Review Paper
Environment
Saahil Hembrom; Neeta Kumari
Abstract
Mining activities adversely affect the groundwater quality. Human health also subsequently gets affected because of many environmental and ecological risks due to mobilization of contaminants and alteration of hydrogeochemical processes. This review assesses the hydrogeochemical characteristics and groundwater ...
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Mining activities adversely affect the groundwater quality. Human health also subsequently gets affected because of many environmental and ecological risks due to mobilization of contaminants and alteration of hydrogeochemical processes. This review assesses the hydrogeochemical characteristics and groundwater quality in mining areas emphasizing the crucial processes like rock-water interaction, acid mine drainage formation, and heavy metal contamination. These processes impact the end uses of groundwater quality like drinking, irrigation and industrial uses. To understand the causes of contamination and the availability and suitability of the water, groundwater investigation is required such as assessment of physicochemical parameters and hydrogeochemical faces. By using isotopic techniques and integration of spatial and temporal changes with remote sensing and GIS application, pollution load can be evaluated on water resources. A bibliographic analysis highlights the current research progress in mining sector, focusing on global and regional studies and their impact on water resources. Contamination from heavy metals like arsenic, chromium, cadmium, and other toxic elements has posed serious illnesses to human health and the surrounding ecosystem. The review also highlights the research gaps and prospects for improving groundwater resources through appropriate mitigation strategies like sustainable mining practices and water treatment technologies.
Original Research Paper
Exploration
BALBIR NAGAL; Ajay Krishna Prabhakar; Mahesh Pal
Abstract
This study delineates groundwater potential (GWP) zones across Haryana, India, for the year 2023 using geospatial techniques integrated with the analytical hierarchy process (AHP). Multiple thematic layers, including slope, land use/land cover (LULC), soil, geology, drainage density (DD), lineament density ...
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This study delineates groundwater potential (GWP) zones across Haryana, India, for the year 2023 using geospatial techniques integrated with the analytical hierarchy process (AHP). Multiple thematic layers, including slope, land use/land cover (LULC), soil, geology, drainage density (DD), lineament density (LD), elevation, rainfall, and topographic wetness index (TWI), were generated using datasets from SRTM, Sentinel-2, food and agriculture organization (FAO), and the India meteorological department (IMD) and weighted through the AHP. These layers were integrated using weighted overlay analysis (WOA) to generate the final GWP map. The GWP map was validated against field groundwater level (GWL) data from 646 wells recorded in 2018 by the central ground water board (CGWB), resulting in an accuracy of 77.55 percent. This confirmed the reliability of the geographic information system (GIS) and AHP technique. The study reveals that moderate GWP zones dominate (43.71%) the region, followed by high (33.24%) and very high (11.96%) zones, whereas low and very low GWP zones cover 7.59% and 3.51% of the area, respectively. The findings indicate that Haryana’s groundwater distribution is largely stable, with minor variation observed between 2018 and 2023. This shows stable aquifer behaviour and relatively unchanged recharge and extraction patterns over the five-year period. The outcomes of this study are valuable for strategic groundwater management, especially in arid and semiarid regions of Haryana state.
Original Research Paper
Exploration
Satyajeet Parida; Abhishek Kumar Tripathi; Tarek Salem Abdennaji; Yewuhalashet Fissha
Abstract
Coal quality is predominantly determined by its Gross Calorific Value (GCV), which directly influences its economic valuation. Traditional empirical formulas for GCV estimation, though effective, become inefficient and laborious when handling large datasets. To address this, machine learning (ML) techniques ...
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Coal quality is predominantly determined by its Gross Calorific Value (GCV), which directly influences its economic valuation. Traditional empirical formulas for GCV estimation, though effective, become inefficient and laborious when handling large datasets. To address this, machine learning (ML) techniques offer a robust alternative for accurate and rapid predictions. This study employs seven coal quality parameters. Total Moisture (TM), Ash (ASH), Volatile Matter (VM), Hydrogen (H), Carbon (C), Nitrogen (N), and Sulphur (S), as independent variables to develop predictive models for GCV. Four conventional regression techniques, namely Support Vector Regression (SVR), K-Nearest Neighbors (KNN), Random Forest (RF), and Decision Tree (DT), along with two robust regression models Random Sample Consensus (RANSAC) and Huber Regressor (HR) are explored. The dataset comprises coal samples from five Asia-Pacific countries: China, Indonesia, Korea, the Philippines, and Thailand. Comparative performance analysis reveals that the robust regression models significantly outperform the conventional ML techniques. The RANSAC and Huber Regressor models achieve superior prediction accuracy with R² values of 0.9941 and 0.9952, respectively. These findings highlight the potential of robust regression approaches for reliable GCV estimation, facilitating efficient coal quality assessment in large-scale applications.
Case Study
Exploitation
Victor Patson Mutambo
Abstract
This study evaluates rock mass ratings, rock strength parameters, and the geological structures of the dominant rock units alongside a quantitative assessment of the performance of various anchor systems for enhanced ground support in mine excavations located within the Synclinorium area. This region ...
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This study evaluates rock mass ratings, rock strength parameters, and the geological structures of the dominant rock units alongside a quantitative assessment of the performance of various anchor systems for enhanced ground support in mine excavations located within the Synclinorium area. This region is notable for its complex, folded, and mineralized formations. The deeper levels of the synclinorium are characterised by poor ground conditions, faults, and shear zones. Stress induced by mining activities worsens the situation. These factors have significantly impacted the stability of excavation. Fall-of-ground (FOG) incidents have exhibited a concerning increase over the past nine years. This trend necessitates a thorough investigation into the factors contributing to it. Our research employed empirical methods for rock mass classification, specifically utilising Barton’s Q system and Bieniawski and Scanline mapping of geological structures along the crosscut walls at a 1.50 m elevation. We conducted borehole logging and pull-out tests to evaluate the working and ultimate capacities of rock bolt anchors deployed in the excavations. Borehole cores were analysed for geological formations, colour, and grain size. The findings indicate that excavations in areas with mined-through rock and stone necessitate urgent and intensive roof support to stabilise the surrounding rock mass, thereby enhancing standing time. Additionally, we identified joint patterns, joint orientations, and the various stresses affecting the surrounding rock mass in the crosscuts. The above highlights the importance of geological data in the design of effective ground control and support mechanisms. Pull-out testing conducted at the 3360 level recorded a 28.6% failure rate in primary development despite very competent ground.
Original Research Paper
Mineral Processing
Mohamed Taha Osman Abdelraheem; Salih Aydogan; Babiker Alkloosi; Mustafa Boyrazli
Abstract
This article describes the kinetics of utilizing ammonium nitrate to dissolve pure metallic silver in hydrogen peroxide solution (H2O2). Using pure metallic silver allows for precise leaching kinetics research by removing interference from impurities and facilitating accurate interpretation of rate-controlling ...
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This article describes the kinetics of utilizing ammonium nitrate to dissolve pure metallic silver in hydrogen peroxide solution (H2O2). Using pure metallic silver allows for precise leaching kinetics research by removing interference from impurities and facilitating accurate interpretation of rate-controlling mechanisms. The impact of temperature, rotation speed, H2O2 concentration, and ammonium nitrate concentration were all examined. The results show a favorable relationship between the rate of silver (Ag) dissolution and the rotation speed. Additionally, a low concentration of ammonium nitrate (between 0.003 and 0.20 M) has advantageous effects on Ag dissolution. The dissolution rate was significantly impacted by H2O2 concentrations between 0.08 and 0.15 M, because this range of H₂O₂ concentration required to provide sufficient oxidative potential for significant silver solubility. However, this effect is less pronounced in the 0.20–0.50 M range. 20 - 50 °C range of temperatures are advantageous since H2O2 is stable in this range. It was calculated that the activation energy was 25.66 kJ/mol.
Original Research Paper
Environment
Nanang Suparman; Muhammad Andi Septiadi; Yuflih Rizkia Timoty; Faizal Pikri
Abstract
This study aims to analyse the regulatory hierarchy and its implications within the regional autonomy regime in the context of bauxite mining management in Indonesia, with a focus on Tanjungpinang City. Although decentralization grants local governments the authority to manage natural resources, overlapping ...
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This study aims to analyse the regulatory hierarchy and its implications within the regional autonomy regime in the context of bauxite mining management in Indonesia, with a focus on Tanjungpinang City. Although decentralization grants local governments the authority to manage natural resources, overlapping regulations between central and regional authorities have resulted in governance conflicts, weak enforcement, and substantial environmental degradation. Utilizing a mixed-method approach informed by Kagan’s regulatory model, this research integrates field-based environmental assessments including bauxite sediment sampling and post-mining water quality analysis with a normative analysis of mining regulations and governance practices. The findings reveal a dominance of procedural legal frameworks over substantive environmental accountability. Regional autonomy laws tend to prioritize investor interests, often at the expense of community welfare and environmental restoration. Additionally, inadequate local oversight has allowed the continued export of unprocessed bauxite, exacerbating environmental harm. This study contributes new insights by exposing the structural misalignment between regulatory authority and environmental responsibility under Indonesia’s current autonomy regime. It underscores the urgent need for regulatory reform that clarifies lines of authority, mandates in-country bauxite processing prior to export, and enforces post-mining reclamation obligations at the regional level. These recommendations aim to support policymakers in designing enforceable and context-sensitive reforms for sustainable bauxite mining governance.
