Open Stopes Research Articles

Optimization of Crown Pillar Thickness in the Stress Relaxation Zone Surrounding Sub-Level Open Stopes

Optimization of Crown Pillar Thickness in the Stress Relaxation Zone Surrounding Sub-Level Open Stopes

Enhancing open stope stability prediction in mining engineering: Optimal configuration of an artificial neural network model

Ensuring the stability of underground excavations is a crucial concern in mining engineering, directly impacting operational safety and efficiency. Given the dynamic nature of the subsurface environment, innovative methods are essential to enhance stability in open stopes and mitigate potential risks. This study focuses on refining predictions of underground excavation stability in mining engineering through the optimization of an artificial neural network (ANN) model. Analyzing Potvin’s database, which includes 175 historical cases, we examined the impact of various ANN model configurations. Our findings indicate that normalizing data using Standard Scaler and employing Swish as the activation function across all layers yielded the most accurate predictions for this specific scenario. Additionally, employing the SHAP (Shapley Additive exPlanations) tool enabled us to assess feature importance and identify the most influential factors. Our results underscore the significant impact of the shape factor on underground opening stability, closely followed by the Q value. This research contributes to enhancing predictive models for underground mining excavation stability, emphasizing the critical role of specific parameters affecting open stope stability.

Experimental and numerical assessment for the roof-contacted filling effect of expansive backfill

The effect of roof-contacted filling is a significant factor that affects the ground pressure management in the filling mining method. The expansive backfill is developed to solve the problem of the difficulties of roof-contacted filling in the open stopes with subsequent filling mining method, as well as actively supporting the surrounding rock masses by expanding its own volume and generating certain pre-stress to the roof. This paper focuses on analyzing the mechanical effects of roof-contacted filling with expansive backfill using a simulation test. Subsequently, the results of the simulation test were validated through theoretical analysis and numerical simulation. The results showed that: (1) The roof of the roof-contacted unfilling (RCU) mining room and the roof of the roof-contacted filling with ordinary backfill (RCO) mining room were both under downward stress whereas the roof of the roof-contacted filling with expansive backfill (RCE) mining room was supported by upward stress. (2) The RCU mining room led to a stress concentration in the roof and pillar, whereas the RCO mining room relieved the stress concentration in the roof and pillar. The RCE mining room did not only relieve the stress concentration in the roof and pillar, but also provide support to the roof and pillar. (3) The expansion stress generated by the self-expansion of the expansive backfill played a direct supporting role to the roof and the upper part of the pillar, and produced a positive Poisson effect to the lower filling body, which in turn provided support to the middle and lower part of the pillar. (4) Compared to RCU and RUO, the RCE enables the filling body to change from passive bearing to active support of the roof and pillar. This transition allowed more effective limitation of roof and pillar deformation while working in coordination with them to improve stress distribution within the mining room. As a result, the stability of the mining room was maintained, and the effect of roof-contacted operations was significantly enhanced.

Stability analysis of crown pillar under the zone of relaxation around sub-level open stopes using numerical modelling

The stability of stopes and pillars is a critical concern in deep underground metal mining due to the potential for stress concentration and relaxation that can cause the dynamic failure of pillars. This study employs a finite element approach to investigate the behaviour of crown pillars to various stress and geo-mining parameters. The analysis includes examining stresses, deformations, and yielding zones around the crown pillars using variations in rock mass parameters. The study involves 240 non-linear numerical models with the Mohr–Coulomb elastoplastic failure criteria under plane strain conditions. The results indicate that increasing the crown pillar thickness from 5 to 8 m reduces maximum vertical deformation by 55.8% and the maximum extent of yielding zones from 2.2 to 0 m. The study suggests a 6 m crown pillar for depths up to 670.56 m and thicker pillars of 7 m or more for greater depths.

