Underground Limestone Mine Research Articles

Stability Analysis of Pillars in Underground Limestone Mine Using Three-Dimensional Scanning Techniques

Stability Analysis of Pillars in Underground Limestone Mine Using Three-Dimensional Scanning Techniques

A study on design and the stability assessment of underground limestone mine using numerical analysis

ABSTRACT In this study, numerical analysis was performed to design a drainage system, conduct a stability assessment of the drift, and construct a layout of the drift planned to be developed in the future. Moreover, we determined the factors that affected the stability of the drift in underground limestone mines. According to hydraulic analysis, the drifts planned for the future development of test bed mines are located underground, and the amount of the groundwater inflow rate in the drift is predicted to increase rapidly. Therefore, the numbers of pumps and drainage wells need to be doubled. Based on the stability assessment of the drift, stability could be ensured with a factor of safety of 1.5 or higher, with the exception of some sections. In addition, it was determined that a minimum factor of safety of 1.0 or higher could be maintained for widths up to 12 m based on considerations of future drift development. Finally, the effects on the factor of safety were analysed by changes in the levels of factors applied to the stability assessment of the drift.

Development of Autonomous Driving Patrol Robot for Improving Underground Mine Safety

To improve the working conditions in underground mines and eliminate the risk of human casualties, patrol robots that can operate autonomously are necessary. This study developed an autonomous patrol robot for underground mines and conducted field experiments at underground mine sites. The driving robot estimated its own location and autonomously operated via encoders, IMUs, and LiDAR sensors; it measured hazards using gas sensors, dust particle sensors, and thermal imaging cameras. The developed autonomous driving robot can perform waypoint-based path planning. It can also automatically return to the starting point after driving along waypoints sequentially. In addition, the robot acquires the dust and gas concentration levels along with thermal images and then combines them with location data to create an environmental map. The results of the field experiment conducted in an underground limestone mine in Korea are as follows. The O2 concentration was maintained at a constant level of 15.7%; toxic gases such as H2S, CO, and LEL were not detected; and thermal imaging data showed that humans could be detected. The maximum dust concentration in the experimental area was measured to be about 0.01 mg/m3, and the dust concentration was highly distributed in the 25–35 m section on the environmental map. This study is expected to improve the safety of work by exploring areas that are dangerous for humans to access using autonomous patrol robots and to improve productivity by automating exploration tasks.

Modified Design of Pillar Based on Estimated Stresses and Strength of Pillar in an Underground Limestone Mine

Modified Design of Pillar Based on Estimated Stresses and Strength of Pillar in an Underground Limestone Mine

Stability Analysis of Pillar in Underground Limestone Mine Using 3D Scanning Techniques

Stability Analysis of Pillar in Underground Limestone Mine Using 3D Scanning Techniques

Radar imaging of fractures and voids behind the walls of an underground mine

Two- and three-dimensional rock-penetrating-radar data were acquired on the wall of a pillar in an underground limestone mine. The objective was to test the ability of radar to image fractures and karst voids and to characterize their geometry, aperture, and fluid content, with the goal of mitigating mining hazards. Strong radar reflections in the field data correlate with fractures and a cave exposed on the pillar walls. Large pillar wall topography was included in the steep-dip Kirchhoff migration algorithm because standard elevation corrections are inaccurate. The depth-migrated 250 MHz radar images illuminate fractures, karst voids, and the far wall of the pillar up to approximately 25 m depth into the rock, with a spatial resolution of <0.5 m. Higher frequency radar improved the image resolution and aided in the interpretation, but at the cost of shallower depth of penetration and extra acquisition effort. Due to the strong contrast in physical properties between the rock and the fracture fluid, fractures with apertures as thin as a 50th of a radar wavelength were imaged. Water-filled fractures with mm-scale aperture and air-filled fractures with cm-scale apertures produce strong reflections at 250 MHz. A strong variation in the reflection amplitude along each fracture is interpreted to represent the variable fracture aperture and the nonplanar fracture structure. Fracture apertures were quantitatively measured, but distinguishing water from air-filled fractures was not possible due to the complex radar wavelet and fracture geometry. Two conjugate fracture sets were imaged. One of these fracture sets dominates the rock mass stability and water inrush challenges throughout the mine. All of the detected voids and a large cave are at the intersection of two fractures, indicating preferential water flow and dissolution along conjugate fracture intersections. Detecting, locating, and characterizing fractures and voids prior to excavation can enable miners to mitigate potential collapse and flood hazards before they occur.

