Surface mining operations can have a significant visual impact on the surrounding landscape and communities. There have been numerous cases of local populations pushing back against the development of mining operations near their communities. This study aims to determine whether it is possible to assess the visual impact of surface mining operations before the development has started. With this assessment it would become possible to make informed decisions as to pitshell development and selection in terms of visual impact. A tool was developed to determine whether it is possible to make this assessment. This tool must be able to be used to determine the visual impact of two or more scenarios in the pre-feasibility phase of development to assist in the decision of which scenario will be developed further in the feasibility phase. In this context, the visual impact is defined as the perceived change in the landscape as a result of mine development on local communities around the surface mine development. The scenarios are defined as the option of pitshells designed during the pre-feasibility phase of mine development. The tool makes a distinction between the physical changes in the landscape caused by the surface mining operation and how this physical change is perceived by anyone viewing the operation. The tool is GIS based and utilizes the free open-source software QGIS to calculate the physical change in the landscape. The tool calculates the vertical and horizontal visibility angle. The visibility angle describes the extent that the change in the landscape takes up in the view of an observer. In addition to that, the tool calculates the contrast between the changed colour in the landscape and its surroundings, which describes how much the change stands out. It requires two sets of data to work: a digital elevation model of the prospected surface mine pitshell and the surrounding landscape and a RGB satellite image of the surrounding area. The DEM provides elevation data for every square metre of the landscape to the GIS, while the satellite image provides RGB colour data. All other data can be generated from the previously mentioned data features. The tool is tested using a case study, in which several distinct pitshell scenarios are compared against one another on their visual impact. The case study is based on the extension of a limestone quarry in Belgium, which aims to secure reserve for future operations. The area of interest is surrounded by several small to medium size villages, which would be affected visually by the extension. Therefore, a comparison should be made regarding the visual impact of the several pitshell scenarios. The results showed it is possible to determine the visual impact of surface mining operations before the development has started. On this basis, it is possible to compare different pitshell scenarios and determine which scenario would be least impactful in terms of visual impact on the local community and surrounding landscape. The reliability of the tool can only be assessed theoretically, however a high level of accuracy is achieved by adhering to guiding principles which are set out in this thesis.

The current plan of bottom to top bottom sublevel stoping for the Kearney gold vein at the Cavanacaw mine, Omagh, Northern Ireland may not be the most effective in terms of stability. The Kearney ore body is a narrow vein gold deposit, which has been previously exploited through an open pit and is currently being developed as an underground operation using sublevel stoping (modified Avoca mining method). Stability within the mine is one of the key factors to be considered when it comes to hard rock mining. It should be considered equally as important from a safety and economic point of view. The extraction sequence plays an important role when considering the stability of a designed mine. This thesis aims to establish if the current planned sequence of extraction of bottom to top sublevel stoping is the most effective in terms of overall rock stability, or whether an alternative plan would be better? In the context of this thesis, the modified Avoca mining method is a form of sublevel stoping where material is extracted (stoped) between two drives (blind tunnels) and then backfilled. The project addressed, a conceptual study, field testing and laboratory testing in order to yield the information required to build several numerical models. The numerical modelling was carried out on several different stoping orders which met the constraints set out by Galantas, using the Hoek-Brown model within Plaxis2D. The analysis was conducted on the total displacements, phase displacements, predicted failure points and safety factors. The analysis of the different models showed that an alter- native stoping method of middle to top bottom to middle sublevel stoping peformed better in terms of stability. This improvement in stability was shown by an increase in the minimum safety factor from 3.20 to 3.50, over the current plan. There is further evidence in the reduction of the total number of predicted failure point by 25%.

This thesis aims to evaluate the effect of signal processing techniques related to ultrasonic laboratory measurements of shear waves. Compressional and shear wave velocities play an important role in static elastic rock deformation behaviour estimation. Onsets of compressional and shear wave signal have to be determined in order to calculate the corresponding wave propagation velocity. Onset estimation by automation is especially problematic in shear wave signals due to noise caused by reflections and refractions, which results in inaccurate onset estimations and, therefore, requires manual onset picking which is time-inefficient and, hence, costly. Akaike Information Criterion (AIC) is the automated picking method applied to the ultrasonic signals in this thesis. By efficiently processing shear wave signals it was tried to optimize the results of the AIC. Ten processing techniques from biomedical engineering, statistical signal processing, audio and speech processing and RADAR applications were thoroughly researched. Their applicability to ultrasonic signals was reasoned based on literature. Six applicable signal processing techniques were eventually applied to 30 synthetic and 30 real ultrasonic signals. The mean and standard deviation of the error related to onset estimation before and after processing was used for evaluation. Visual comparison before and after processing was also executed to evaluate the visual impact of the processing techniques. Results showed that only a Butterworth high-pass filter visually enhances synthetic and real ultrasonic signals and improves the mean and standard deviation with respect to onset estimation. A Chebyshev high-pass filter also improved onset estimation results, but deteriorated the visual interpretation of the time signals. A simple amplitude filter unexpectedly provided the best results with respect to onset estimation. It is concluded from this studies that onset estimation by AIC can be improved by application of related signal processing techniques. This could be beneficial in estimation static deformation behaviour. Potential room for improvement is found within parameter optimisation and synthetic signal production