Original Research Paper
Exploitation
Marco Antonio Cotrina-Teatino; Jairo Jhonatan Marquina-Araujo; Solio Marino Arango-Retamozo; Luis Alex Rios-LLaure; Jose Nestor Mamani-Quispe; Salomon Ortiz-Quintanilla
Abstract
This work aimed to optimize fuel consumption and CO2 emissions in mining haul trucks through a sustainability focused machine learning approach in a gold mine in La Libertad, Peru. The methodology comprised three stages. First, operational data from 26 m3 haul trucks (10,103 records over 12 months) were ...
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This work aimed to optimize fuel consumption and CO2 emissions in mining haul trucks through a sustainability focused machine learning approach in a gold mine in La Libertad, Peru. The methodology comprised three stages. First, operational data from 26 m3 haul trucks (10,103 records over 12 months) were normalized using Z-score scaling. Second, a Ridge regression model was trained to predict fuel consumption based on variables such as truck utilization, trips, road gradient, material type, haul distance, and operating hours. Finally, three operational strategies were simulated: Controlled Reduction (CRS), Balanced Efficiency (BES), and Maximum Utilization (MUS), to evaluate environmental, economic, and social impacts. The results indicated that the Ridge model achieved strong predictive performance in estimating fuel consumption (R2 = 0.83; MSE = 38.16). According to the simulated scenarios, environmentally, CRS reduced fuel consumption by 30% and CO2 emissions by 1,481.3 tons; BES achieved 7.99% savings and 394.9 tons less CO2. Economically, CRS saved USD 664,924.6 in fuel costs and BES USD 177,276.3. Socially, the carbon cost decreased by USD 11,406.1 (CRS) and USD 3,041.0 (BES). MUS increased emissions by 864.3 tons and fuel costs by USD 387,966.4. This research proposes a novel integration of machine learning and sustainability analysis applied to haul trucks in open-pit mining material transport. It also offers a replicable, data-driven framework for mining companies to reduce emissions, optimize costs, and align their operations with sustainability goals.
Short Communication
Rock Mechanics
Sonu Saran; Prudhvi Raju Gadepaka; Ashok Jaiswal
Abstract
The stability of underground coal galleries is critically influenced by time-dependent deformation behavior of surrounding rock masses, particularly in deep mining environments where long-term stress redistribution can lead to delayed failure. In continuous miner-based mining systems, determining an ...
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The stability of underground coal galleries is critically influenced by time-dependent deformation behavior of surrounding rock masses, particularly in deep mining environments where long-term stress redistribution can lead to delayed failure. In continuous miner-based mining systems, determining an appropriate cut-out distance is essential to ensure productivity and safety, especially for weak rock mass. This study proposes a novel numerical–statistical framework for the optimal design of cut-out distance (COD) in room-and-pillar coal mining using continuous miners. A time-dependent viscoelastic-viscoplastic constitutive model was implemented in FLAC3D to simulate roof deformation across varying geo-mining conditions, including gallery widths (5 & 6 m), depths (100 to 400 m), and COD values (4 to 12 m). The Coal Roof Index (CRI), a composite geotechnical classification parameter, was incorporated to evaluate roof integrity. Results from the numerical simulations were used to develop two empirical models, COD₁ for depths ≤ 200 m and COD₂ for depths > 200 m, via multivariate nonlinear regression. The models demonstrated high predictive accuracy, with R² values of 0.95 and 0.90, respectively. The results reveal a strong correlation between the cut-out distance and various influencing parameters, i.e., width, depth, and CRI classification. Statistical validation through t-tests and ANOVA confirms the significance and reliability of the proposed model. Both proposed models have been validated by two field cases of the Indian coal mine. Critical CRI thresholds were quantified for safe CODs, offering actionable insights for field implementation. The proposed design approach provides a robust framework for improving the safety and sustainability of underground coal mine development, particularly under weak roof conditions.
Original Research Paper
Exploitation
TAIWO Blessing olamide; Adebayo Babatunde; Toluwase Daniel Olaiya
Abstract
This study developed and assessed several artificial intelligence (AI) models for predicting blast-induced toe volume in small-scale dolomite mines located in the Akoko Edo Local Government Area, Edo State, Nigeria. Seven predictive models were constructed: Adaptive Boosting (AdaBoost), Random Forest ...
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This study developed and assessed several artificial intelligence (AI) models for predicting blast-induced toe volume in small-scale dolomite mines located in the Akoko Edo Local Government Area, Edo State, Nigeria. Seven predictive models were constructed: Adaptive Boosting (AdaBoost), Random Forest (RF), Gaussian Process Regression (GPR), Support Vector Regression (SVR), a conventional Artificial Neural Network (ANN), and two Imperialist Competitive Algorithm-optimized ANNs (ICA-ANNs). The models were trained using eight input parameters including uniaxial compressive strength (UCS), spacing (S), burden (B), sub-drill (SB), drill hole length (DHL), stiffness ratio (SR), maximum instantaneous charge (MIC), and powder factor (K) with blast toe volume (TV) as the target output. Input data were collected through a combination of field measurements and laboratory analyses. Among all the models evaluated, the ICA-ANN with an 8-7-1 architecture achieved the highest predictive accuracy. It outperformed AdaBoost by 9.17%, SVR by 7.20%, GPR by 5.56%, RF by 4.75%, a standard ANN (8-5-1) by 0.78%, and a standard ANN (8-7-1) by 0.28%, based on mean squared error (MSE) and coefficient of determination (R²) metrics. Furthermore, the ICA-ANN model was applied to optimize blast design parameters. The optimal values obtained were: spacing = 1.0 m, burden = 0.8 m, sub-drill = 0.6 m, MIC = 0.72 kg, and powder factor = 0.65 kg/m³. These optimized parameters reduced the blast toe volume by 20.05%, from 209.50 m³ to 154.87 m³. The results highlight the robustness and efficiency of the ICA-ANN model for blast design optimization. By improving fragmentation quality and minimizing residual toe volume, the approach offers a practical pathway for enhancing both productivity and cost-effectiveness in small-scale mining operations.
Original Research Paper
Environment
Aditi Nag
Abstract
India's mining heritage sites (MHSs) represent underdeveloped tourist avenues for culture conservation and community upliftment. This study undertakes a dual-site comparison depending on a mixed-methods approach combining perception surveys of visitors, satellite image analysis, and statistical techniques ...
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India's mining heritage sites (MHSs) represent underdeveloped tourist avenues for culture conservation and community upliftment. This study undertakes a dual-site comparison depending on a mixed-methods approach combining perception surveys of visitors, satellite image analysis, and statistical techniques involving t-tests, chi-square analysis, and hierarchical clustering, for Dhori Mines (Jharkhand) and Barr Conglomerate (Rajasthan). Results starkly reveal contrasts: while Barr confirms ecological recovery and community integration, Dhori suffers due to infrastructure and interpretive constraints. Other strategies include AI-powered heritage interpretation and visitor segmentation to improve site competitiveness. It emerges from the findings that data-oriented landscape and tourism planning coupled with local participation can sustain and promote post-mining landscapes effectively.
Case Study
Environment
Saeed Omori; Arezoo Abedi; Kumars Seifpanahi-Shabani; Hamid Abbasdokht; Mohammad Ghafoori; Mohammad Abasian; Antony van der Ent
Abstract
This study evaluated the efficiency of the native hyperaccumulator Odontarrhena inflata in extracting nickel (Ni) from ultramafic soils in the Robat-Sefid region of northeastern Iran and assessed the feasibility of applying agromining under controlled conditions. A six-month greenhouse experiment was ...
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This study evaluated the efficiency of the native hyperaccumulator Odontarrhena inflata in extracting nickel (Ni) from ultramafic soils in the Robat-Sefid region of northeastern Iran and assessed the feasibility of applying agromining under controlled conditions. A six-month greenhouse experiment was conducted using homogenized serpentine soil with a total Ni concentration of 1,460 mg/kg. By the end of the cultivation period, the aerial parts of the plant yielded 122 g of dry biomass containing 2,195 mg/kg of Ni. The calculated bioconcentration factor (BCF = 1.5) and translocation factor (TF = 3.53) confirmed effective Ni uptake and translocation from roots to shoots. The biomass was pyrolyzed at 550 °C to produce ash, which underwent cross-washing and sulfuric acid (H₂SO₄) leaching. This leaching process achieved a Ni extraction efficiency of 78.9%, and the overall Ni recovery from soil to biomass ash was estimated at 3.53%. Elemental analyses showed substantial reduction of Magnesium (Mg) and Iron (Fe) in the final crystalline product; however, Calcium (Ca) and Sodium (Na) remained at appreciable levels, indicating that further recrystallization or purification steps are necessary to achieve industrial-grade ANSH (ammonium nickel sulfate hexahydrate). Compared with other Ni hyperaccumulators, O. inflata exhibited lower shoot Ni levels than Odontarrhena chalcidica and Alyssum murale, but the combination of its strong ecological adaptability, elevated TF, and native occurrence collectively designates it as a sustainable and promising candidate for agromining applications in nickel-rich soils of Iran.