Effects of fragmentation on production at Peak mine Midlands, Zimbabwe

Over the years, the cost of production at Peak Mine Midlands, Zimbabwe, has been high due to oversized rock fragments. This led to increased mining costs far above what was planned by the mine. Initial investigations showed that valuable production hours were lost in trying to deal with the oversized rock fragments at the draw points. This study was conducted at Peak Mine Midlands, Zimbabwe, on 1210 level. The methods used to assess the effects of fragmentation on production at the mine included visual assessment, analysis of the drill and blasting costs of the production data, 2D image analysis, sieve analysis, time and motion studies, analysis of the explosive’s consumption, mining blasting trials and post-mine blast experiments. The results show that the current ring hole designs used in the sublevel open stopes at Peak Mine do not give optimal particle size distribution after the primary blasts as the sieve analyses show that 50% of the sizes of materials at the draw points are larger than 1,000 mm. While 4 secondary blasts were budgeted per shift to reduce the sizes of the boulders to sizes that will pass through the grizzly’s sieves, 7 secondary blasts were done per shift and this led to about 75% increase in the cost of explosives consumed by the mine. The results also show that 35.96 minutes of production time were lost on each secondary blast conducted. Four trial blasts conducted on different ring hole patterns show that ring #1 which has a burden of 1.2 m instead of 1.8 m in the other ring holes led to a significant reduction in drilling and blasting costs from USD $33.79 in trial #4 (the control case) to $28.80 in trial #1. The sieve analyses also show that the optimal fragment sizes from primary blasts are in the range of X = +200 mm and X = +300 mm. So, the designed ring hole patterns in the sublevel open stopes should produce particles with sizes in the range of  150 mm and  +350 mm from the primary blasts.

Numerical analysis for assessing the effects of crown pillar thickness on ore dilution around the sub-level open stopes

Sub-level open stopes with delayed backfill are being used to extract massive, tabular, and steeply dipping ore bodies for increased productivity. Crown pillars in the orebody divide mining zones to increase productivity by enabling simultaneous production in two or more zones. Despite their effectiveness, these mining techniques may be accompanied by substantial amounts of overbreak, resulting in considerable ore dilution. This study investigates the effects of crown pillar thickness under various geo-mining conditions on hanging wall overbreak. Under plane strain conditions, 240 non-linear finite element models with the Mohr-Coulomb elastoplastic failure criteria were used for numerical analysis. The outcomes are analyzed in terms of Equivalent Linear Relaxation Depth (ELRD) and Equivalent Linear Overbreak Sloughage (ELOS). The results show that by increasing the crown pillar thickness from 5 m to 8 m, the ELRD and ELOS are reduced by roughly 45 % and 74 %, respectively, for a given material property.

Prediction of dilution in sublevel stoping through machine learning algorithms

One of the most used underground mining methods is open stope mining which involves extracting a large body of ore through drilling and blasting. The method offers plenty of advantages but it has some very important drawbacks, such as overbreak, wall instability and unplanned ore dilution. The research looks to test the efficiency of using machine learning algorithms to estimate the dilution in open stopes, some of the expected benefits are reduced time cost (compared to numerical analysis) and more accurate results (compared to empirical methods). The algorithms utilized are: random forest (RF), support vector machine (SVM) and k-nearest neighbors (KNN). The algorithms were trained and tested with 752 cases from several mines across the world. Three algorithms accomplished AUC scores over 0.850, which can be considered excellent results, but random forest achieved the most impressive results (precision score = 0.835, accuracy score = 0.804 AUC score = 0.942). From the obtained results it is possible to conclude that the machine learning algorithms can be used as trustworthy tools for the estimation of dilution, but some adjustments may be needed to increase the accuracy to specific mine sites.

Predicting the stability of open stopes using Machine Learning

The Mathews stability graph method was presented for the first time in 1980. This method was developed to assess the stability of open stopes in different underground conditions, and it has an impact on evaluating the safety of underground excavations. With the development of technology and growing experience in applying computer sciences in various research disciplines, mining engineering could significantly benefit by using Machine Learning. Applying those ML algorithms to predict the stability of open stopes in underground excavations is a new approach that could replace the original graph method and should be investigated. In this research, a Potvin database that consisted of 176 historical case studies was passed to the two most popular Machine Learning algorithms: Logistic Regression and Random Forest, to compare their predicting capabilities. The results obtained showed that those algorithms can indicate the stability of underground openings, especially Random Forest, which, in examined data, performed slightly better than Logistic Regression.

