A digital face mapping case study in an underground hard rock mine

Mining companies are increasingly being motivated to become High Reliability Organisations (HROs) in order to achieve better results in critical areas such as safety, environment management, and loss avoidance despite their complex environments. High Reliability Organisations are recognised by their abilities to effectively anticipate failures and disasters, including use of lessons learnt from previous failures. This paper seeks to demonstrate how designers for systems in the mining industry can learn from failures to anticipate failures and effectively manage them. It also demonstrates the applicability of a hybrid model which incorporates and integrates Fault Tree Analysis (FTA), Reliability Block Diagram (RBD) analysis, Risk Priority Number (RPN) concepts, and Analytical Hierarchy Processes (AHPs) in a case study for a High Potential Incident (HPI) at an underground hard rock mine. It shows how valuable lessons can be extracted and how these lessons can be used in decision making to prevent and manage future failures. The main contribution of this work is the demonstration of incorporating HRO principles with a hybrid modelling framework for learning from failures.

Rock support systems have become widely dominant components in underground hard rock mines. They are used to maintain the stability of underground openings and reinforce disturbed rock masses after creating an excavation or start- ing mining activity. Thus, the objective of this study is to examine the effect of support types on the performance stabil- ity of underground tunnels that exist in hard rock mines, in terms of deformation, the extent of failure zones and the strength of the rock mass surrounding the tunnel. This, in turn, will help in the selection of an appropriate support system that mitigates the stress-deformation conditions around the tunnel. Herein, four models have been built using the RS2D program, simulated and introduced to evaluate the behaviour of an underground tunnel with different rock support systems. The first model is simulated without any support system, whereas, rock bolts have been installed in the second model. The third model applied only shotcrete, while rock bolts and shotcrete are combined together in the fourth model. The results are presented and discussed in terms of strength factor, the extent of yielding zones and rock mass displacement/convergence. The results show that tunnel stability suffers when there is no rock support at all, while, it is significantly improved when the rock support system is installed. The optimum improvement is obtained when both shotcrete and rock bolts are employed together.

A major part of the European Union’s (EU) project Sustainable Intelligent Mining System (SIMS) is investigating the development of diesel-free/carbon–neutral underground mines in order to ensure sustainable underground mining in the future. Replacing diesel machines with electric vehicles in underground hard rock mines has been widely acknowledged by the mining industry worldwide as a critical step to improve working conditions by reducing diesel exhaust–related contaminants, to reduce mine ventilation electrical power cost by reducing mine airflow quantity, and to reduce mine greenhouse gas emissions. All of these are major requirements to achieve sustainable future underground mining practices. A field trial of Epiroc’s 2nd generation of Battery Electric Vehicles (BEVs) at Agnico Eagle Finland’s Kittilä mine was conducted during 2019–2020. Vehicles tested were MT42 mine truck, ST14 Load-Haul-Dump (LHD), and Boomer E2 jumbo drill rig. This paper outlines the improvement of the working conditions observed in the field trial, and the opinions of the mine personnel at Kittilä mine on using BEVs instead of diesel machines. Measurements of atmospheric contaminants and air temperatures taken during the field trial clearly demonstrated a significant improvement of working conditions when BEVs were operating as opposed to diesel machines. This field observation was supported by the opinion of the majority of the Kittilä mine workers. However, some remaining concerns must be addressed before BEVs can replace diesel machines.

Exposure of ground support systems to corrosive environments in underground hard rock mines can result in a significant degradation and loss of performance. This can have significant repercussions on the structural integrity of excavations in rock and potentially endanger the safety of mine workers. The use of corrosion inhibitor coatings can extend the useful working life of expandable rock bolts exposed to corrosive environments and act as a risk mitigating factor. This paper reports an investigation on the relative resistance to corrosion of six different variants of expandable bolts based on coating type and load capacity. The bolts were submerged for 226 days under two distinct water environments in an underground mine in the Sudbury basin. A systematic methodology was developed to assess the relative performance based on image analysis, SEM/EDX analysis of the exposed rock bolts, and changes in the morphological nature of the corrosion process. The results demonstrated significant variations in the performance of the various expandable coated bolts. This has significant implications in the material selection process for suitable rock bolts for specific corrosive environments.

