Rockfall hazard assessment based on semi-automatic point cloud analysis from UAV data
Master thesis - UiT - Faculty of Science and Technology. Department of Geology. Slettind in Flaktstad municipality, Lofoten in Nordland, has numerous rockfalls throughout the year, hitting Fv 805 on a weekly basis, and is estimated the most dangerous road in Nordland county. A rock avalanche hit the...
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[A.K. Bergbjørn]
2018
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ftsvv:oai:vegvesen.brage.unit.no:11250/2594862 2023-05-15T17:08:16+02:00 Rockfall hazard assessment based on semi-automatic point cloud analysis from UAV data Bergbjørn, Anna Karin Hormes, Anne Norway, Lofoten 2018-12 application/pdf http://hdl.handle.net/11250/2594862 eng eng [A.K. Bergbjørn] http://hdl.handle.net/11250/2594862 Attribution-NonCommercial-NoDerivatives 4.0 Internasjonal http://creativecommons.org/licenses/by-nc-nd/4.0/deed.no CC-BY-NC-ND 141 Statens vegvesen Region nord Geologi Steinsprang Skred Sikkerhet Master thesis 2018 ftsvv 2022-05-01T08:13:24Z Master thesis - UiT - Faculty of Science and Technology. Department of Geology. Slettind in Flaktstad municipality, Lofoten in Nordland, has numerous rockfalls throughout the year, hitting Fv 805 on a weekly basis, and is estimated the most dangerous road in Nordland county. A rock avalanche hit the road winter 2017 closing it for 2 weeks, and isolating the small village Myrland. Statens Vegvesen consider to build a tunnel to protect the road but the failure mechanisms has been little understood, as it is deemed too dangerous for traditional fieldwork to access the mountain in a safe manner. Traditional fieldwork involves shear strenght testing of joint sets and infill, as well as mapping of joint orientations, roughness, and volumes of blocks, using Barthons Q method, Rock Mass Index or GSI. However as it has not been feasible to attend the wall for such mapping, due to the steepness, height and risk for rockfall, new techniques for rockfall hazard assessment have been put in use. The purpose of this master thesis has been to use photogrammetry from UAV images, and Structure-for-Motion to create a 3D modell and identify joint surfaces, orientations and evaluate the failure mechanisms. The workflow has been compared and evaluated against traditional mapping methods. Photogrammetry from drone images has proven valuable for understanding the structures and driving forces in a rock mass, and is a more flexible and cheaper option than LiDAR or similar, to build point clouds. As such, a validated semiautomated workflow is a resource for evaluating steep, inaccessible mountainsides. Statens vegvesen Region nord Master Thesis Lofoten Nordland Nordland Nordland Norwegian Public Roads Administration (NPRA) (BIBSYS Brage) Lofoten Norway Slettind ENVELOPE(18.833,18.833,69.433,69.433) |
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Open Polar |
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Norwegian Public Roads Administration (NPRA) (BIBSYS Brage) |
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ftsvv |
language |
English |
topic |
Statens vegvesen Region nord Geologi Steinsprang Skred Sikkerhet |
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Statens vegvesen Region nord Geologi Steinsprang Skred Sikkerhet Bergbjørn, Anna Karin Rockfall hazard assessment based on semi-automatic point cloud analysis from UAV data |
topic_facet |
Statens vegvesen Region nord Geologi Steinsprang Skred Sikkerhet |
description |
Master thesis - UiT - Faculty of Science and Technology. Department of Geology. Slettind in Flaktstad municipality, Lofoten in Nordland, has numerous rockfalls throughout the year, hitting Fv 805 on a weekly basis, and is estimated the most dangerous road in Nordland county. A rock avalanche hit the road winter 2017 closing it for 2 weeks, and isolating the small village Myrland. Statens Vegvesen consider to build a tunnel to protect the road but the failure mechanisms has been little understood, as it is deemed too dangerous for traditional fieldwork to access the mountain in a safe manner. Traditional fieldwork involves shear strenght testing of joint sets and infill, as well as mapping of joint orientations, roughness, and volumes of blocks, using Barthons Q method, Rock Mass Index or GSI. However as it has not been feasible to attend the wall for such mapping, due to the steepness, height and risk for rockfall, new techniques for rockfall hazard assessment have been put in use. The purpose of this master thesis has been to use photogrammetry from UAV images, and Structure-for-Motion to create a 3D modell and identify joint surfaces, orientations and evaluate the failure mechanisms. The workflow has been compared and evaluated against traditional mapping methods. Photogrammetry from drone images has proven valuable for understanding the structures and driving forces in a rock mass, and is a more flexible and cheaper option than LiDAR or similar, to build point clouds. As such, a validated semiautomated workflow is a resource for evaluating steep, inaccessible mountainsides. Statens vegvesen Region nord |
author2 |
Hormes, Anne |
format |
Master Thesis |
author |
Bergbjørn, Anna Karin |
author_facet |
Bergbjørn, Anna Karin |
author_sort |
Bergbjørn, Anna Karin |
title |
Rockfall hazard assessment based on semi-automatic point cloud analysis from UAV data |
title_short |
Rockfall hazard assessment based on semi-automatic point cloud analysis from UAV data |
title_full |
Rockfall hazard assessment based on semi-automatic point cloud analysis from UAV data |
title_fullStr |
Rockfall hazard assessment based on semi-automatic point cloud analysis from UAV data |
title_full_unstemmed |
Rockfall hazard assessment based on semi-automatic point cloud analysis from UAV data |
title_sort |
rockfall hazard assessment based on semi-automatic point cloud analysis from uav data |
publisher |
[A.K. Bergbjørn] |
publishDate |
2018 |
url |
http://hdl.handle.net/11250/2594862 |
op_coverage |
Norway, Lofoten |
long_lat |
ENVELOPE(18.833,18.833,69.433,69.433) |
geographic |
Lofoten Norway Slettind |
geographic_facet |
Lofoten Norway Slettind |
genre |
Lofoten Nordland Nordland Nordland |
genre_facet |
Lofoten Nordland Nordland Nordland |
op_source |
141 |
op_relation |
http://hdl.handle.net/11250/2594862 |
op_rights |
Attribution-NonCommercial-NoDerivatives 4.0 Internasjonal http://creativecommons.org/licenses/by-nc-nd/4.0/deed.no |
op_rightsnorm |
CC-BY-NC-ND |
_version_ |
1766063987540099072 |