Unplanned dilution prediction in open stope mining: developing new design charts using Artificial Neural Network classifier

Minimizing dilution is essential in open stope mine design as excessive unplanned dilution can compromise the operation's profitability. One of the main challenges associated with the empirical dilution graph method used to design open stopes is how to determine the boundary of the dilution zones objectively. Hence, this paper explores the implementation of machine learning classifiers to bridge this gap in the conventional dilution graph method. Stope performance data consisting of the stope dilution (unplanned dilution), the modified stability number, and the hydraulic radius were compiled from a mine located in Kazakhstan. First, the conventional dilution graph methods were used to assess the dilution. Next, a Feed-Forward Neural Network (FFNN) classifier was implemented to predict each level of dilution. Overall, the FFNN results indicated that 97% of the stope surfaces were correctly classified, indicating an excellent classification performance, while the conventional dilution graph method did not show a good performance. In addition, the outputs of the FFNN were used to plot new dilution graphs with a probabilistic interpretation illustrating its practicability. It was concluded that the FFNN-based classifier could be a useful tool for open stope design in underground mines.

Dust pollution hazard and harmful airborne dust exposure assessment for remote LHD operator in underground lead-zinc ore mine open stope.

Underground mines embroil several occupational hazards, including airborne dust generation from various mining operations. Line-of-sight remote Load Haul Dumper (LHD) mucking is adopted to draw the blasted muck from unsupported open stopes in underground metalliferous mines. Assessment of particulate matter (PM) concentrations and remote LHD operator's exposure is crucial for devising appropriate dust control measures. In this study, PM generated due to mucking in longhole open stope by line-of-sight remote LHD during downcast airflow was measured using real-time aerosol spectrometers. The particulate concentrations at upstream and downstream of dust source were analysed for various particle sizes as well as occupational dust types, such as alveolic and thoracic. The airborne dust concentration of ≤ 10μm (PM10), ≤ 5μm, and ≤ 1μm (PM1) size at operator's location in downstream was measured 71.3%, 28.5%, and 3.0%, respectively. The alveolic and thoracic dust types, respectively, were determined 25.1% and 74.2% in downstream and 48.9% and 84.6% in upstream total airborne dust concentration (311 ± 246μg/m3). Dilution of airborne dust generated due to muck sliding inside the stope was analysed with time. Moreover, dust concentrations under typical airflow scenarios encountered in open stope were simulated using Ventsim software to identify the potential dust exposure hazard for remote LHD operator. The simulation revealed that downcast airflow causes maximum exposure of harmful airborne dust for remote LHD operator. This study enhanced the understanding of exposure potential of airborne dust during remote LHD mucking. Moreover, it emphasised adoption of tele-remote-operated LHD and automated mucking operation in open stopes.

PlatMine pillar strength formula for the UG2 Reef

The Upper Group 2 (UG2) chromitite reef is a shallow-dipping stratiform tabular orebody in the South African Bushveld Complex, which strikes for hundreds of kilometres. Mining is extensive, with depths ranging from close-to-surface to 2 500 m. Pillars are widely used to support the open stopes and bords. Little work has been done in the past to determine the strength of pillars on the UG2 Reef and design was done using formulae developed for other hard-rock mines. This has led to oversized pillars with consequent sterilization of ore. In this paper we describe a back-analysis of stable and failed UG2 pillars on the Bushveld platinum mines, and provides a strength formula for UG2 pillars. The formula may be used cautiously on all Bushveld platinum mines with similar geotechnical, geometrical, and geomechanical conditions to the pillars in the database.

Development of a knowledge-based system for assessing unplanned dilution in open stopes

A reliable estimation of unplanned dilution is one of the most vital parameters in open stopping mining methods as unplanned dilution and sloughing can lead to an increase of the cost production and most importantly to safety concerns. Owing to this importance, several predictive models to estimate unplanned dilution and stope overbreak have been proposed. However, some of these existing models do not provide any physical meaning of the underlying dilution problem. To overcome this limitation, a fuzzy inference system (FIS) is proposed. In this paper, a new design tool capable of assessing unplanned dilution in a reliable manner is developed. The methodology relies on a fuzzy inference system (FIS) as many epistemic uncertainties that are associated with unplanned dilution are not systematically accounted for in existing methods. To this end, a dilution database from Ridder-Sokolny mine in Kazakhstan has been compiled. It consists of unplanned dilution cases and includes stope geometry, rock mass characteristics and stope reconciliation data. Subsequently, knowledge-based FIS was implemented. Overall, the results of the FIS show good classification performance (84% of accuracy). In comparison to the dilution graph developed based on traditional method, the FIS results show better performance. Hence, it was concluded that the proposed FIS could be an alternative tool for empirical open stope design.

