Relevant Thesis-Based Degree Programs
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Graduate Student Supervision
Master's Student Supervision
Theses completed in 2010 or later are listed below. Please note that there is a 6-12 month delay to add the latest theses.
The characterization and classification of rock mass structures is a key step in any rock engineering operation. The commonly used methods today, such as Rock Quality Designation (RQD), Rock Mass Rating (RMR), Tunneling Quality Index (Q-System), and Geological Strength Index (GSI) are based on empirical correlations and the subjective judgment of the user. Geotechnical projects are becoming larger and much more complex than when these methods were first developed, some 40 years ago. The development of new methods should focus on taking advantage of recent advances in mapping, remote sensing, and automation to reduce bias and errors.(Elmo, Yang, et al., 2021) proposed the Network Connectivity Index (NCI) as an alternative rock mass quality indicator. With the potential of being utilized fully using only remote sensing methods, this approach is further evaluated. Using Discrete Fracture Network (DFN) models, a sensitivity analysis was performed to assess the impact of structural properties and window sampling procedures on the variability of NCI results. The DFN methodology was reviewed, and input parameters were defined for generating a set of base case models. An additional set of models, based on DFN input parameters from existing projects was also generated. 2D sections were extracted from each model as analogs for rock exposures. A Python algorithm incorporated grid-based and random-based window sampling of the 2D sections, and an NCI value was calculated for each window. The impact of input rock mass structural properties on the variability of NCI values was evaluated, focusing on fracture size distributions, mean fracture size, and trace length data. The impact of the sampling procedures was also investigated, focusing on the sampling window pattern, size, shape, and rotation. The potential to correlate between NCI results and volumetric fracture intensity (P₃₂) values were also checked.The analysis showed that NCI is scale-dependent. As sampling windows increase in size, NCI values increase. The variability of NCI results is sensitive to the persistence of features in the rock exposure, with longer features having a larger impact and resulting in higher NCI values. Finally, a correlation between NCI and P₃₂ could not be established.
Autonomous vehicle technologies are yet to become a common commercial product for road vehicles, however their presence in open pit mines has been a reality for over 10 years. The technology is now past the early adoption phase with the biggest companies in the industry boasting over 100 active autonomous vehicles in operation. The technology has been proven to reduce fuel consumption, maintenance and operation costs and downtime with respect to manned operations in a range of open pit mine operation improving productivity by up to 30%. The move towards widespread adoption, however, comes with concerns of ease of implementation, security, and reliability at scale.Early developments of Autonomous Hauling Systems (AHS) were based in Wi-Fi; however, this technology was designed for office and domestic use, so its implementation in mining required increased complexity and adaptation to operate, raising many concerns for operation at large scale. In 2019, the use of mobile networks began being popularized for industrial use with the release of the 4.9G LTE protocol, facilitating the use of the technology for AHS applications. Since this change, AHS industry players have been shifting their systems to use the technology, and with the more recent releases of 5G, the improved performance is expected to bring further changes to the way these automations are implemented. The 5G protocol brings the use of Mobile Edge Computing (MEC) as part of its innovative sweep of technologies, moving computing from few large server farms to closer locations to the end users.This Thesis explores the development of a physical replica of an Autonomous Haling Vehicle (AHV), observing the developments of on-road vehicles to evaluate the use of new technologies and processes to improve current AHS technologies. The project is used as abasis to evaluate the use of 5G communication in comparison to its 4G LTE predecessor in different aspects of the driving process.The system used is based off the Robotics Operating System (ROS) and it the uses a set of sensors and LTE, 5G and mm-Wave communication modules incorporated in a 1:14 scale remote controlled hauling truck.
Mineral exploration is the necessary first step of any mining project. Mineral prospectivity analysis is a cost and time efficient exercise with for goals to delineate area of high prospectivity or to rank targets. The worldwide tendency in mineral exploration efforts is to focus on brownfield areas where large amount of data is available. Hence, data-driven methods for mineral prospectivity modeling (MPM) is preferred. In this research, two aspects of data-driven MPM are explored to examine the influence of them on the mineral prospectivity map using two different machine learning algorithm (random forest (RF) and support vector machine (SVM)). This research aims at demonstrating that RF algorithm is the best method for MPM in a variety of case scenarios involving different training datasets and input features.This study primary target is epithermal Au deposit in the area of the Iskut property owned by Seabridge Gold Inc. Different training dataset were created using same 18 deposit locations but different set of non-deposit locations: selected non-prospective known mineral occurrences (KMO) for Au deposits and 10 sets of random point pattern. Predictor maps were generated from publicly available and privately-owned geospatial data based on a conceptual exploration model using a mineral system approach and were separated into two input datasets: one exclusively included public data while the other included data from the private and public domain. Data-driven mineral potential models using RF and SVM (using three different kernel function) were compared based on sensitivity to parameter configuration, accuracy and performance. It was found that the accuracy of a model increases when the number of predictor maps increases. This research also showed that non-prospective KMO based uniquely on distance and commodity can introduce a bias. On one hand, almost all the SVM models are overfitting, most likely because of the scarce training dataset. Moreover, they are sensitive to outliers in the training data and require long computational time. On the other hand, the RF is easy to parameter, transparent, less sensitive to outliers and performs well. Hence, RF is the method to opt for in data scarce region over SVM.