Haul truck driving remains to be the most dangerous occupation in the mining industry (Kecojevic et al., 2007), leading in fatalities over other occupations in the mining industry. As the transition to autonomous haul trucks over the coming decades will not address thisproblem today, an immediate solution is needed that can be adopted at short notice to help aid in minimizing haul truck accidents. A gamified training simulator was developed that teaches haul truck drivers how to handle the hazardous situations that lead to the most injuries in the mine environment. The simulated nature of the training method allows truck drivers to gain knowledge on hazardous situations and immediately put them into practice by applying the gained knowledge during the different levels. Gamification aspects like a score system and progression through levels were added to increase engagement during the training simulation. A statistical analysis was made on haul truck accidents from 1983 until 2022. The accidents that proved to be most relevant were included in the content of the training software. A training software was developed using Unreal Engine 5, where trucks could be driven using a Thrustmaster T150 Pro steering wheel and driving pedals. 18 participants from ages 19-65 underwent the training program and two tests of identical difficulty were taken before and after undergoing the training, in order to make a judgement on the progress of the participants by undergoing the training simulation. Participants were judged based on the number of crashes and traffic violations committed, attempts required to complete the test, as well as the time taken to finish the test. A score was calculated based on these factors providing an overall judgement of the participants’ level of skill. Average skill of participants increased by undergoing the gamified training, and training showed particularly good progress for underperformers in the initial test. After completing the gamified training the average score of participants was notably higher and the standard deviation was notably lower. It was concluded that all participants were brought to a comparably high level after completing the training.

The implementation of autonomous haulage systems in open-pit mines is a progressive step in the industry, but it brings potential safety risks that need careful evaluation. This study developed a discrete event simulation model to analyze and evaluate these risks in different operating scenarios -fully autonomous, hybrid (a mix of autonomous and human-operated vehicles), and non-autonomous operations.The simulation model was developed using the HAULSIM and Anylogic software, integrated mine layout, haulage operations, and various fleet compositions. It provided insights into collision risks, a significant concern in mining safety literature. Results showed that collisions were inversely proportional to the number of autonomous vehicles in operation, indicating the potential safety advantages of fully autonomous operations. However, certain high-traffic intersections were identified as high-risk areas, emphasizing the need for targeted risk mitigation strategies. Further, a profile risk matrix was developed to provide a comprehensive view of collision severity and likelihood in each scenario. This highlighted the impact of collisions on both human safety and project operations. Based on the results, risk mitigation strategies were proposed, with a focus on redesigning intersections, implementing strict rules for human-operated trucks in autonomous zones, and improving autonomous vehicle capabilities. The study, while insightful, was limited by assumptions and the generic nature of operational data used in the simulation. Therefore, future research should seek to incorporate more detailed, mine-specific data and empirical insights from projects that have implemented autonomous haulage systems. Continuous advancements in autonomous technology and simulation modeling will be key to ensuring a safe and productive mining environment.

This thesis investigates the impact of truck electrification systems on CO2 emissions and financial aspects in mining. Through comprehensive simulations, the study analyses diesel-electric trucks with trolley assist as well as battery-electric variants with trolley assist and stationary charging. The research findings reveal a noteworthy decline in CO2 emissions and an improvement in cost-effectiveness for the diesel-electric variant with trolley assist. Comparable CO2 savings are observed in battery-electric scenarios, but with varying financial profiles. The transition to battery-electric trucks with trolley assist leads to improved efficiency and associated cost savings. On the other hand, stationary charging entails financial challenges owing to high equipment costs. The analysis of emissions was conducted by different scopes, allowing a differentiated analysis of direct emissions (Scope 1) and indirect emissions (Scopes 2 and 3). This research highlights the significance of adopting a comprehensive approach towards achieving a sustainable future in mining. It provides clear insights into the potential of electrification technologies, particularly highlighted by a 49% CO2 reduction in battery-electric scenarios.

The mining industry is a significant contributor to greenhouse gas emissions and operational costs. The transportation of materials within mining operations, especially using diesel-powered trucks, accounts for a substantial portion of both emissions and costs. Optimizing fuel consumption in this context is crucial for environmental sustainability and financial efficiency. While there is some existing research on minimizing fuel consumption in mining operations, most of these studies prioritize production over fuel efficiency. Additionally, there is a lack of solutions addressing real-time dispatch problems while also minimizing fuel consumption. This thesis proposes the development of a model to optimize truck speeds in mining operations, with the primary goal of reducing fuel consumption. The focus is on minimizing waiting times for trucks at loading stations by adjusting their speeds using a metaheuristic algorithm, specifically Simulated Annealing (SA). To achieve this, a discrete event simulation (DES) model made in HaulSim to replicate a simplified mining site in Nevada, USA. The simulation includes Caterpillar 793 series trucks. The SA algorithm is applied to find optimal truck speeds, with the aim of reducing fuel consumption with the same level of production. The hypothesis is that lowering truck speeds in mining environments with queueing trucks can significantly reduce fuel consumption. The study explores how speed optimization impacts efficiency across different mining conditions, identifies the most effective optimization techniques, and quantifies potential fuel savings. The research reveals a 27-32% reduction in fuel consumption across various truck scenarios. However, these findings should be interpreted cautiously due to limitations in data constraints and modelling simplifications. In conclusion, the study highlights the effectiveness of SA in optimizing truck speeds and the potential for substantial fuel savings in mining operations. We recommend the integration of Artificial Neural Networks (ANNs) for a more nuanced approach to fuel consumption estimation and optimization, considering factors like road maintenance intervals and payload. This synergistic approach, combining metaheuristics and machine learning techniques, aligns with sustainable practices in the mining industry and offers promising avenues for further research and application.

