A new approach for semi-automatic rock mass joints recognition from 3D point clouds

Please use this identifier to cite or link to this item: http://hdl.handle.net/10045/36557
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dc.contributorIngeniería del Terreno y sus Estructuras (InTerEs)es
dc.contributor.authorRiquelme, Adrián-
dc.contributor.authorAbellán Fernández, Antonio-
dc.contributor.authorTomás, Roberto-
dc.contributor.authorJaboyedoff, Michel-
dc.contributor.otherUniversidad de Alicante. Departamento de Ingeniería Civiles
dc.identifier.citationComputers & Geosciences. 2014, Accepted Manuscript, Available online 4 April 2014. doi:10.1016/j.cageo.2014.03.014es
dc.identifier.issn0098-3004 (Print)-
dc.identifier.issn1873-7803 (Online)-
dc.description.abstractRock mass characterization requires a deep geometric understanding of the discontinuity sets affecting rock exposures. Recent advances in Light Detection and Ranging (LiDAR) instrumentation currently allow quick and accurate 3D data acquisition, yielding on the development of new methodologies for the automatic characterization of rock mass discontinuities. This paper presents a methodology for the identification and analysis of flat surfaces outcropping in a rocky slope using the 3D data obtained with LiDAR. This method identifies and defines the algebraic equations of the different planes of the rock slope surface by applying an analysis based on a neighbouring points coplanarity test, finding principal orientations by Kernel Density Estimation and identifying clusters by the Density-Based Scan Algorithm with Noise. Different sources of information —synthetic and 3D scanned data— were employed, performing a complete sensitivity analysis of the parameters in order to identify the optimal value of the variables of the proposed method. In addition, raw source files and obtained results are freely provided in order to allow to a more straightforward method comparison aiming to a more reproducible research.es
dc.description.sponsorshipThis work was partially funded by the University of Alicante (vigrob-157, uausti11–11, and gre09–40 projects), the Swiss National Science Foundation (FNS-138015 and FNS-144040 projects) and by the Generalitat Valenciana (project GV/2011/044).es
dc.subjectRock masses
dc.subjectSemi-automatic detectiones
dc.subject3D point cloudes
dc.subjectSensitivity analysises
dc.subject.otherIngeniería del Terrenoes
dc.titleA new approach for semi-automatic rock mass joints recognition from 3D point cloudses
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