Smart Helmet-Based Personnel Proximity Warning System for Improving Underground Mine Safety

A smart helmet-based wearable personnel proximity warning system was developed to prevent collisions between equipment and pedestrians in mines. The smart helmet worn by pedestrians receives signals transmitted by Bluetooth beacons attached to heavy equipment, light vehicles, or dangerous zones, and provides visual LED warnings to the pedestrians and operators simultaneously. A performance test of the proposed system was conducted in an underground limestone mine. It was confirmed that as the transmission power of the Bluetooth beacon increased, the Bluetooth low energy (BLE) signal detection distance of the system also increased. The average BLE signal detection distance was at least 10 m, regardless of the facing angle between the smart helmet and Bluetooth beacon. The subjective workload for the smartphone-, smart glasses-, and smart helmet-based proximity warning system (PWS) was evaluated using the National Aeronautics and Space Administration task load index. All six workload parameters were the lowest when using the smart helmet-based PWS. The smart helmet-based PWS can provide visual proximity warning alerts to both the equipment operator and the pedestrian, and it can be expanded to provide worker health monitoring and hazard awareness functions by adding sensors to the Arduino board.

Stochastic Predictions of Ore Production in an Underground Limestone Mine Using Different Probability Density Functions: A Comparative Study Using Big Data from ICT System

This study stochastically predicted ore production through discrete event simulation using four different probability density functions for truck travel times. An underground limestone mine was selected as the study area. The truck travel time was measured by analyzing the big data acquired from information and communications technology (ICT) systems in October 2018, and probability density functions (uniform, triangular, normal, and observed probability distribution of real data) were determined using statistical values. A discrete event simulation model for a truck haulage system was designed, and truck travel times were randomly generated using a Monte Carlo simulation. The ore production that stochastically predicted fifty times for each probability density function was analyzed and represented as a value of lower 10% (P10), 50% (P50), and 90% (P90). Ore production was underestimated when a uniform and triangular distribution was used, as the actual ore production was similar to that of P90. Conversely, the predicted ore production of P50 was relatively consistent with the actual ore production when using the normal and observed probability distribution of real data. The root mean squared error (RMSE) for predicting ore production for ten days in October 2018 was the lowest (24.9 ton/day) when using the observed probability distribution.

Analysis of the Impacts of Mining Sequence and Overburden Depth on Stability at a Dipping Limestone Mine

Ground falls represent a significant hazard at underground mines in the stone, sand, and gravel (SSG) sector in the USA. Researchers from the National Institute for Occupational Safety and Health (NIOSH) are currently conducting detailed investigations into the complex loading conditions at underground stone mines operating in challenging conditions. This paper presents the application of numerical modeling to analyze pillar and roof stability at a dipping underground limestone mine. A validated numerical model was used to explore the potential behavior of the pillars and roof as loading conditions change. The validated model was used to compare changes in mining sequence, overburden depth, and the in situ stress field. This will allow mine operators and engineers to have a better idea of the conditions that could be encountered as mining progresses. Results from the numerical modeling indicate that roof displacement more than doubles as the vertical stress increases from 10 MPa (1450 psi) to 19 MPa (2750 psi) when the maximum and minimum horizontal stresses were 41 MPa (5950 psi) and 22 MPa (3190 psi), respectively. Consequently, as the pillar load increases, the safety factor of the pillars is projected to decrease by about 25%. The impact of the practical application of numerical models can result in a reduction of ground-fall accidents and injuries as well as generally safer working conditions.

Monitoring of Multiple-level Stress Interaction at Two Underground Limestone Mines

The National Institute for Occupational Safety and Health (NIOSH) has previously established pillar design guidelines for shallow, flat-lying mines and single-level operations. Little guidance exists for ground control design in multiple-level stone mines, and understanding the interactions between levels would allow engineers to better select interburden thicknesses and the necessary amount of pillar columnization. To investigate these loading conditions in multiple-level environments, NIOSH has partnered with two separately operated multiple-level mines to study the stress interaction between the levels as undermining occurs. The first mine is located in Tennessee with up to a 243-m overburden and 7-m interburden thickness between levels. The second mine is located in Kentucky with a 304-m overburden and 26-m interburden thickness between levels. The monitoring program at these sites includes stressmeters and LiDAR for tracking stress redistributions and rock displacement in response to undermining. Monitoring is ongoing, but numerical modeling results show the expected interaction between levels.