Rock mass characterization for underground hard rock mines

To mitigate unforeseen incidents, such as key block failure or tunnel collapse during excavation, an appropriate support pattern that correlates with the geological conditions of the rock mass at the tunnel face should be designed. Rock mass evaluations should be conducted through geological face mapping during the construction phase, alongside predictions based on field investigations during the design phase. When marked discrepancies are identified, it is customary to convene an on-site evaluation involving a committee of experts. This study develops a digital tunnel face mapping system that utilises mobile devices to facilitate online evaluations during the construction phase. This system effectively replaces the traditional on-site field evaluation method. Tunnel face mapping can be promptly accomplished using images captured at the excavation face, enabling rapid analysis. In conjunction with the mapping capabilities, the developed system was designed to digitally store geological information, which includes parameters such as rock strength distribution, the spacing and length of discontinuities observed during the mapping process, as well as data pertaining to weathering and the groundwater conditions of those discontinuities. This information was then correlated with the rock mass rating sheet to automate the determination of ratings for each parameter, ultimately leading to a conclusive classification of the rock mass quality. By employing this system for tunnel face mapping and rock quality evaluation, we significantly reduced the discrepancies in the evaluation results that often arise due to the subjective judgement of geologists, as well as human errors that can occur throughout the rating process.

Many underground hard rock mines have to deal with various degrees of squeezing in underground tunnels. Depending on the severity of the phenomenon, considerable investments either in rock reinforcement and support can be necessary. In worst cases, time-consuming and possibly hazardous scaling and rehabilitation operations can be required to keep drifts serviceable. Therefore, a proper understanding of the rock mass, its response to excavation and the mechanisms involved is paramount to the design of cost-effective ground support systems in squeezing ground. The Lapa gold mine located in northwestern Quebec operates in such challenging ground conditions. Using field observations and data from the Hard Rock Squeezing Ground Index (Mercier-Langevin and Hadjigeorgiou, 2011), a plausible failure mechanism is proposed. The resulting ground control strategy is presented.

Rock mass characterization is a very important part of engineering geological investigation. For a better understanding of the rock mass behaviour, it is crucially important to obtain as much as possible information about the discontinuity network, especially about orientation and the number of dominant discontinuity sets. The traditional methodology includes field mapping which dominantly produces a limited amount of data and consequently only a rough estimate about discontinuity network. To increase the number of measurements and to eliminate orientation bias, rock mass on the Špičunak rock slope in Gorski kotar, Croatia, was analysed using a combination of 3D Point Cloud and Textured Mesh Model generated from 3D Point Cloud by Poisson surface reconstruction. Both models were obtained from Terrestrial Laser Scanning. Two considerably different parts of a rock slope, with different weathering conditions and different degrees of fracturing were mapped. Discontinuities were mapped in the field and on the models using manual mapping techniques and semi-automated methods. Manual mapping on a 3D Point Cloud and Textured Mesh Model was done by Compass plugin and by Trace a polyline tool in Cloud Compare software version V2.12 and semi-automated mapping methods were done by Discontinuity Set Extractor and qFacet Fast Marching plugin for Cloud Compare software version V2.12. This study was used to show how the application of different methodologies, for the detection of geometric properties of discontinuities, influences the result. Statistical analyses were performed on the collected data to determine differences in the accuracy between the mapping techniques. Manual mapping on the 3D Point Cloud and high-resolution Textured Mesh Model showed good agreement with field measurements, apart from the higher number of discontinuities mapped by remote sensing methods. On the other hand, significant deviations were found between manual and semi-automated mapping techniques. Semiautomated methods did not correctly detect certain discontinuities, especially bedding planes that are perpendicular to a rock face. Also, semi-automated methods overestimate the number of discontinuity sets, especially in a highly weathered and highly fractured rock mass. These differences between methods can influence kinematic analysis results. Based on the results, an appropriate methodology was proposed to utilize the advantages of both manual and semiautomated methods. The proposed approach presents a powerful tool to accurately map and measure discontinuity orientation with results comparable to the field measurements.