Original Research Paper
Exploration
Hamid Geranian; Mohammad Amir Alimi
Abstract
This study employs Sentinel-2 satellite images along with the random forest algorithm to create a regional geological map. For this purpose, the independent variables consist of the images for 10 Sentinel-2 bands of the Khosuf-I region, while the class labels consist of a geological map of Khosuf-I divided ...
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This study employs Sentinel-2 satellite images along with the random forest algorithm to create a regional geological map. For this purpose, the independent variables consist of the images for 10 Sentinel-2 bands of the Khosuf-I region, while the class labels consist of a geological map of Khosuf-I divided into three and fifteen rock units. The classification accuracy of the resulting model is 90.97 and 84.85% for the three-class training and testing data, and 94.76 and 63.92% for the fifteen-class training and testing data, respectively. These models are then applied to the Sentinel-2 satellite images' data of the Birjand-IV region to prepare two preliminary geological maps. The Birjand-IV region's three-class geology map reveals that igneous rocks are present in the northern and southern regions, while sedimentary rocks occupy the middle section and metamorphic rocks are found within the region's igneous masses. Similarly, the fifteen-class geology map of Birjand-IV indicates that andesite, dacite, intermediate tuff rock units, and metamorphic rocks characterize the northern region. Conversely, the southern part of the region is mainly composed of ophiolite, flysch sediments, basaltic and ultra-basic volcanic rocks, and limestone and shale interlayers. Field studies in three areas confirm the accuracy of the preliminary geology maps.
Original Research Paper
Mine Economic and Management
Sarina Akbari; Reza Ghezelbash; Hamidreza Ramazi; Abbas Maghsoudi
Abstract
Natural hazards, particularly landslides, have long posed significant threats to people, buildings, and the surrounding environment. Therefore, comprehensive planning for urban and rural development necessitates the development and implementation of landslide risk zoning models. Numerous methodologies ...
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Natural hazards, particularly landslides, have long posed significant threats to people, buildings, and the surrounding environment. Therefore, comprehensive planning for urban and rural development necessitates the development and implementation of landslide risk zoning models. Numerous methodologies have been proposed for generating landslide hazard maps, which can potentially aid in predicting future landslide-prone areas. This study employed an integrated approach that combines statistical and multi-criteria decision-making (MCDM) methodologies. The Frequency Ratio (FR) and Analytical Hierarchy Process (AHP) were utilized as knowledge-driven approaches, while the Support Vector Machine (SVM) using an RBF kernel, a widely recognized machine learning algorithm, was applied as a data-driven method. Ten factors influencing landslides were considered, including slope angle, aspect, altitude, geology, land use, climate, erosion, and distances from rivers, faults, and roads. The results revealed that landslides are more predictable in the southern, southwestern, and central regions of the studied area. A quantitative assessment of the different methods using prediction-rate curves indicated that the SVM method outperformed the FR and AHP-FR approaches in identifying susceptible areas. The findings of this work could be effectively employed to mitigate potential future hazards and associated damages.
Original Research Paper
Exploitation
Ali Rezaei; Ebrahim Ghasemi; Ali Farhadian; Sina Ghavami
Abstract
In this study, a comprehensive investigation has been done on 10 different types of granite building stones from various mines in Iran. The study aims to investigate the relationship between the texture coefficient (TC) and abrasivity properties of the studied stones. Abrasivity of stones was quantified ...
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In this study, a comprehensive investigation has been done on 10 different types of granite building stones from various mines in Iran. The study aims to investigate the relationship between the texture coefficient (TC) and abrasivity properties of the studied stones. Abrasivity of stones was quantified through six indices, including equivalent quartz content (EQC), rock abrasivity index (RAI), Schimazek abrasivity factor (F), Cerchar abrasivity index (CAI), building stone abrasivity index (BSAI), and the Taber wear index (Iw). Bi-variate regression analysis was applied to develop the predictive equations for relationship between TC and abrasivity indices. The investigations demonstrated that there is a direct relationship between TC and all abrasivity indices. Furthermore, TC has moderate to high relationship with abrasivity indices. After developing the equations, their accuracy was evaluated by performance criteria including determination coefficient (R2), the normalized root mean square error (NRMSE), the variance account for (VAF), and the performance index (PI). The strongest relationship was found between TC and RAI (with R2, VAF, NRMSE, and PI value of 0.850, 0.074, 85.386, and 1.630, respectively), while the weakest relationship was observed between TC and F (with R2, NRMSE, VAF, and PI value of 0.491, 0.532, 47.605, and 0.435, respectively). This research demonstrates importance of the textural characteristics of stones, especially TC as a reliable index, on the abrasivity properties of granite building stones. Thus, the equations developed herein can be practically used for estimating the stone abrasivity in building stone quarrying and processing projects.
Original Research Paper
Rock Mechanics
Aram Ardalanzdeh; Seyed Davoud Mohammadi; Vahab Sarfarazi; Hossein Shahbazi
Abstract
Creating holes in rocks using different methods presents various challenges. In this research, an attempt was made to investigate these characteristics and the existing problems in creating holes based on the texture and brittleness of the rock. For this purpose, several core specimens were taken from ...
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Creating holes in rocks using different methods presents various challenges. In this research, an attempt was made to investigate these characteristics and the existing problems in creating holes based on the texture and brittleness of the rock. For this purpose, several core specimens were taken from the Alvand granitic batholith of Hamadan, and the petrological and textural indexes of the rocks were determined. There are four types of rock textures, ranging from coarse-grained to fine-grained. The texture coefficients (TC) for the four types of rocks (G1 to G4) were 1.709, 1.730, 1.774, and 1.697, respectively. The brittleness index (B1) for the four types of rocks (G1 to G4) were 9.13, 11.01, 12.07, and 10.65, respectively. After that, using a diamond drill, one hole was created in each rock core specimen, and at the end of drilling, a fracture pit was separated from the bottom of each hole in the specimen. The results show that as the mineral size decreases, the fracture pit depth also decreases, and in porphyry texture, the fracture pit depth is between the fracture pit depths of coarse-grained and medium-grained rocks. As the texture coefficient (TC) and brittleness of the rock specimens increase, the fracture pit depth decreases, and in porphyry texture, the fracture pit depth remains between the fracture pit depths of coarse-grained and medium-grained rocks. Finally, the results from laboratory tests indicate that creating holes using a drill to study the effect of the holes on rock behavior can cause damage to the rocks.
Original Research Paper
Exploitation
Masoud Monjezi; Morteza Baghestani; Peyman Afzal; Ali Reza Yarahmadi Bafghi; Seyyed Ali Hashemi
Abstract
Blasting is an essential operation in mining projects, significantly affecting the particle-size distribution, which is critical for subsequent processes such as loading, hauling, and milling. Effectiveness of the blasting operations rely on accurate rock characterization, especially when dealing with ...
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Blasting is an essential operation in mining projects, significantly affecting the particle-size distribution, which is critical for subsequent processes such as loading, hauling, and milling. Effectiveness of the blasting operations rely on accurate rock characterization, especially when dealing with different rock types. Proper rock and fragmentation characterization allows for tailored blast designs and also can lead to precise predictions of fragmentation quality. Various characterization techniques exist. This paper examines the application of fractal analysis to classify fragmentation quality and rock types, utilizing the Choghart iron mine in Iran as a case study. Extensive fieldwork collected data on rock properties (uniaxial compressive strength and density) and fragmentation outcomes during blasting. The fractal modeling revealed distinct breakpoints for classification, followed by Logratio analysis to assess relationships among the identified classes. Finally, mathematical models were established to predict fragmentation features based on the relevant rock attributes. The models demonstrated improved predictive accuracy as compared to the prior classifications.
Original Research Paper
Exploration
Ahmadreza Erfan; Saeed Soltani Mohammad; Maliheh Abbaszadeh
Abstract
Machine learning (ML) has significantly transformed multiple disciplines, including mineral resource evaluation in mining engineering, by facilitating more accurate and efficient estimation methods. Ensemble methods, as a fundamental component of modern machine learning, have emerged as powerful ...
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Machine learning (ML) has significantly transformed multiple disciplines, including mineral resource evaluation in mining engineering, by facilitating more accurate and efficient estimation methods. Ensemble methods, as a fundamental component of modern machine learning, have emerged as powerful tools that robust techniques that integrate multiple predictive models to improve performance beyond that of any individual learner. This study proposes a novel ensemble method for estimating ore grades by localizing the base learner weights in ensemble method. Ordinary kriging, inverse distance weighting, k-nearest neighbors, support vector regression, and artificial neural networks have been used as the base learners of the algorithm. In ML base learners, coordinates (easting, northing and elevation) of samples have been defined as input nodes and grade has been defined as target. The proposed method has been validated for predicting the copper grade (Cu%) in Darehzar porphyry deposit. The performance of proposed method has been by individual base learners and famous ensemble methods. This comparison shows that performance of proposed method is better than other ones. The findings highlight the necessity of adapting ensemble methods to address spatial variability in geological data, thereby establishing a robust framework for ore grade estimation.