Development of a multiple level underground limestone mine from geology through mine planning

The development of a multiple level underground mine is a complex task in which geology, engineering, ground control, and unit operations are integrated into a single mine design. The components are inter-dependent and must function cohesively to ensure a profitable underground mining operation. To optimize reserve recovery, mine planning should begin from the lowest level and progress up. This limits any misjudgments or oversights of a given level affecting the underlying levels and ensures the ability to maximize recovery from each level. Mine planning should start with the exploration and characterization of the geologic setting. Once the reserve geology and quality is well understood, then mine planning can begin with respect to the following: (1) orientation of mine works with respect to horizontal stress; (2) access to the reserve; (3) determination of opening widths; (4) selection of back, floor horizons and pillar centers; (5) selection of development and secondary mining heights; (6) appropriate inter-burden thicknesses; and (7) examining the stability of the multiple level mine through numerical modeling. The multiple level mine design process and decisions are presented through a case history example. The theme is that there is one opportunity to “get it right” and many chances to overlook a small aspect within the design that will plague the mine throughout all levels and through the entirety of its operating life.

Stability Assessment of an Underground Limestone Mine Considering Hydro-Mechanical Characteristics of Fractured Rock Mass

Stability Assessment of an Underground Limestone Mine Considering Hydro-Mechanical Characteristics of Fractured Rock Mass

Deep Neural Network for Ore Production and Crusher Utilization Prediction of Truck Haulage System in Underground Mine

A new method using a deep neural network (DNN) model is proposed to predict the ore production and crusher utilization of a truck haulage system in an underground mine. An underground limestone mine was selected as the study area, and the DNN model input/output nodes were designed to reflect the truck haulage system characteristics. Big data collected on-site for 1 month were processed to create learning datasets. To select the optimal DNN learning model, the numbers of hidden layers and hidden layer nodes were set to various values for analyzing the training and test data. The optimal DNN model structure for ore production prediction was set to five hidden layers and 40 hidden layer nodes. The test data exhibited a coefficient of determination of 0.99 and mean absolute percentage error (MAPE) of 2.80%. The optimal configuration for the crusher utilization prediction was set to four hidden layers and 40 hidden layer nodes, and the test data exhibited a coefficient of determination of 0.99 and MAPE of 2.49%. The trained DNN model was used to predict the ore production and crusher utilization, which were similar to the actual observed values.

Simulation of Truck Haulage Operations in an Underground Mine Using Big Data from an ICT-Based Mine Safety Management System

Information communication technology (ICT)-based mine safety management systems are being introduced at numerous mining sites to track the location of equipment and workers in real time and monitor environmental changes. This paper presents the results of a case study in which the big data created by an ICT-based mine safety management system are used for simulating truck haulage operations. An underground limestone mine located in Danyang, South Korea was studied, and the data generated over three months, from October 1 to December 31, 2018, were analyzed. Truck tag packet data recognized by relays were extracted and analyzed to calculate the averages and standard deviations of the truck travel times of each mine segment. A discrete event simulation program that simulates truck haulage operations in the study area was developed. Haulage times, the number of haulage operations, production output, and truck delay times were predicted, and results were compared with the actual operation results that were obtained on January 2 and 9, 2019. The difference between the predicted and actual results for the total amount of loaded ore was 30 tons for January 2 and 0 tons for January 9. The mean absolute error between the predicted and observed truck travel times was 0.13 min for January 2 and 0.14 min for January 9. The truck travel times that were measured differently according to the data aggregation period were set as temporal factors, and truck haulage simulations were performed. The results showed that more reliable simulation results were obtained as data accumulation time increased.

Analyzing the Stability of Underground Mines Using 3D Point Cloud Data and Discontinuum Numerical Analysis

This study describes a precise numerical analysis process by adopting the real image of mine openings obtained by light detection and ranging (LiDAR), which can produce a point cloud data by measuring the target surface numerically. The analysis target was a section of an underground limestone mine, to which a hybrid room-and-pillar mining method that was developed to improve ore recovery was applied. It is important that the center axis and the volume of the vertical safety pillar in the lower parts match those in the upper parts. The 3D survey of the target section verified that the center axis of the vertical safety pillar in the lower parts had deviated in a north-westerly direction. In particular, the area of the lower part of the vertical safety pillar was approximately 34 m2 lower than the designed cross-sectional area, which was 100 m2. In order to analyze the stability of the vertical safety pillar, a discontinuum numerical analysis and safety factor analysis were conducted using 3D surveying results. The analysis verified that instability was caused by the joints distributed around the vertical safety pillar. In conclusion, investigation of the 3D survey and 3D numerical analysis techniques performed in this study are expected to provide higher reliability than the current techniques used for establishing whether mining plans require new mining methods or safety measures.

