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.

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 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

This research was aimed at determining the influence parameters have on the cut-off grade of sublevel stopes in the Garpenberg underground mine, and combing these to maximise value at the mine. Garpenberg is a multi-deposit poly-metallic mine owned by Boliden AB. Cost calculations were performed on data from 2018 to derive costs for each deposit and activity. Ore transport and rock bolting were two parameters which were suitable to be used for variable cost implementation. These costs were assigned to the different orebodies and cut-off grades were adjusted to reach a break-even level. Stopes were created using the Stope Optimizer software in Deswik, using the variable cut-offs and the technical constraints previously used for Garpenberg's Life of Mine Plan. Cut-offs were elevated or depressed to optimise NPV for each deposit separately and the optimal combination yielded an NPV increase of 530 Mkr, a 4\% increase, compared to using static cut-off grades. More value can be obtained by combining cut-off strategies in Garpenberg as whole, in contrast to separate optimisation. Scheduling of newly created stoping areas within the Lappberget deposit proved to be beneficial for value. Finally, a financial analysis into double stopes of 50 m high was performed. The elimination of production levels provides the main cost reduction. However, lower selectivity and expected dilution will limit the financial gain from this method.