Original Research Paper
Exploitation
Hasan Ghasemzadeh; Hassan Madani; Farhang Sereshki; Sajjad Afraei
Abstract
One of the most prevalent risks in coal mines is spontaneous combustion (spon com) of coal, which is a major source of coal loss in these environments. Therefore, to avoid coal loss and preventing the potential risks, a criterion for predicting the spon com of coal is essential. The main purpose of this ...
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One of the most prevalent risks in coal mines is spontaneous combustion (spon com) of coal, which is a major source of coal loss in these environments. Therefore, to avoid coal loss and preventing the potential risks, a criterion for predicting the spon com of coal is essential. The main purpose of this work is to present a new model for predicting the spon com of coal potential using a decision tree technique, known as the Spon com of coal decision Tree (SCCDT). In this research work, after identifying the effectiveness of each parameter on the spon com of coal, several parameters were examined, including characteristics such as moisture, ash, pyrite, volatile matter, fixed carbon, mineralogy, and petrography. Subsequently, the primary phases of applying the decision tree model were analyzed, and the probability of the spon com of coal potential was determined based on intrinsic parameters. Finally, the mentioned parameters were categorized, and an appropriate model for classifying the spon com of coal potential was developed. In the SCCDT model, the spon com of coal potential was divided into three classes: low, medium, and high. The model was then applied to Parvadeh I to IV coal mines in Tabas. A comparison of the study's findings showed relatively good agreement with the SCCDT model. Using the proposed model can help to predict the spon com hazard and prevent the various life-threatening/mortal and financial risks.
Original Research Paper
Exploration
Mojtaba Bazargani Golshan; Mehran Arian; Peyman Afzal; Lili Daneshvar Saein; Mohsen Aleali
Abstract
The purpose of this research is application of the Concentration-Number and Concentration-Area fractal models for determining the distribution pattern of REEs and lithium in mining area of the North Kochakali coal deposit. According to the Concentration-Area and Concentration-Number fractal graphs, four ...
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The purpose of this research is application of the Concentration-Number and Concentration-Area fractal models for determining the distribution pattern of REEs and lithium in mining area of the North Kochakali coal deposit. According to the Concentration-Area and Concentration-Number fractal graphs, four different geochemical groups were obtained for REEs and lithium in the mining area of North Kochakali coal deposit. The comparison of the threshold values and the models obtained based on the Concentration-Area and Concentration-Number fractal models indicate that the Concentration-Area Fractal model has performed better in determining different geochemical groups and separating anomalies from the background for REEs and lithium in North Kochakali coal deposit. Based on the fractal models in the mining area, the southeastern and western parts have the highest concentrations of REEs and the northeastern parts have the highest concentrations of lithium. These parts should be considered in mining operations due to their higher economic value. The locations of the REEs anomalies are consistent with the location of right-lateral faults with a normal component, since these faults are young and have operated after the formation of coal seams, so the mineralization of REEs in North Kochakali coal deposit is epigenetic.
Original Research Paper
Rock Mechanics
Mohammad Amin HajiMohammadi; Mojtaba Bahaaddini; Mohammad Hossein Khosravi; Hassan Vandyoosefi
Abstract
Discontinuities are known as a primary factor in instability of tunnels and underground excavations. To prevent potential damage and overbreak by underground advancement, it is essential to provide a model, which considers both the geometrical and mechanical characteristics of discontinuities. Discrete ...
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Discontinuities are known as a primary factor in instability of tunnels and underground excavations. To prevent potential damage and overbreak by underground advancement, it is essential to provide a model, which considers both the geometrical and mechanical characteristics of discontinuities. Discrete Fracture Network (DFN) is a conceptual model to represent and analyse the complex system of discontinuities within the rock mass. Combined DFN with analytical or numerical methods can be employed as a scientific tool to analyse generated rock blocks, and their stabilities under different loading conditions. This paper aims to investigate the created overbreak by tunnel advancement in the Alborz tunnel located in the Tehran-North freeway in Iran. First, the geometrical characteristics of discontinuities were surveyed by tunnel advancement in 200 meters. Four major joint sets and a bedding plane were identified and their statistical characteristics were measured. The DFN model was generated and its validity was investigated through a comparison against field data. The average volume of generated blocks in the studied area was measured 0.22 m3. The stability of generated blocks around the opening was kinematically evaluated. The volume of formed blocks around tunnel in the DFN model prone to instability due to static or dynamic loads was estimated 2605 m3 while the measured overbreak in field was 2735 m3. The depth of overbreak in DFN model showed a good agreement with field measurements. The results show that DFN model combined with kinematic stability analysis can provide a scientific tool to investigate geological overbreak in underground excavations.
Original Research Paper
Exploration
Marco Antonio Cotrina-Teatino; Jairo Jhonatan Marquina-Araujo; Jose Nestor Mamani-Quispe; Solio Marino Arango-Retamozo; Joe Alexis Gonzalez-Vasquez
Abstract
Traditional geostatistical methods such as kriging exhibit limitations by assuming linear and symmetric dependencies, which can lead to smoothed estimates and the loss of local variability. To address these issues, this study applies Archimedean copulas (Clayton, Gumbel, and Frank) for the estimation ...
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Traditional geostatistical methods such as kriging exhibit limitations by assuming linear and symmetric dependencies, which can lead to smoothed estimates and the loss of local variability. To address these issues, this study applies Archimedean copulas (Clayton, Gumbel, and Frank) for the estimation of copper ore grades in a deposit located in Peru. A total of 5,654 composites, each 15 meters in length, were obtained from 185 diamond drill holes. The data were transformed to a uniform scale to allow for copula fitting. Dependence structures were modeled by lag distance, with the dependence parameter fitted using fifth-degree polynomials, and three-dimensional conditional estimation was implemented. Results indicate that ordinary kriging yielded RMSE = 0.161, MAE = 0.104, R2 = 0.692, and a correlation of 0.861. The Clayton copula slightly improved these metrics (RMSE = 0.154, MAE = 0.101, R2 = 0.717, R = 0.871), while the Gumbel copula captured higher local variability (RMSE = 0.161, MAE = 0.116, R2 = 0.692, R = 0.855). The Frank copula achieved the best performance with RMSE = 0.137, MAE = 0.090, R2 = 0.778, and R = 0.905. In conclusion, Archimedean copulas significantly enhance geostatistical estimation by better capturing spatial dependence, offering a robust alternative to classical geostatistical methods.
Original Research Paper
Exploration
Ahmed Mahmoud Abdelhameed; Maher Abdelateef El Amawy; Ayman Mahrous; Mohamed El-Khouly; Adel Fathy
Abstract
Hyperspectral imaging (HSI), combined with advanced machine learning algorithms (MLAs), has unlocked novel research opportunities and revolutionized geological mapping by enabling precise lithological classification. Accurately detailed geological mapping is one of the most essential requirements for ...
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Hyperspectral imaging (HSI), combined with advanced machine learning algorithms (MLAs), has unlocked novel research opportunities and revolutionized geological mapping by enabling precise lithological classification. Accurately detailed geological mapping is one of the most essential requirements for targeting mineralization. However, achieving comprehensive lithological mapping remains a challenge, hindering systematic mineral exploration. This work explores the use of PRISMA hyperspectral data and the Support Vector Machine (SVM) and Artificial Neural Network (ANN) algorithms to objectively map the Precambrian rock assemblages at the El Ineigi area in the Central Eastern Desert (CED) of Egypt. For this purpose, PRISMA data in HDF5 format were first pre-processed and subsequently transformed through principal component analysis (PCA). The processed spectral data were then combined with extensive fieldwork and previously existing geological maps and classified using SVM and ANN to achieve enhanced discrimination of the exposed rock units in the study area. Our results conclusively demonstrate the exceptional capability of PRISMA data for detailed lithological mapping. The SVM and ANN classification achieved remarkably high overall accuracy, successfully generating a robust geological map that clearly discriminates between various Neoproterozoic basement rock units in the El Ineigi area. Through the integration of diagnostic spectral signatures with field verification, we confidently identified all major mappable units, including metavolcanics, metagabbro-diorite complexes, younger granites, and Wadi deposits. The proposed integrated approach demonstrates superior performance compared to traditional mapping techniques, offering enhanced discrimination precision and operational efficiency. These findings strongly support the combined use of PRISMA hyperspectral data and MLAs for lithological mapping applications.
Review Paper
Exploration
Ukpata Austin Odo; Jude S Ejepu; Bernd Striewski
Abstract
The mining sector must address the growing challenges of resource management, safety issues, and environmental impact concerns. All stages of the mining life cycle need essential geospatial technologies to address the mentioned challenges. This article examines how Geographic information systems (GIS), ...