Numerical modeling of open TBM tunneling in stratified rock masses using a coupled FDM-DEM method

With recent developments in understanding the adverse health effects of diesel particulate matter and growing emphasis on sustainability, zero-emission electric vehicles are becoming an increasingly common option in underground mining systems. As exposure regulations become stricter, and with potential savings in the cost of ventilation, fuel and consumables, there is also rising economic incentive to consider alternatives to diesel machinery. As a result, the diesel-electric debate is fundamental to any underground mining company's triple bottom line. A cost-benefit analysis for electric load haul dump units (eLHDs) was conducted in the context of Western Australian underground hard rock mines. This included a review of the issues affecting the diesel-electric debate and the development of a parametric life cycle cost model. The results indicate that eLHDs are not yet a universal solution to all underground mining systems. eLHDs can offer lower operating costs and do contribute many qualitative benefits, especially with respect to reduced exposure to diesel particulate matter. However they also have several drawbacks, primarily associated with trailing cable management. Nevertheless, with a suitable mine design, eLHDs are a viable option and could provide a pathway for zero-emission electric machinery in the Australian mining industry. Preamble - Western Australia is one of the world's leading mineral provinces. In the 2012-2013 financial year, Western Australia's mineral and petroleum sales totaled A$102billion, representing some 42% of Australia's total merchandise exports [1]. As such, changes to the Western Australian mining industry has national and international economic implications.

Designing a mine using room and pillar mining method (R&P) represents complicated task. Choosing the dimensions of pillar is a very sensitive subject, since it is directly connected with room stability and exploitation factor of ore body, because some part of ore body stays in pillars. This paper will compare multiple ways to correctly calculate dimensions of pillar and rooms, so that room stability wouldn't be endangered and, at the same time, to excavate as much ore body as possible, to enhance efficiency.

Non-explosive mining and waste utilization for achieving green mining in underground hard rock mine in China

Numerical modeling has evolved as a popular tool for the design of underground structures over the years. This paper presents design of horizontal crown pillar in a hard rock mine where a steeply dipping orebody is being mined. Three-dimensional continuum elastic model was developed to compute the stresses around the underground openings using 3DEC. Deatiled geological mapping was done to estimate the rock mass quality. Intact rock strength parameters were scaled to get rock mass strength parameters. Empirical rock failure criterion was used for analyzing the stability of the pillars in the central galaxy area of Khetri mine. The planned extraction of all stopes below the crown pillar would result in huge excavation of 200m × 110m × 30m. One stope of 30m-strike length is left to be extracted and no instability is reported to date.

With the exploitation of resources expanded to deep, the geological environment which is “three tenors one disturbance” of rock mass in great depth has been more complex, resulting in some new engineering geological disasters, such as rock burst, pressure bumping, large deformation of surrounding rock, brittle-ductile transition of rock and zonal disintegration of rock mass, which occur frequently in deep underground engineering rock mass. The impact load caused by collision, explosion, extrusion and outburst is the root cause of the dynamic instability of the deep rock mass. What should be emphasized is that high in-situ stress and blasting excavation disturbance complicate disaster developing mechanism of deep underground engineering rock mass and sharply increase the difficulty of controlling disaster. This paper is aimed at the research status and development trend, of which dynamic characteristics of deep high stress rock mass and its damage and failure effect each other under impact, and conduct analysis, in the later stage where I would discuss how to carry out the response law of the deep high-stress rock mass under the impact load and the mechanism of catastrophe developing, which is of great significance to build a model of instability and fracture evolution about deep rock mass under shock disturbance and to maintain its safety and stability.

Estimating penetration rates of Jumbo drills is crucial for optimizing underground mining drilling processes, aiming to reduce costs and time. This study investigates various regression and machine learning methods, including Multilayer Perceptron (MLP), Support Vector Regression (SVR), and Random Forests (RF), to predict the penetration rates (ROP) using multivariate inputs such as operation parameters and rock mass characteristics. The Rock Mass Drillability Index (RDi), incorporating both intact rock properties and structural parameters, was utilized to characterize the rock mass. The dataset was split into 80% for training and 20% for testing. Performance metrics including correlation coefficient (R2), variance accounted for (VAF), mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean square error (RMSE) were calculated for each method to evaluate the accuracy of the predictions. SVR exhibited the best prediction performance for ROP, achieving the highest R2, lowest RMSE, MAE, and MAPE, as well as the largest VAF values of 0.94, 0.15, 0.11, 4.84, and 94.13 during training, and 0.91, 0.19, 0.13, 6.02, and 91.11 during testing, respectively. With this high accuracy, we conclude that the proposed machine learning algorithms are valuable and efficient predictors for estimating jumbo drill penetration rates in underground mining operations.