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dc.contributor.authorVanderbeck, Roberten
dc.date2016-04-28 13:59:16.437
dc.date2016-04-29 09:45:55.702
dc.date.accessioned2016-04-29T20:07:06Z
dc.date.available2016-04-29T20:07:06Z
dc.date.issued2016-04-29
dc.identifier.urihttp://hdl.handle.net/1974/14339
dc.descriptionThesis (Master, Mining Engineering) -- Queen's University, 2016-04-29 09:45:55.702en
dc.description.abstractThis thesis deals with the subject of convergence detection in underground excavations --- typically mining operations. Traditional methods for convergence monitoring involve operose surveying procedures that produce measurements in low density along a drift. The aim of this thesis is to introduce a novel convergence monitoring solution which extracts typical convergence features from point cloud scans in a drift. These features are extracted by convergence indicators. These indicators are amalgamated using a Bayesian statistical approach to build an inference about whether or not convergence is occurring. This algorithm was tested on simulated as well as actual mining convergence drift scans.en
dc.language.isoengen
dc.relation.ispartofseriesCanadian thesesen
dc.rightsQueen's University's Thesis/Dissertation Non-Exclusive License for Deposit to QSpace and Library and Archives Canadaen
dc.rightsProQuest PhD and Master's Theses International Dissemination Agreementen
dc.rightsIntellectual Property Guidelines at Queen's Universityen
dc.rightsCopying and Preserving Your Thesisen
dc.rightsThis publication is made available by the authority of the copyright owner solely for the purpose of private study and research and may not be copied or reproduced except as permitted by the copyright laws without written authority from the copyright owner.en
dc.rightsThis publication is made available by the authority of the copyright owner solely for the purpose of private study and research and may not be copied or reproduced except as permitted by the copyright laws without written authority from the copyright owner.en
dc.subjectBayesianen
dc.subjectMiningen
dc.subjectGround controlen
dc.subjectindicatorsen
dc.subjectConvergenceen
dc.subjectLiDARen
dc.titleA Bayesian Approach to Convergence Detection in Underground Excavations using LiDARen
dc.typethesisen
dc.description.degreeM.A.Sc.en
dc.contributor.supervisorMarshall, Joshua A.en
dc.contributor.departmentMining Engineeringen
dc.degree.grantorQueen's University at Kingstonen


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