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The mining sector must address the growing challenges of resource management, safety issues, and environmental impact concerns. All stages of the mining life cycle need essential geospatial technologies to address the mentioned challenges. This article examines how Geographic information systems (GIS), remote sensing (RS), LiDAR, drone mapping, and positioning systems find applications in mineral exploration, mine planning, operational monitoring, and post-mining rehabilitation. Artificial intelligence (AI) and machine learning (ML) systems enhance the functional potential of these technologies through predictive modeling capabilities, which work in conjunction with real-time analytic functions. The research shows that these technologies enable better decision-making, performance optimisation, and environmental risk reduction. Modern mining relies entirely on these technologies because they support accurate resource assessment, optimise design operations, and help enforce safety standards and environmental codes. Adopting such technologies requires resolving implementation costs, addressing data integration issues, and acquiring the necessary technical expertise. The future development of mining technology should focus on enhancing the integration of geospatial information platforms, creating sustainable solutions for medium-sized mining operations at affordable prices, and developing predictive evaluation systems utilizing AI algorithms. The mining industry accomplishes safer operation methods through efficient technologies, enhancing sustainability.
Original Research Paper
Exploration
Joshua Chisambi; Leornard Kalindekafe; Kettie Magwaza; Ruth Mumba; Martin Kameza
Abstract
The Nathenje region in central Malawi hosts significant gold mineralization within high-grade metamorphic rocks of the Mozambique Belt, yet remains underexplored despite extensive artisanal mining activity. The structural controls on primary bedrock gold mineralization within these high-grade metamorphic ...
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The Nathenje region in central Malawi hosts significant gold mineralization within high-grade metamorphic rocks of the Mozambique Belt, yet remains underexplored despite extensive artisanal mining activity. The structural controls on primary bedrock gold mineralization within these high-grade metamorphic rocks remain poorly understood, limiting systematic exploration and resource development. We conducted integrated field mapping, structural analysis, petrographic examination, and geochemical sampling to characterize gold mineralization controls in the Nathenje prospect, central Malawi. Detailed structural measurements combined with stereographic analysis reveal three deformation phases, with gold mineralization predominantly associated with D₂ transpressional structures. Fire assay results demonstrate significant gold concentrations (0.15–5.0 g/t Au) in arsenopyrite-bearing quartz veins, with the highest grades systematically occurring at structural complexity zones. Petrographic analysis reveals native gold particles (5–50 μm) intimately associated with arsenopyrite along grain boundaries and within microfractures, indicating coupled precipitation processes. Critically, we identify a hierarchical structural control system operating from regional NE-SW trending shear zones to microscale sulphide boundaries, with fold hinges, dilutional jogs, and amphibolite-gneiss contacts yielding consistently higher gold grades (>3 g/t Au) than other structural settings. Our results establish the first comprehensive structural model for gold mineralization in central Malawi's metamorphic terrain and provide specific targeting criteria applicable to similar high-grade metamorphic environments throughout the East African Orogen.
Original Research Paper
Environment
Clement Kweku Arthur; Yao Yevenyo Ziggah; Victor Amoako Temeng
Abstract
Blast-induced noise is one of the most persistent environmental challenges in surface mining, posing significant health risks to workers and nearby communities. Accurate prediction of noise levels prior to blasting is essential for mitigating its adverse impacts. This study proposes an explainable ensemble ...
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Blast-induced noise is one of the most persistent environmental challenges in surface mining, posing significant health risks to workers and nearby communities. Accurate prediction of noise levels prior to blasting is essential for mitigating its adverse impacts. This study proposes an explainable ensemble machine learning framework for predicting blast-induced noise using data from an open-pit gold mine in Ghana. Four ensemble models namely: Extreme Gradient Boosting (XGBoost), Gradient Boosting, Adaptive Boosting (AdaBoost), and Categorical Boosting (CatBoost), were developed and evaluated using a comprehensive dataset of 324 blasting events. Performances of the developed models were assessed using coefficient of determination (R²), root mean squared error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE) and coefficient of the variation of the root mean squared error (CVRMSE), with XGBoost emerging as the best-performing model (R² = 1.0000, RMSE = 0.0005, MAE = 0.0004, MAPE = 0.0010, CVRMSE = 0.0013). To address the black-box nature of ensemble method, Shapley Additive exPlanations (SHAP) was employed, offering both global and local interpretability. SHAP analysis identified the distance from the blast site to the monitoring point as the most influential factor. This integrative approach not only enhances predictive accuracy but also improves model transparency, supporting sustainable mining practices aligned with United Nations Sustainable Development Goals (SDGs) 3 and 15.
Original Research Paper
Environment
Ritu Bala Garg; Gurpreet Singh
Abstract
This study presents a comprehensive investigation into the synergistic use of fly ash (FA), coal bottom ash (CBA), and quarry dust (QD) as partial replacements for conventional construction materials, aiming to mitigate environmental degradation while enhancing material performance. Individually and ...
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This study presents a comprehensive investigation into the synergistic use of fly ash (FA), coal bottom ash (CBA), and quarry dust (QD) as partial replacements for conventional construction materials, aiming to mitigate environmental degradation while enhancing material performance. Individually and in combination, a series of concrete mixes were prepared incorporating these wastes at varying proportions, and were tested for workability, compressive strength, and durability (water absorption and chloride ion penetration). Results indicate that blends of FA, CBA, and QD can effectively substitute up to 40% of cement and fine aggregates without compromising structural performance. The mixes containing 20% fly ash, 10% bottom ash, and 10% quarry dust exhibited superior compressive, split tensile, and flexural strength, and reduced water absorption and chloride ion penetration, demonstrating their potential in aggressive environments.
Original Research Paper
Rock Mechanics
Swaraj Chowdhury; Rakesh Kumar; Ankit Kumar
Abstract
The present study examines the strength and permeability behavior of glass fibre-reinforced fly ash-bentonite (FaB) mixture to assess its potential as an alternate geo-material. The FaB mixture is produced by adding 20% bentonite with 80% fly ash and is further reinforced with glass fibre. The unconfined ...
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The present study examines the strength and permeability behavior of glass fibre-reinforced fly ash-bentonite (FaB) mixture to assess its potential as an alternate geo-material. The FaB mixture is produced by adding 20% bentonite with 80% fly ash and is further reinforced with glass fibre. The unconfined compressive strength (UCS) tests have been conducted at a strain rate of 0.625 mm/min by varying the curing period (0 to 60 days), relative moisture content (R.M.C– 80% to 120%) and fibre content (0% to 1.0%). The effect of fibre content on the coefficient of permeability (k) and compressibility behavior of the FaB mixture has been investigated through one-dimensional consolidation tests. The findings indicate that the UCS of the FaB mix samples improves with an increase in curing period and fibre content. At 100% R.M.C, the UCS increases from 48 kPa to 228 kPa for the unreinforced samples as the curing period increases from 0 to 60 days. At 90% R.M.C, both unreinforced and reinforced FaB mix samples have exhibited the highest UCS values considering all curing periods. With fibre content increasing from 0% to 1.0%, the UCS rises about 33% to 44% at 100% R.M.C. Fibre reinforcement also contributes to reduction of k and compressibility. Based on the experimental findings, a closed-form equation has been developed for the prediction of UCS of FaB mixture reinforced with and without glass fibre. Results confirm that glass fibre reinforcement improves the strength, permeability, and compressibility of the FaB mixture, establishing it as an alternate geo-material.
Original Research Paper
Exploration
Shirin Jahanmirir; Ali Aalianvari; Hossein Ebrahimpour-Komleh
Abstract
This paper introduces the Human Mental Search (HMS) algorithm as a novel and superior optimization technique for predicting groundwater seepage in tunnel environments. Traditional methods for predicting such seepage often struggle with the complexities of subterranean water flow, particularly in heterogeneous ...
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This paper introduces the Human Mental Search (HMS) algorithm as a novel and superior optimization technique for predicting groundwater seepage in tunnel environments. Traditional methods for predicting such seepage often struggle with the complexities of subterranean water flow, particularly in heterogeneous geological conditions, and while machine learning approaches have offered improvements, they often require significant computational resources. The HMS algorithm, inspired by human cognitive processes, employs memory recall, adaptive clustering, and strategic selection to efficiently refine solutions. Our results demonstrate that HMS significantly outperforms established algorithms in predicting groundwater seepage, achieving an R² value of 0.9988, a Mean Squared Error (MSE) of 0.0002, and a Root Mean Squared Error (RMSE) of 0.0137. In comparison, the Whale Optimization Algorithm (WOA) achieved an R² of 0.9951 with much higher MSE and RMSE, and other methods, like Genetic Programming and ANN-PSO, show higher error values. The HMS algorithm also showed a Variance Accounted for (VAF) of 99.88% and a Mean Absolute Error (MAE) of 0.0041, further validating its high predictive accuracy. This study highlights the HMS algorithm’s superior performance and computational efficiency for optimizing groundwater seepage predictions, positioning it as a powerful tool for geotechnical engineering applications, including real-time monitoring.
