Drilling is one of the routine operations carried out in geotechnical projects in order to retrieve samples from the ground. The retrieved samples, i.e. cores, are stored in boxes and analyzed by the geologists and mining engineers to determine several parameters required for rock mass classification systems, such as RMR (Rock Mass Rating), GSI (Geological Strength Index), and Q. For this routine task to be automated, cores should be segmented properly. In this paper, a method is introduced for the segmentation of cores and detection of their fracture paths by using shadows. First of all, three digital true color images of a core box, with the same camera position but different light source positions, are taken using a high resolution camera. After the detection of the core box with color thresholding, the sections of the box are detected by using Hough transform and boundary tracing algorithms. Then, after extracting cores from each row of the box using color thresholding, touching cores are separated from each other with the help of shadows, concave points, and edges. Finally, fracture paths of the cores are detected by taking positions of the light sources into account and tracing the boundaries of the detected shadows. All coding routines are developed in MATLAB 2017a. Two different core boxes with 4 and 5 rows storing HQ and NQ diameter cores having various joint/bedding plane angles are photographed to conduct the study.
We propose an automated camera setup for photogrammetric roughness analysis in the laboratory environment. The developed fast and low-cost automation setup can be very useful for tedious and laborsome manual field logging practices. The photographs are processed in MATLAB to obtain disparity maps. Coding routines for stereo photogrammetry and digital measurements are written in MATLAB. Secondly, 6 effecting factors (projecting an image onto core face, depth of field, brightness, camera-to-object to baseline distance ratio, projected image size and occlusion) influencing noise in roughness depth maps computed by employing stereo photogrammetry are investigated. After deciding the best values that allow the lowest amount of noise, depth maps of 6 core faces are computed. Using the 3D point cloud generated, roughness profile measurements are made. Then, 8 profile measurements are made for each core face, both manually and digitally.The accuracy of the disparity maps has been verified by comparing 48 joint roughness coefficient (JRC) measurements made manually using a profile gauge. It was proved that surface roughness can be measured very fast in millimetric accuracy with an average Root Mean Square Error (RMSE) of 3.50 and Mean Absolute Error (MAE) of 3.02 by the help of the proposed set-up and calibration.
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