The importance of decarbonization in various industries is growing significantly, especially when it comes to LHD equipment in the minerals and metals industry. Conventional diesel load and haul (LHD) equipment used in quarrying and mining operations have drawbacks that result in the emissions of hydrocarbon and greenhouse gases that need to be reduced to reach the Net Zero Emissions scenario. Decarbonization options that could replace diesel equipment include battery electric vehicles, green hydrogen, and alternative fuels. This project aims to give an overview of current and near future available decarbonization methods and to analyse the environmental benefit, cost effectiveness and technological feasibility of these alternatives to diesel fuel. The case study used to simulate and compare the three different decarbonization options is a mountaintop marble quarry based in Ipoh, Malaysia. A comparison has been made with Ipoh located in Malaysia and a hypothetical situation of Ipoh located in France to show the true potential of decarbonization as Malaysia does not incentivize businesses to reduce emissions due to their subsidies on the use of fossil fuel. Malaysia’s energy mix consist of primarily unabated fossil fuels. The proposed workflow involves an extensive literature review of current and near-future decarbonisation technologies to replace diesel equipment and to create a haulage network based on block model data using the software Xpac Quarry solutions and its Haulnet package. Diesel equipment is imported from the Quarry solutions database and used as a base case to compare BEV and trolley assist simulations. Hydrogen and HVO fuel consumption have been calculated manually to obtain NPVs and associated emissions. An investigation into required infrastructure and energy requirements has been done to accurately define costs and resulting total CO2eq emissions. The results indicate that replacing diesel at Ipoh with any of the decarbonization methods will result in a negative NPV as the infrastructure requirements need extensive capital investment of which the payback period often exceeds the life of mine. The least expensive method to implement HVO fuels, followed by BEVs and green hydrogen. Furthermore, it has been found that using BEVs instead of diesel at Ipoh is more environmentally polluting because producing 1 kWh of electricity is more polluting than producing the same energy worth of diesel. Green hydrogen is the least polluting method with no greenhouse gas emissions associated with it, followed by alternative fuels. When investigating the technological feasibility of the three main decarbonization options, hydrogen is the furthest away of being technologically feasible as from now. No green hydrogen using water electrolysers has been produced at an industrial scale, and a lack of technical expertise and infrastructure is present regarding distribution, transportation, and storage. Currently, no hydrogen LHD equipment is commercially available currently, and the same applies to BEVs that could replace the current equipment used at Ipoh. To bridge this gap, alternative fuels are the best option, but the market is competitive, and supply is limited. This project has given a good overview of current possibilities and clearly shows today’s options for decarbonisation and where industries stand with respect to infrastructure and equipment.

Measuring rock stress is a difficult process, especially in the Stjernøya Nepheline-Syenite mine in Northern Norway. The complex nature and topography of the shallow deposit makes it difficult to conduct industry standard tests. Therefore, this thesis aims at answering how different stress measurement techniques and their results compare to each other in a shallow low-stress hard-rock mine. To do this, an elaborate laboratory work is done to find a relation between stress (UCS) and acoustic properties and velocities of the nepheline syenite. Using that information, a similar combination of tests is done in the field, namely a flat jack test and acoustic velocity measurements with a hammer and geophones. To give further insight in the gathered field-data, visual classification methods, like RMR are done to verify and aid the tests and the results. The laboratory tests gave more insight in the relation between stress and acoustic velocity. There exists a positive, somewhat logarithmic relation between confining pressure and velocity through the sample, until the end of the elastic domain. Depending on the sample, this becomes more apparent after an initial loading phase between 0 and 10-20 MPa. In this low-stress zone, the wave arrival times could not be accurately observed and tend to give very low results. This problem translated to the field. Despite the flat-jack provided an understandable vertical pressure of 9.8 MPa, the acoustic measurements in the field did not always coincide with this pressure, according to the laboratory relationship, generally giving lower velocities than expected. However, a relation between rock quality and wave-velocity could also be present, as the cracks in the heterogeneous rock could heavily influence acoustic velocity. Therefore, this research concludes that both the flat jack and the acoustic investigative methods have their use in understanding the stress-profile of the shallow deposit. Given the low stresses in the mine, the acoustical investigative method could not be used directly to measure stress, but it can be used as a control for the flat jack tests and have use in monitoring of the rock wall.