Semester

Spring

Date of Graduation

2023

Document Type

Thesis

Degree Type

MS

College

Statler College of Engineering and Mineral Resources

Department

Mining Engineering

Committee Chair

Ihsan Berk Tulu

Committee Member

Deniz Tuncay

Committee Member

Michael Murphy

Abstract

In mining, hazards causing rib and roof fall accidents generally develop with time. The development of a hazard depends on factors like geology, excavation geometry, overburden stress, and mining-induced stresses. Hazard mapping in an underground mine is essential for roof and rib stability assessment. However, often it is neither practical nor economical to inspect and map vast areas of mine workings with limited personnel. New technologies, recently, have been adopted in the mining industry that can solve this problem. Advances in LiDAR technology and simultaneous localization and mapping (SLAM) algorithms have led to the development of portable and mobile laser scanning devices that can be used in GPS-denied environments. Mapping with a LiDAR produces point-clouds, which are essentially sets of three-dimensional point coordinates (X, Y, Z) and point attributes. One of those attributes is the intensity values, which are just the returning light pulse that the scanner registers when the pulse is reflected from a surface. Intensity values are related to the color of a surface, so if it’s darker, reflected signal values are closer to zero, while lighter surfaces reflect higher values. In any underground mining operation, the identification of the changes in lithologies is important to assess the stability of the pillar ribs or tunnel walls. It is also important to recognize rib/roof/floor deformation or any changes i.e., fall of rocks, to recognize potential hazards. In this research, it was demonstrated that SLAM-based LiDAR or other active remote sensing sensors can be used to recognize the changes in the lithology and deformation in underground operations. In this thesis, first, the potential of identifying different lithologies with a SLAM-based LiDAR was studied. For that purpose, scans of rock cores of different lithologies were collected to investigate the relationship between the lithology, the intensity values and the measurement distance. The LiDAR data was processed with Cloud Compare, and One-Way ANOVA was used to compare the intensity values obtained for each lithology. The results showed that the intensity values of different lithologies tested in this research are statistically significantly different from each other, so the lithologies could be classified in confidence intervals of intensity values. Second, the experimental underground coal mine section was mapped with the SLAM-based LIDAR to investigate the potential of detecting changes in underground entries such as volumetric changes due to rockfalls or deformations. The collected data was processed with two algorithms: Iterative closest point (ICP) to align point clouds, and the Multiscale model-to-model cloud comparison (M3C2) to detect any change between two point clouds. In conclusion, the SLAM-based LiDAR proved to have the capability to detect changes of more than 3.8 cm.

Embargo Reason

Publication Pending

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