A Feasibility Study on The Implementation of Neural Network Classifiers for Open Stope Design

Assessing the stability of stopes is essential in open stope mine design as unstable hangingwalls and footwalls lead to sloughing, unplanned stope dilution, and safety concerns compromising the profitability of the mine. Over the past few decades, numerous empirical tools have been developed to dimension open stope in connection with its stability, using the stability graph method. However, one of the principal limitations of the stability graph method is to objectively determine the boundary of the stability zones, and gain a clear probabilistic interpretation of the graph. To overcome this issue, this paper aims to explore the feasibility of artificial neural network (ANN) based classifiers for the design of open stopes. A stope stability database was compiled and included the stope dimensions, rock mass properties, and the stope stability conditions. The main parameters included the modified stability number (N’), and the stope stability conditions (stable, unstable, and failed), and hydraulic radius (HR). A feed-forward neural network (FFNN) classifier containing two hidden layers (110 neurons each) was employed to identify the stope stability conditions. Overall, the outcome of the analysis showed good agreement with the field data; most stope surfaces were correctly predicted with an average accuracy of 91%. This shows an improvement over using the existing stability graph method. In addition, for a better interpretation of the results, the associated probability of occurrence of stable, unstable, or caved stope was determined and shown in iso-probability contour charts which were compared with the stability graph. The proposed FFNN-based classifier outperformed the conventional stability graph method in terms of accuracy and better prabablistic interpretation. It is suggested that the classifier could be a reliable tool that can complement the conventional stability graph for the design of open stopes.

Stope stability assessment by the Mathews–Potvin method: a case-study of open stoping in salt rock mass under conditions of secondary stress field

Predicting the stability of open stopes is a challenging task for underground mine engineers. The introductory part of this paper presents main issues related to stability and safety of soil and rock mass in course of large earthworks and underground works. On the basis of former studies, this paper reviews the research findings on stope stability by the methods of Mathews and Potvin in the stoping design for rock salt mines operating at depths greater than 1 km under conditions of secondary stress fields, as well as the rock mass quality assessment by Barton’s method. The conservative stope design has been performed for a few scenarios of design parameters. In conclusions, it is underlined by the stability graph at the given parameters, that in the first case-study scenario, HR is 4.5 m and the Mathews–Potvin stability number is 68 m. It is concluded that the stopes will be stable and preserve their shapes. On the basis of presented case study (stope stability assessment in the test halite–polyhalite–polymineral salt deposit was carried out for a few scenarios at maximal effective stresses and under conditions of jointing), the authors provide the general provisions on salt rock mass assessment at great depths.

Open stope stability assessment through artificial intelligence

Abstract Underground mining is a set of methods that allows the extraction of ore in depth, ensuring sustainability and economic viability. One of the problems that arise in underground mine operations is open stope stability. The method for assessing stabil ity of open stopes is the stability graph proposed by Mathews et al. (1981). It is possible to estimate and provide information about this stability and assist in the decision mak ing about its viability. With the data obtained from 35 open stopes from a Zinc mine, the present study aims to use artificial intelligence techniques, specifically artificial neural networks, to process the data and classify the open stopes according to the sta bility regions of the graph. As a result, the applied methodology presented good asser tiveness for the classification of two classes, stable and unstable open stopes, resulting in a global probability success of 82% overall hit probability and 18% apparent error rate. For the classification into three classes, adding the transitional open stopes, the internal validation presented a global probability success of 91% and apparent error rate of 9%. In external validation, the network evaluation measures presented values of global probability success of 42% and apparent error rate of 58%.

