges
dc.typeConference Object
dcterms.dateAccepted2018-06-01
prism.endingPage212
prism.issueIdentifier3
prism.publicationDate2019
prism.publicationNameComputer-Aided Civil and Infrastructure Engineering
prism.startingPage191
prism.volume34
pubs.conference-finish-date2018-06-07
pubs.conference-name17th International Conference on Computing in Civil and Building Engineering
pubs.conference-start-date2018-06-05
pubs.funder-project-idEuropean Commission (334241)
pubs.funder-project-idEuropean Commission FP7 Collaborative projects (CP) (31109806)
rioxxterms.licenseref.startdate2019-03-01
rioxxterms.licenseref.urihttp://www.rioxx.net/licenses/all-rights-reserved
rioxxterms.typeConference Paper/Proceeding/Abstract
rioxxterms.versionVoR
rioxxterms.versionofrecord10.1111/mice.12407

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