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Abstract Submission Opens
1 Jul 2017
Abstract Submission Deadline
1 Dec 2017 (Extended to 10 Jan 2018)
Notification of Abstract Acceptance
15 Jan 2018
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16 Apr 2018 (Extended to 15 Jun 2018)
Notification of Full Paper Acceptance
From 16 July 2018
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By 17 September 2018

Young Researcher Plenary

Andreas BUYER
Graz University of Technology, Austria

Thu-01 Nov 2018 | 09:35 – 09:55 | Summit 2

"Joint Trace Detection in Digital Images"

Rock mass characterization is very important in rock engineering. The characterization encompasses the description of the apparent joint network as well as the spatial distribution of ground types or lithologies. Currently, the rock mass is documented and mapped either on-sight or with digital mapping-software like ShapeMetriX3D on the desktop. In the latter, digital images are used to generate 3D point clouds and manually map the joint planes or traces. With an increasing trend to automate the mapping procedure, the analysis of the digital images experiences a digital rebirth. In this paper, digital image processing is applied to detect edges in the images of two selected tunnel faces, clusters the signals and a structure map is generated which can be used for determining joint orientations, spacings and trace lengths. The applied method resulted in both cases in a very good detection joint traces, which could be distinguished into four distinct joint sets in the first step and reduced into three joint set in the second clustering process. In total, 743 (case study I) and 1233 (case study II) joint traces were detected automatically. However, in the current application, the segment linking process is incomplete and leads to scattering of the orientation values due to short line segments. Additionally, no effort was made yet to exclude artificial color changes, like caused by chiselling, from the analysis. However, with the large number of measurements, their influence is considered negligible. The applied method shows strengths especially in detecting geological features, which do not per se occur as joint planes and which will be missed in an automated vector analysis of the point clouds.

Andreas Buyer, M.Sc., B.Sc., finished his Bachelor of Science in Geosciences in 2012 at the Munich GeoCenter. He received his Master of Science in Engineering- and Hydrogeology at the Technical University Munich in 2014. In his master’s thesis he investigated the rock fall hazard and the stratigrafic situation in a former gravel pit.
After a short-term employment at the civil engineering bureau Moser & Jaritz ZT GmbH (Austria), he came in the year of 2015 to the Institute of Rock Mechanics and Tunnelling at the Graz University of Technology as a scientific assistant. His research focuses on the automatization of photogrammetric mapping tools and on rock mass description. Herein he uses digital image processing tool to identify joint traces in combination with a vector-based 3D point cloud analysis. Resulting, a highly detailed structure map of the detected features shall lead to a time-saving, objective and improved characterization of a rock mass. The results can also be used directly in DFN modelling of tunnel faces or slopes. Besides his main focus on rock mass characterization he also investigates rock block fragmentation during rock fall events.