The concept of digital rock physics (DRP) is widely used in petroleum engineering and science. It is a digital interpretation of a real rock specimen which is used to visualize and model rock properties over multiple scales. It is an excellent cross-over concept for mining. In the oil and gas (O&G) industry, making the most of every centimetre of the core, or gram of cutting sample is imperative when it comes to minimizing the risks and improving the outcomes of an exploration program. Similarly, to O&G applications, DRP can be translated directly into mineral exploration and mining with a significant effect, especially in the field of geometalurgy. By creating a 3D version of a core sample based on real rock properties and not using numerical or probabilistic models, it is now possible to perform a multitude of virtual experiments and observations without damaging the original sample. The most commonly used rock analysis includes high-resolution micro-computed tomography (micro CT), scanning electron microscopy (SEM), and focused ion beam (FIB) imaging, that enables a 3D analysis of the rock's structural and mineralogical properties at higher resolutions compared to that which is possible with light microscopy. However, these DRP methods are relatively slow in data collection and have other problems that limit the use of the technology to our advantage. This thesis presents a robust method for rapid, large-scale acquisition of data from digital models of rock specimens, combined with an automated data segmentation using machine learning, which dramatically increases the speed of digital rock analysis. The feasibility of the approach is demonstrated through the 3D analysis of both homogenous and highly heterogeneous rock samples by achieving a significant improvement in speed of analysis as compared to manual approaches for data segmentation and digital rock analysis.
Managing the payload is key to running safe, efficient and profitable mining operations. Despite the relative simplicity of shovel-truck operations, they are currently not achieving optimum productivity. By overloading trucks, shovel operators can significantly affect the operational efficiency of a mobile fleet and, consequently, reduce the overall profitability of a mining project. Uncertainty around the real volume of the mineral payload hauled from the mine to the processing plant impacts the planning for a smart decision-making process, which increases the risk of operating the mine below a desirable standard of efficiency and profitability. Also, inefficient payload management increases the consumption of fuel and tire, which impacts the carbon footprint by raising demand for elements harmful to the environment. Modern digital and data technologies offer the potential to factor out these drawbacks and to provide digital solutions to optimize shovel-truck performance. This Thesis proposes two approaches for estimating the volume and distribution of the truck payload. The first approach is a batch-performance machine vision system for imaging-based analysis of the payload. The system is developed and tested on a 1/14 model of a mining truck, and results are visualized in an immersive augmented reality environment. The second approach is based on the utilization of a proposed model of machine earning where data from sensors embedded in different parts of the shovel are collected and streamlined to an in-house private cloud for virtualization and processing. A TensorFlow-based ML platform was then used to find correlations between the truck payload and its components for an accurate visualization and volume computation under harsh operating conditions. Finally pros, cons and discussions around its applicability in a real operation were analyzed for both approaches. Analysis of profitability, cost and requirement of computational resources complements the encouraging results attained from both approaches regarding volume computation and visualization of the mining truck payloads.
Corrosion of ground support poses a significant safety risk to underground mines that is difficult to identify and costly to address. The corrosion of embedded ground support elements is undetectable by visual inspection. This leads to unexpected failure typically mitigated by comprehensive rehabilitation that covers any areas suspected of corrosion. Rock bolts are replaced when the visible portion of a rock bolt is judged to be excessively corroded or loss of holding capacity is confirmed by the pull-out test.This thesis investigates the influenced of the mineral electrochemical properties in a heterogeneous rock mass on ground support corrosion. Previous work focused on the uniform corrosion of ground support in wet and dry conditions by empirical experiment in a real or simulated underground mining environment and on the corrosivity classification of environment by various qualitative measures. This thesis approaches the electrochemical influence of rock mass to corrosion of rock bolt by characterization of mineral electrochemical properties, numerical simulation of the galvanic corrosion process, and validation of numerical model by submersion experiment. A procedure for determining the electrochemical properties of minerals was developed with a complementary numerical model for galvanic corrosion of ground support by minerals. Additionally, the simulated numerical corrosion model was validated with a submersion experiment.The numerical model was found to have over-estimated the galvanic corrosion of steel by cathodic mineral, but predicted cathodic protection of steel by anodic mineral. The numerical model did not account for the acid byproducts of oxidized sulphide minerals that resulted in pitting corrosion. These findings allow us to predict galvanic corrosion of steel by minerals and propose a new mechanism for initation of pitting corrosion to by pyrite oxidation.