Case Study
Exploitation
Mojtaba Dehghani Javazm; Mohammadreza Shayestehfar
Abstract
In this work, various methods for evaluating recoverable reserves including estimation techniques and conditional simulation have been compared in the Miduk copper deposit using data from 55,119 blast holes and 6,178 composite samples from exploratory drillings in the supergene and hypogene zones, with ...
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In this work, various methods for evaluating recoverable reserves including estimation techniques and conditional simulation have been compared in the Miduk copper deposit using data from 55,119 blast holes and 6,178 composite samples from exploratory drillings in the supergene and hypogene zones, with a block model constructed for the analysis. Four methods were employed: UC, LUC, DCSBG, and SGS. The correlation coefficients for UC, DCSBG, and SGS methods in the supergene zone, as well as the results from extraction drill holes (extraction blocks) at a cut-off grade of 0.15%, were 0.637, 0.527, and 0.556, and the correlation coefficient for calculating tonnage and the metal content using UC was 0.364 and 0.629, respectively. For the hypogene zone, the correlation coefficients for metal content at a cut-off grade of 0.15% were 0.778, 0.788, and 0.790 for UC, DCSBG, and SGS, and at a cut-off grade of 0.65%, they were 0.328, 0.431, and 0.458, respectively. By employing The LUC method in the supergene zone with a change in SMU and comparing the results obtained from the E-Type map, the performance of this method is higher across all cut-off grades. As the cut-off grade increases in the hypogene zone, the performance of the LUC method relative to simulation methods decreases. The LUC method can be used to observe the impact of the convergence of results obtained from this method with real data from low-grade to high-grade sections, highlighting the necessity of differentiating this zone into low and high-grade segments during the estimation process.
Original Research Paper
Exploitation
Moein Bahadori; Moahammad Amiri Hosseini; Iman Atighi
Abstract
As open-pit mining advances, the decreasing separation between blast blocks and surface structures necessitates rigorous control of induced ground vibrations to mitigate structural risks. This study performed 13 single-hole blasting operations at the Golgohar Sirjan Iron Mine processing plant to evaluate ...
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As open-pit mining advances, the decreasing separation between blast blocks and surface structures necessitates rigorous control of induced ground vibrations to mitigate structural risks. This study performed 13 single-hole blasting operations at the Golgohar Sirjan Iron Mine processing plant to evaluate vibration control strategies for protecting the onsite processing plant. A Blastmate III seismograph was employed to record 54 three-component data sets, including waveform data, maximum amplitude, and dominant frequencies. By superimposing waves, optimal delay times (ODT) for the blast holes were determined and the corresponding effects on wave frequencies were analyzed. An experimental blasting pattern was designed based on the derived ODT values, and the impact on ground vibration was examined. The results indicated a 10% reduction in vibration levels with the proposed delay times. Furthermore, considering the minimum distance of 111 meters from the processing plant to the final pit and adhering to the DIN safety standard, it is recommended that blast holes with a maximum diameter of 165mm be used to ensure a safety factor of 15%. For distances exceeding 187 meters, blast holes with a 250mm diameter are recommended to maintain production efficiency and a safety factor of 50%.
Original Research Paper
Environment
farhad samimi namin; Zahra S Tarasi; Keyvan Habibi kilak
Abstract
Environmental issues related to mine wastes have highlighted the importance of waste recycling. A study was conducted on sand mines in Kurdistan province, Iran, focusing on the construction of artificial stones from effluent to minimize environmental impact. The research included environmental, physical-mechanical, ...
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Environmental issues related to mine wastes have highlighted the importance of waste recycling. A study was conducted on sand mines in Kurdistan province, Iran, focusing on the construction of artificial stones from effluent to minimize environmental impact. The research included environmental, physical-mechanical, and economic analyses, using the Analytic Hierarchy Process (AHP) for environmental assessments. Tests on density, water absorption, and strength showed that stones containing effluents were superior to other products. Increasing effluent percentages did not significantly affect density but improved water absorption and strength. Artificial stones containing 40% effluent demonstrated the greatest resistance and the least water absorption. This formulation achieves compressive strengths of 36.07 MPa, flexural strengths of 15.09 MPa, and tensile strengths of 1.89 MPa. Furthermore, it possesses a dry density of 2.33 gr/cm³, and a water absorption rate of 3.82%. Additionally, stones with effluent demonstrated better resistance to corrosion acid. The research methodology employed in the environmental analysis involved the application of the Analytic Hierarchy Process (AHP). Findings from environmental studies indicated that the volume of waste emerged as the most significant criterion with 27.3% weight when evaluating the selection of construction products that are environmentally compatible. Furthermore, research in environmental studies indicates that artificial stone is at least 10% more preferred than natural stone, 48% more preferred than tile, and 63% more preferred than brick. The analysis within the economic section demonstrated that the production of artificial stone incorporating waste, which achieved an internal rate of return of 138%, was more cost-effective than comparable products.
Original Research Paper
Exploration
Shaghayegh Esmaeilzadeh; Ali Moradzadeh; omid Asghari; Reza Mohebian
Abstract
Seismic inversion is a critical technique for estimating the spatial distribution of petro-elastic properties in the subsurface, based on the seismic reflection data. This work introduces an iterative geostatistical seismic inversion method, designed to address challenges in complex geological settings ...
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Seismic inversion is a critical technique for estimating the spatial distribution of petro-elastic properties in the subsurface, based on the seismic reflection data. This work introduces an iterative geostatistical seismic inversion method, designed to address challenges in complex geological settings by incorporating self-updating local variogram models. Unlike the conventional approaches that rely on a single global variogram or fixed local variograms, the proposed method dynamically updates the spatial continuity models at each iteration using automatic variogram modeling and clustering of variogram parameters. The optimal number of clusters is determined using three cluster validity indices: Silhouette Index (SI), Davies-Bouldin Index (DB), and Calinski-Harabasz Index (CH). The method’s effectiveness was evaluated using a three-dimensional non-stationary synthetic dataset, demonstrating robust convergence when employing the SI and CH indices, with both achieving a high global correlation coefficient of 0.9 between the predicted and true seismic data. Among these, the CH index provided the best balance between the computational efficiency and inversion accuracy. The results highlight the method’s ability to effectively capture local spatial variability, while maintaining a reasonable computational cost, making it a promising approach for seismic inversion in complex sub-surface environments.
Original Research Paper
Exploitation
Alireza Afradi; Arash Ebrahimabadi
Abstract
Rock-fragmentation is generally regarded as a crucial indicator within the mining industry for evaluating the effects of blasting operations. In this work, a database was primarily constructed using field data to predict rock fragmentation in the mines of Anguran and Sarcheshmeh. The datasets comprised ...
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Rock-fragmentation is generally regarded as a crucial indicator within the mining industry for evaluating the effects of blasting operations. In this work, a database was primarily constructed using field data to predict rock fragmentation in the mines of Anguran and Sarcheshmeh. The datasets comprised the input parameters such as Burden (m), spacing (m), powder factor (kg/m³), and stemming (m), with fragmentation (cm) as the output parameter. The analysis of these datasets was conducted using the Ant Lion Optimizer (ALO) and Crow Search Algorithm (CSA) methodologies. To assess the predictive models' accuracy, metrics including the coefficient of determination (R²), Variance Accounted For (VAF), and Root Mean Square Error (RMSE) were employed. The application of ALO and CSA to the database yielded results indicating that for ALO, R² = 0.99, RMSE = 0.005, and VAF (%) = 99.38, while for CSA, R² = 0.98, RMSE = 0.02, and VAF (%) = 98.11. Ultimately, the findings suggest that the predictive models yield satisfactory results, with ALO demonstrating a greater level of precision.
Original Research Paper
Exploration
Seyedeh golaleh Hosseini; Kourosh shahriar; mohammadamin karbala
Abstract
Mine drainage remains a critical challenge in ensuring the safety and sustainability of mining operations, as it is often complicated by complex subsurface flow behaviors and mechanical stress interactions. This study proposes an integrated three-phase framework for analyzing and optimizing drainage ...
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Mine drainage remains a critical challenge in ensuring the safety and sustainability of mining operations, as it is often complicated by complex subsurface flow behaviors and mechanical stress interactions. This study proposes an integrated three-phase framework for analyzing and optimizing drainage systems at the Angouran lead–zinc mine. In the first phase, the hydro-mechanical behavior of the rock mass was simulated using UDEC software, demonstrating that increased normal stress reduces fracture aperture and permeability. The simulated pore pressure (4.5×10⁵ Pa) closely matched the field measurements (4.4×10⁵ Pa), with only a 2.2% deviation. In the second phase, a multi-criteria decision-making approach using the Analytic Hierarchy Process (AHP) and input from 32 domain experts identified Q4 (very high quality) and Q2 (medium quality) indicators as the most influential criteria. In the third phase, three machine learning models—linear regression, polynomial regression, and artificial neural networks (ANNs)—were trained on piezometric data to predict water discharge. The ANN model outperformed the other models, achieving an R² of 0.94 and RMSE of 0.18, effectively capturing the nonlinear dynamics of groundwater flow within the mine. The findings highlight that the integration of numerical modeling, expert-based decision analysis, and AI-driven prediction provides a robust and innovative approach for designing and managing mine dewatering systems, with potential applicability to other complex hydrogeological environments.