A New Criterion for Numerical Modelling of Hangingwall Overbreak in Open Stopes

Determining stability, quantifying planned dilution, and estimating the potential dilution associated with hangingwall overbreak are critical in the process of stope design in sublevel open stoping mines. To satisfy these objectives, empirical stability graphs and numerical modelling are currently used in the mining industry. Empirical methods are limited to the database used to calibrate them. In the case of numerical modelling, some of the available criteria used to evaluate hangingwall overbreak do not include the intermediate principal stress around the stope and/or the rock mass geotechnical characteristics. In this study, a new criterion for numerical modelling is proposed to estimate the hangingwall overbreak in open stopes. This new criterion includes the intermediate principal stress around the stope and the rock mass geotechnical characteristics. To develop the criterion, several open stope numerical models are simulated considering different geometrical and geotechnical conditions. The criterion is calibrated to reproduce empirical case histories of hangingwall overbreak. Next, the criterion is verified with case histories of hangingwall overbreak that presented different conditions used to calibrate the criterion. The proposed criterion establish a significant influence and relationship between rock quality and the minimum and intermediate principal stresses on hangingwall overbreak. The criterion offers sufficient flexibility for application to a wide range of geometries, in situ stress conditions, and depth and rock mass properties.

Factors Influencing Ore Recovery and Unplanned Dilution in Sublevel Open Stopes. Case study of Shaft No.4 at Konkola Mine, Zambia

Konkola Copper Mine’s Number 4 Shaft is a trackless underground mine applying sublevel open stoping (SLOS) mining method. Number 4 shaft wants to increase ore production from 1 million metric tonnes per annum to 3 million metric tonnes per annum in the next 5 years but ore recovery is 70% or less and dilution is 20% or more. In order to achieve the desired annual target of 3 million metric tonnes ore recovery should be increased from70% to (≥85%) and dilution should be reduced from 20% to (≤10%). Despite being one of the most used underground mining methods, the current SLOS has a challenge of high unplanned dilution. This paper reviews and evaluates parameters that influence recovery and unplanned dilution in sublevel open stopes and applies numerical modelling using PHASE2 software to establish the influence of stress environment on unplanned dilution at the mine. The input parameters for numerical modelling were: Uniaxial Compressive strength (UCS=170MPa), Geological Strength Index (GSI) =55, Young’s Modulus (E) =26000MPa, Hoek-Brown constant (s) =0.0067, Hoek-Brown constant (mi) =20 and Poisson ratio (v) =0.2 major principal stress (σ1) 39MPa, intermediate stress (σ2= 18MPa) and the minor principal stress (σ3= 15MPa). Results obtained from review of mine production records indicate that the main factors that influence unplanned dilution at Number 4 shaft are: poor ground conditions, lack of compliance to recommended stope designs, poor drilling and blasting practices, presence of geological discontinuities, adopted mining sequence of extracting high ore grade first that leads to creation of high stress blocks within the orebody and delayed mucking practice. Results obtained from PHASE 2D model indicate that total displacement of 90mm is recorded in the hangingwall hence influencing stope wall instability that leads to increased unplanned dilution. After stope extraction, it was observed that 60MPa of induced stress developed at the top right corner and 45-50 MPa at the crown pillar and right bottom corner of the stope.

Identifying and mapping potentially adverse discontinuities in underground excavations using thermal and multispectral UAV imagery