Application of laser scanning for rock mass characterization and discrete fracture network generation in an underground limestone mine

Terrestrial laser scanning (TLS) is a useful technology for rock mass characterization. A laser scanner produces a massive point cloud of a scanned area, such as an exposed rock surface in an underground tunnel, with millimeter precision. The density of the point cloud depends on several parameters from both the TLS operational conditions and the specifications of the project, such as the resolution and the quality of the laser scan, the section of the tunnel, the distance between scanning stations, and the purpose of the scans. One purpose of the scan can be to characterize the rock mass and statistically analyze the discontinuities that compose it for further discontinuous modeling. In these instances, additional data processing and a detailed analysis should be performed on the point cloud to extract the parameters to define a discrete fracture network (DFN) for each discontinuity set. I-site studio is a point cloud processing software that allows users to edit and process laser scans. This software contains a set of geotechnical analysis tools that assist engineers during the structural mapping process, allowing for greater and more representative data regarding the structural information of the rock mass, which may be used for generating DFNs. This paper presents the procedures used during a laser scan for characterizing discontinuities in an underground limestone mine and the results of the scan as applied to the generation of DFNs for further discontinuous modeling.

Improving protection against respirable dust at an underground crusher booth

The U.S. National Institute for Occupational Safety and Health completed a 15-month study at an underground limestone mine crusher booth that evaluated three research parameters: (1) the effectiveness of a filtration and pressurization system for improving the air quality inside the operator booth, (2) the relative effectiveness of η > 99 and η > 95 experimental prototype filters in the system, and (3) the performance of three different cab pressure monitoring devices. The protection factor was quantified monthly using particle counters in the respirable dust range of 0.3 to 1 μm particle size, and gravimetric dust samples were gathered at the beginning and end of the overall study. Under static (closed-door) conditions, the filtration unit offered a gravimetric calculated protection factor between 10 and 31, depending on the filter type and loading condition. The monthly particle counting analysis shows that the η > 95 filter offers a protection factor nearly five times that of the η > 99 filter, where n = 15 samples. The booth pressure monitors were tested and proved to be a valid indicator of system performance over time.

Enhancing manual P-phase arrival detection and automatic onset time picking in a noisy microseismic data in underground mines

Accurate detection and picking of the P-phase onset time in noisy microseismic data from underground mines remains a big challenge. Reliable P-phase onset time picking is necessary for accurate source location needed for planning and rescue operations in the event of failures. In this paper, a new technique based on the discrete stationary wavelet transform (DSWT) and higher order statistics is proposed for processing noisy data from underground mines. The objectives of this method are to (i) improve manual detection and picking of P-phase onset; and (ii) provide an automatic means of detecting and picking P-phase onset time accurately. The DSWT is first used to filter the signal over several scales. The manual P-phase onset detection and picking are then obtained by computing the signal energy across selected scales with frequency bands that capture the signal of interest. The automatic P-phase onset, on the other hand, is achieved by using skewness- and kurtosis-based criterion applied to selected scales in a time-frequency domain. The method was tested using synthetic and field data from an underground limestone mine. Results were compared with results obtained by using the short-term to long-term average (STA/LTA) ratio and that by Reference Ge et al. (2009). The results show that the method provides a more reliable estimate of the P-phase onset arrival than the STA/LTA method when the signal to noise ratio is very low. Also, the results obtained from the field data matched accurately with the results from Reference Ge et al. (2009).

Analysis of extensometer, photogrammetry and laser scanning monitoring techniques for measuring floor heave in an underground limestone mine

An underground limestone mine in eastern Ohio was experiencing significant floor heave and roof falls, attributed to high horizontal stresses. Areas of the mine showing floor heave were monitored with roof-to-floor extensometers and photogrammetry surveys to determine the rate and magnitude of heave. Extensometer data were recorded hourly at four locations across adjacent entries while photogrammetry surveys of the floor were performed at the same locations every two to five weeks. A final survey was performed using an I-Site 8200 laser scanner. Following instrumentation, floor heave up to 10.1 cm (4 in.) was measured by the extensometers, photogrammetric reconstructions and laser scanner over a six-month period. The extensometers were biased by the location where they were placed, failing to consistently capture the location and extent of floor heave and cracking. The photogrammetry surveys were not precise enough to capture small magnitude movements. Mining in the area was halted and within several months the floor movement and incidence of roof falls were significantly lessened.

Empirical Analysis on the Correlation Between Mono-crystalline Si Photovoltaic Module Temperature and Irradiance and Ambient Temperature in Seoul Area

This study presents a method for calculating carbon-dioxide emissions of diesel trucks during haulage operations by considering road gradient, load capacity and emission standards. An underground limestone mine in Korea was selected as a study area to apply the method. In the study area, variations of emission factors were observed along haulage routes between a crusher and two loading points. The method could simulate the changes of emission factors reasonably with regard to road gradient and load capacity. Along the same haulage route, carbon-dioxide emission decreased by 4-6% with the change of current trucks to newer ones with improved emission standards. The suggested method can be used for the analysis with other vehicle types and greenhouse gases. Therefore, this method can be used for general estimation of greenhouse gas emissions.