Original Research Paper
Environment
Feridon Ghadimi; Abolfazl Shafaei; Abdolmotaleb Hajati
Abstract
This work investigates the extraction of sodium sulfate (Na2SO4) from Mighan Playa in Arak, Iran, where 163 boreholes were drilled to depths of up to 20 m revealed a heterogeneous lithology dominated by Glauberite (Na2Ca(SO4)2) and Mirabilite (Na2SO4·10H2O) with average sodium sulfate concentrations ...
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This work investigates the extraction of sodium sulfate (Na2SO4) from Mighan Playa in Arak, Iran, where 163 boreholes were drilled to depths of up to 20 m revealed a heterogeneous lithology dominated by Glauberite (Na2Ca(SO4)2) and Mirabilite (Na2SO4·10H2O) with average sodium sulfate concentrations of 25% (ranging from 2–32% and peaking at 55% in localized southwestern areas). The playa’s surface is primarily clay-covered (94%) and interbedded with evaporitic facies including Gypsum, Halite, and carbonate minerals. Seasonal water inflows of 200–800 l/s from a wastewater treatment plant, together with 3.5 m-deep extraction pits and gravitational drainage, have resulted in stagnant ponds over 25% of the southern lake area and an annual reduction in surface area of 5–10%. Stratigraphic analysis further indicates pure Glauberite layers (0.5–1 m thick) at depths of 1,653–1,656 m, in contrast with thicker impure Glauberite-Mirabilite sequences (up to 9 m) present between 1,649–1,659 m. To mitigate these challenges, an integrated engineering approach is proposed that includes pumping seepage brine (with a moisture content of 40%) to solar evaporation pools, employing continuous dual-pump slurry systems for tailings management, and implementing hydraulic balancing through retaining walls and winter brine reserves—measures that enhance extraction efficiency by 30–42% in high-concentration zones. These adaptive mining practices, incorporating in-situ brine leaching and advanced wastewater treatment, are designed to meet 70% of Iran’s annual sodium sulfate demand from an 8 km² operational area while reducing environmental degradation.
Original Research Paper
Environment
Masoud Monjezi; Safa Moezinia; Jafar Khademi Hamidi; Mojtaba Rezakhah; Vahid Amini; Amir Batarbiat
Abstract
Open-pit mine rehabilitation is essential for managing environmental impacts and achieving sustainable development after mining operations cease. The goal of this study is to find the best way to fix up the Zarshuran Gold Mine by ranking eight different ways to fix it up using the Fuzzy Analytic Hierarchy ...
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Open-pit mine rehabilitation is essential for managing environmental impacts and achieving sustainable development after mining operations cease. The goal of this study is to find the best way to fix up the Zarshuran Gold Mine by ranking eight different ways to fix it up using the Fuzzy Analytic Hierarchy Process (FAHP). These options are restoring the mine to its original state, planting trees, building a wind farm, creating a recreational area, setting up pastures, farming, building a solar power plant, and creating a tourist attraction. A panel of twelve experts evaluated these alternatives according to ten key criteria: air temperature intensity, number of sunny days, soil conditions, distance from residential areas, topographic irregularity, vegetation density, average wind speed, local animal species, site access, and the size and shape of the mined area. The results indicate that the construction of a solar power plant is identified as the most suitable rehabilitation option for the Zarshuran Gold Mine, considering the region’s climatic conditions (particularly the high number of sunny days per year) and its potential for clean energy generation and revenue creation. This study emphasizes the importance of considering environmental, social, and technical criteria in the decision-making process for mine rehabilitation and provides a framework for selecting sustainable rehabilitation methods in similar mining contexts.
Original Research Paper
Rock Mechanics
Amirreza Kavandi; Ramin Doostmohammadi
Abstract
So far, limited research has been conducted on the swelling behavior of Marlstone in the presence of cations. In this study, swelling pressure experiments were performed on rock samples obtained from the Marash Dam, located in northwest Iran. The specimens underwent wetting and drying cycles to achieve ...
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So far, limited research has been conducted on the swelling behavior of Marlstone in the presence of cations. In this study, swelling pressure experiments were performed on rock samples obtained from the Marash Dam, located in northwest Iran. The specimens underwent wetting and drying cycles to achieve an equilibrium condition before cation infiltration. Rock specimens were infiltrated with distilled water and with 1, 2, and 3 mol/L solutions of sodium chloride (NaCl) and calcium chloride (CaCl2). The findings suggest that as the concentration of the solutions rises, the swelling pressure of Marlstone diminishes. Furthermore, at the same concentrations, the swelling pressure of samples soaked in CaCl2 solutions was less than that of those treated with NaCl solutions. Additionally, Marlstone saturated with Ca2+ ions exhibited greater resistance to leaching compared to those saturated with Na+ ions. The findings of this research can be applied to control the swelling pressure of weak rocks in proximity to support systems.
Original Research Paper
Exploitation
Abbas Khajouei Sirjani; Ruqyah Heydari; Ramin Rafiee; Mohammad Amiri Hosseini
Abstract
In open-pit mining blasting operations, one of the most critical parameters that must be continuously and precisely monitored and evaluated is the extent of back-break caused by the blasts. This phenomenon can lead to mine wall instability, collapse of mining equipment, increased dilution rates, and ...
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In open-pit mining blasting operations, one of the most critical parameters that must be continuously and precisely monitored and evaluated is the extent of back-break caused by the blasts. This phenomenon can lead to mine wall instability, collapse of mining equipment, increased dilution rates, and disruption in drilling and charging operations in subsequent stages. The objective of this research is to predict and optimize back-break by combining statistical models with the Firefly Algorithm (FA). For this purpose, a database comprising data from 28 blasts in the waste rock section of Gol-e-Gohar Iron Ore Mine No. 1 was compiled. After data collection, the input parameters, including blast hole length, burden, spacing, Stemming, charge per delay, and Number of holes in the last row, were identified and utilized in the modeling process. To predict back-break, modeling was performed using multiple regression analysis. Among the developed models, the Polynomial statistical model with non-integer coefficients model with an adjusted coefficient of determination 0.885 was identified as the best-performing model and was subsequently used as the objective function in the Firefly Algorithm. The optimization process was then carried out using this algorithm. According to the findings of this research, the implementation of the current operational patterns in the mine along with the optimized proposed patterns resulted in a reduction of 4 meters in the average back-break, decreasing it from 7.5 meters in the waste rock section. The results demonstrate that the Firefly Algorithm is a highly effective and reliable tool for model optimization and a more accurate reduction of back-breaks. This approach has the potential to significantly enhance the efficiency of mining operations and reduce operational costs.
Original Research Paper
Exploitation
Heydar Bagloo; Mohsen Soleiman Dehkordi
Abstract
Loading and haulage operations in open-pit mining represent a significant portion of overall costs. Among various load and transport systems, the shovel-truck method is favored for its flexibility. Consequently, extensive research has been conducted to optimize this system, resulting in numerous productivity-enhancing ...
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Loading and haulage operations in open-pit mining represent a significant portion of overall costs. Among various load and transport systems, the shovel-truck method is favored for its flexibility. Consequently, extensive research has been conducted to optimize this system, resulting in numerous productivity-enhancing methods. However, evaluating the effectiveness of these optimization techniques, particularly in short-term mining activities under varying operational conditions, remains essential. Additionally, understanding how changes in operational conditions impact productivity is important for addressing production fluctuations in daily mining operations. To tackle these challenges, this study uniquely applies advanced machine learning techniques to short-term mining planning, resulting in the development of a real-time Productivity Evaluation Model (PEM) based on supervised learning methods for optimizing truck-shovel operations in open-pit mining. The model, developed and tested using data from a large-scale mining operation in Iran, demonstrated that the Decision Tree was the most effective, achieving an R² value of 0.96. This was closely followed by Random Forest and Gradient Boosting, both with R² values of 0.95. However, the choice of the most suitable learning method may vary depending on the specific dataset and context. The model determines the most appropriate learning method for each dataset within specific mining operations.
Original Research Paper
Rock Mechanics
Saeed Mahdavi; Mohammad Mohammadi; Raheb Bagherpour
Abstract
EPB machines have been the most applicable for tunneling in urban areas over the last decades. To increase soil consistency, reduce machine torque, and stabilize the tunnel face in EPB tunneling, foam injection is essential. The shear strength of the soil in the EPB chamber affects the machine torque. ...
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EPB machines have been the most applicable for tunneling in urban areas over the last decades. To increase soil consistency, reduce machine torque, and stabilize the tunnel face in EPB tunneling, foam injection is essential. The shear strength of the soil in the EPB chamber affects the machine torque. Therefore, in this research, the effects of soil water content, clay percentage, foam injection ratio, and soil granular size on the shear strength are investigated. The Isfahan subway line 2 in Iran was selected as a case study. Based on the results of the vane shear test, the shear strength of soil first increases rapidly and then gradually with an increase in soil particle size, and particle size is the most significant parameter that controls the shear strength of soil samples. The result of the analysis also indicates that increasing FIR up to 40% can lead to a 44% reduction in soil shear strength and, as a result, a decrease in excavation power. Increasing the clay percentage from 20 to 40 percent reduces the soil shear strength by up to 36 percent. The lowest shear strength of soil is achieved when the water content is 5 percent. By increasing the FIR from 10 to 20 percent, the shear strength of samples decreases rapidly and remains constant when the FIR rises up to 40 percent.
