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dc.contributor.advisorKemeny, Johnen
dc.contributor.authorMonte, Jamie Marieen
dc.creatorMonte, Jamie Marieen
dc.date.accessioned2016-11-17T01:05:09Z
dc.date.available2016-11-17T01:05:09Z
dc.date.issued2004
dc.identifier.urihttp://hdl.handle.net/10150/621370
dc.description.abstractThe primary focus of this research is to evaluate whether laser scanning and digital imaging can provide a reliable means to collect essential rock mass data. Simulated and field case studies were conducted to determine if fracture orientation data (dip angle and dip direction) can be accurately estimated from a laser generated three - dimensional point cloud. Orientations measured with a Brunton Compass were compared to values derived from point clouds. The difference in dip direction was within three degrees and as high as twelve degrees for the dip angle. When fracture sets were estimated for both field and laser data, good correlation in mean set orientation and set distribution was observed. Some sets recorded during field mapping were absent in stereo plots of laser derived data due to a shadow zone created during scanning. This indicated that scanning from multiple locations is necessary to reduce potentially missed data. This thesis also investigated whether the newly proposed Digital Rock Mass Rating (DRMR) system could classify rock masses similar to established systems such as the Geological Strength Index (GSI). The seven DRMR parameters, fracture spacing, length, large -scale roughness, block volume, rock bridge percent, and rock mass texture were calculated for images of poor to good rock masses. When DRMR values were compared to GSI ratings estimated during field work, good correlation was seen for good quality rock masses (GSI between 40 and 60). The DRMR overestimated ratings for outcrops with GSI values less than 40, indicating that the rating system may not be applicable for poor quality rock masses. Additional case studies are needed to further validate the DRMR classification system.
dc.language.isoen_USen
dc.publisherThe University of Arizona.en
dc.rightsCopyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.en
dc.titleROCK MASS CHARACTERIZATION USING LASER SCANNING AND DIGITAL IMAGING DATA COLLECTION TECHNIQUESen_US
dc.typetexten
dc.typeThesis-Reproduction (electronic)en
thesis.degree.grantorUniversity of Arizonaen
thesis.degree.levelmastersen
thesis.degree.disciplineGraduate Collegeen
thesis.degree.disciplineMining, Geological and Geophysical Engineeringen
thesis.degree.nameM.S.en
dc.description.noteProvided by the Department of Mining and Geological Engineering.en
refterms.dateFOA2018-08-13T14:59:11Z
html.description.abstractThe primary focus of this research is to evaluate whether laser scanning and digital imaging can provide a reliable means to collect essential rock mass data. Simulated and field case studies were conducted to determine if fracture orientation data (dip angle and dip direction) can be accurately estimated from a laser generated three - dimensional point cloud. Orientations measured with a Brunton Compass were compared to values derived from point clouds. The difference in dip direction was within three degrees and as high as twelve degrees for the dip angle. When fracture sets were estimated for both field and laser data, good correlation in mean set orientation and set distribution was observed. Some sets recorded during field mapping were absent in stereo plots of laser derived data due to a shadow zone created during scanning. This indicated that scanning from multiple locations is necessary to reduce potentially missed data. This thesis also investigated whether the newly proposed Digital Rock Mass Rating (DRMR) system could classify rock masses similar to established systems such as the Geological Strength Index (GSI). The seven DRMR parameters, fracture spacing, length, large -scale roughness, block volume, rock bridge percent, and rock mass texture were calculated for images of poor to good rock masses. When DRMR values were compared to GSI ratings estimated during field work, good correlation was seen for good quality rock masses (GSI between 40 and 60). The DRMR overestimated ratings for outcrops with GSI values less than 40, indicating that the rating system may not be applicable for poor quality rock masses. Additional case studies are needed to further validate the DRMR classification system.


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