Geotechnical mapping, rock mass characterization, and inspections of underground excavations are critical to ensuring that excavation geometry and ground support design are appropriate for stable conditions. A lack of access, poor timing, or hazardous conditions are all factors that can prohibit engineering personnel from collecting high-quality data in the field. Advances in unmanned aerial vehicles (UAV) and portable sensor technology now allow engineering personnel to remotely capture data from underground workings that can be used for developing designs, generating inputs for analyses, and making more informed decisions. Ground falls are a source of many accidents in the underground environment, and they are the focus of many geotechnical investigations and analyses. In this study, the authors investigated how portable UAV-mounted thermal and multispectral imagers could be used to detect and quantify adverse geological discontinuities in hard rock masses that are structurally controlled. Multiple thermal, multispectral, RGB (red, green, and blue), and LiDAR (light detection and ranging) data sets were captured from 5 flights in sub-level open stopes at the Barrick Golden Sunlight Mine in the state of Montana in the western United States of America. Using off-the-shelf software, the thermal, RGB, and multispectral images were processed to create individual three-dimensional point clouds and meshes, which were georeferenced using the LiDAR data. Discontinuities identified in the three-dimensional point clouds and meshes were mapped using tools found in the freeware CloudCompare. One of the thermal models identified wedges that had developed in the crown pillar of an open stope, and 4 of the multispectral models contained enough detail for mapping individual discontinuities. The results of this investigation indicate that these portable imagers are viable tools that can be used to aid engineering personnel in identifying and mapping adverse geological discontinuities and unique rock mass composition. The techniques for capturing, processing, and interpreting thermal and multispectral imagery captured in underground excavations are described in this paper and can be used as the basis for future investigations at individual underground sites.

Use of Probabilistic Numerical Modeling to Evaluate the Effect of Geomechanical Parameter Variability on the Probability of Open-Stope Failure: A Case Study of the Niobec Mine, Quebec (Canada)

The inherent variability of the geomechanical parameters of a rock mass plays a critical role in affecting underground mine stability. Neglecting this characteristic of a rock mass oversimplifies stability assessment and provides potentially inaccurate results as the actual behavior of rock mass is not considered. Use of probabilistic methods in conjunction with numerical analysis is a reliable approach for evaluating the effect of geomechanical parameter variability on the different modes of underground instability. The degree of stability can be expressed by the probability of failure for different stress-induced instability modes. Here, we use probabilistic methods (random Monte Carlo and Monte Carlo simulations) coupled with the finite difference code FLAC3D to incorporate the variability of rock mass geomechanical parameters into numerical analysis. We assess the stability of seven existing primary open stopes at mining levels V and VI at the Niobec Mine, Quebec, Canada. The stability around each open stope is evaluated by calculating the tensile and compressive probabilities of failure, based on the Hoek–Brown tensile and compressive safety factor, and the probability of brittle damage initiation (PDI), via the brittle shear ratio. For the evaluated open stopes, tensile and compressive failures share similar probabilities of occurrence. Considering the PDI values around all the open stopes, no brittle failure is expected to occur under the existing conditions of rock mass quality and in situ stress regime at the Niobec Mine. Comparison of these probabilistic numerical model results with those run using a deterministic numerical approach highlights the effect of variability of rock mass geomechanical parameters on stope stability.

Development of cemented paste backfill based on the addition of three mineral additions using the mixture design modeling approach

Large-scale mining operations have produced large amounts of iron ore tailings (IOT) and stopes. IOT will cause air and water pollution, and open stopes may lead to surface subsidence or even geological disasters. Using cemented paste backfill (CPB) which is composed of IOT and binders to fill stopes can well solve the above two problems. Based on the alkali-activated theory, slag and desulfurized gypsum are selected as basic materials, cement (C), lime (L) and silica fume (SF) are chosen as mineral activators. Mixture design, an experimental design method, is used to model the effect of C, L, and SF on the spread, compressive strength and shrinkage ratio of CPB. The models are helpful to know the interaction between three mineral additions. Results show that the optimal spread is reached when the content of C and SF are 8% and 5%, respectively in C-SF binary system. The compressive strength at 3,7,28 days increase and then decrease with the increase of each admixture in L-SF and C-SF binary system. The maximum compressive strength at 28 days is 0.83 MPa, which can provide support for the adjacent mine structure and keep a stable working platform for miners. The shrinkage ratio decreases with the increase of SF content and increases with the increase of C content in C-SF binary system. However, in C-L binary system, the shrinkage ratio increases with the increase of L or C content. The minimum shrinkage ratio is 1.16% (corresponding to the proportion of 7% of C, 3% of L and 5% of SF). Finally, the morphology of hydration products at different ages are compared by scanning electron microscopy (SEM). The results show that the derived models are very effective in explaining the influence of three mineral admixtures on the workability and mechanical property of CPB. This design method is a useful tool to select the optimal mix proportions to achieve engineering objectives and reduce cost.