Original Research Paper
Environment
Ali Rasouli; Akbar Esmaeilzadeh; Reza Mikaeil; Solat Atalou
Abstract
Identifying joint sets is essential in engineering geology for rock mass classification and slope stability analysis in mining. Accurate clustering of joint sets based on dip and dip direction enhances the understanding of rock behavior and ensures stability in mine walls. This study presents a novel ...
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Identifying joint sets is essential in engineering geology for rock mass classification and slope stability analysis in mining. Accurate clustering of joint sets based on dip and dip direction enhances the understanding of rock behavior and ensures stability in mine walls. This study presents a novel clustering approach integrating the Harmony Search (HS) and Particle Swarm Optimization (PSO) algorithms to classify joint sets in the Sungun copper mine. Initially, joint characteristics were classified using the Fuzzy C-Means (FCM) method, with the elbow method selecting a four-class clustering solution. To optimize clustering, FCM was combined with HS and PSO, and joint data were assessed using Davies-Bouldin, Calinski–Harabasz, and Silhouette indices. The results demonstrated that the hybrid FCM-PSO method outperformed alternatives, achieving scores of 0.80, 347.48, and 0.57, respectively, indicating superior clustering performance and stability. In contrast, the FCM-HS method performed worse than FCM alone, ranking third overall. The findings confirm that FCM-PSO effectively classifies joint sets, providing reliable insights into rock mass behavior in the Sungun mine. Considering the features and advantages of the FCM-PSO method, it is concluded that the proposed approach has significant potential for effective joint classification in mining engineering. This improved clustering approach enhances geological analysis, supporting safer and more efficient mining operations.
Original Research Paper
Rock Mechanics
Mohammad Reza Zeerak; Mohammad Fatehi Marji; Manouchehr Sanei; Mehdi Najafi; Abolfazl Abdollahipour
Abstract
The Extended Finite Element Method (XFEM) is a leading computational approach for studying crack growth in rocks, as it can effectively model complex crack paths and discontinuities without the need for re-meshing. In this context, XFEM is particularly well-suited for simulating the development of hydraulic ...
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The Extended Finite Element Method (XFEM) is a leading computational approach for studying crack growth in rocks, as it can effectively model complex crack paths and discontinuities without the need for re-meshing. In this context, XFEM is particularly well-suited for simulating the development of hydraulic fractures. XFEM is employed to investigate crack initiation, propagation, and aperture size in rock formations, with validation using a Boundary Element Method (BEM)-based approach. Three scenarios are analyzed for crack orientation and interaction in: single cracks at and crack displacement behavior at and multiple cracks at and . Displacement in the vertical direction (U2) and stress distribution around the crack tip in the S22 direction are examined to understand fracture mechanics parameters. The findings highlight that crack at higher angles, such as , exhibit more straightforward propagation, while those at or beyond often require additional stress to continue growing. The comparison between XFEM and BEM results confirms the reliability of the numerical approach, demonstrating strong agreement in predicting fracture behavior in rock materials. The results provide deeper insights into fracture evolution, stress intensity factors, and fracture toughness in geological media. These simulations advance computational fracture mechanics, contributing to optimizing hydraulic fracturing techniques for improved efficiency and safety in subsurface formations. This study is limited to 2D geometries and isotropic materials, potentially missing 3D heterogeneous subsurface complexities. Future work could explore 3D models, anisotropy, and fluid pressure/thermal effects to improve crack growth predictions.
Original Research Paper
Rock Mechanics
Mohammad Shekari Nejad; Mohammad Fatehi Marji; Manouchehr Sanei
Abstract
The slope geometry, rock mass quality, groundwater level, and geological features of the mine mainly influence the slope stability of an open-pit mine. In this study, the stability analysis of the open pit slope under the influence of various factors was studied. The analysis was conducted based on data ...
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The slope geometry, rock mass quality, groundwater level, and geological features of the mine mainly influence the slope stability of an open-pit mine. In this study, the stability analysis of the open pit slope under the influence of various factors was studied. The analysis was conducted based on data collected from the Golgohar iron ore mine in Sirjan. To build the numerical model, first, the geomechanical and hydrogeological parameters of the mine were determined using laboratory and field tests. Then, numerical models of slope stability were built based on the finite difference method using hydromechanical coupling analysis. The real characteristics in these models include lithology types, variations in geomechanical properties, groundwater level, and real slope geometry. Numerical models were built based on three different conditions, including a model in dry conditions, a model considering the groundwater level, and a model after the drainage process. The results show that the whole slope angle of the mine that has the highest safety factor is 36 degrees. In addition, the groundwater level reduces the safety factor of slope stability compared to dry conditions, and the drainage process can increase the safety factor of the mine wall. In all three conditions, the whole slope angle of 36 degrees has the highest safety factor. Therefore, it is suggested that the whole slope angle be considered to increase the safety factor and reduce the stripping ratio to increase the profitability of the open pit mine.
Original Research Paper
Exploitation
Ali Nemati vardin; Masoud Monjezi; Hasel Amini Khoshalan; Jafar Hamidi Khademi; Mojtaba Rezakhah
Abstract
Drilling is one of the most important operations in open-pit mining, and the penetration rate of drill bits is a key performance measure. This paper presents research on the penetration rate of drill bits based on mining rock mass rating, thrust pressure (weight on bit), rotational pressure, and Schmidt ...
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Drilling is one of the most important operations in open-pit mining, and the penetration rate of drill bits is a key performance measure. This paper presents research on the penetration rate of drill bits based on mining rock mass rating, thrust pressure (weight on bit), rotational pressure, and Schmidt hammer rebound hardness. To achieve this, a dataset comprising the drilling operations of 85 blastholes from the Sungun copper mine in Iran was prepared and analyzed using statistical and intelligent methods. Multivariate regression analysis and artificial neural networks developed in Python, utilizing optimization algorithms such as gradient descent, stochastic gradient descent, and adaptive moment estimation, were applied to predict the penetration rate of drill bits in this study. The coefficient of determination (R²), mean absolute error (MAE), and root mean square error (RMSE) served as performance indicators to evaluate the methods employed. Among these, the adaptive moment estimation (Adam)-based model exhibited superior performance compared to alternative models, achieving values of R² = 0.96, MAE = 4.55, and RMSE = 4.30. Furthermore, the sensitivity analysis revealed that mining rock mass rating is the most influential factor on the rate of penetration, while thrust pressure has the least impact.
Original Research Paper
Exploitation
Mojtaba Rezakhah
Abstract
Optimizing short-term production in open-pit copper mines is crucial for maximizing economic returns and ensuring operational stability, yet is frequently challenged by inherent geological variability. This work presents a novel Mixed-Integer Linear Programming (MILP) framework designed to address these ...
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Optimizing short-term production in open-pit copper mines is crucial for maximizing economic returns and ensuring operational stability, yet is frequently challenged by inherent geological variability. This work presents a novel Mixed-Integer Linear Programming (MILP) framework designed to address these challenges by directly integrating critical geometallurgical parameters, specifically rock hardness (SPI index) and clay content, into the short-term production planning process. The simultaneous integration of these key geometallurgical feed quality attributes within an operational MILP model distinguishes this work from previous approaches and effectively bridges geological data analytics with operational decision-making, aligning economic objectives with enhanced metallurgical performance. Utilizing real operational data from the Sarcheshmeh Copper Mine, the framework was validated over a 186-day period. It achieved optimal production conditions on 137 days (73.6% of the duration), realizing a maximum Net Present Value (NPV) of $132,000. Key outcomes included a significant 21% reduction in concentrate grade variability and a 15% decrease in flotation reagent consumption, achieved through the simultaneous control of SPI and clay content. Advanced statistical methods were employed to identify critical relationships. While the model demonstrates scalability for porphyry copper mines globally, its successful implementation depends on careful parameter customization and alignment with existing infrastructure. This research work underscores the substantial value of data-driven, integrated optimization techniques in enhancing both profitability and process stability within mineral processing circuits.
Original Research Paper
Exploitation
Fatemeh Asadi Ooriad; Javad Gholamnejad; Ali Dabagh
Abstract
Designing and planning in open-pit mining encompass a series of processes that commence with the preparation of a block model. Subsequently, upon designing the final scope, it culminates with the timing and sequencing of mining blocks, with the aim to maximize the pit's value within specific technical ...
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Designing and planning in open-pit mining encompass a series of processes that commence with the preparation of a block model. Subsequently, upon designing the final scope, it culminates with the timing and sequencing of mining blocks, with the aim to maximize the pit's value within specific technical and operational constraints. Mathematical programming methods have proven suitable for optimizing mine production scheduling. Previous studies have addressed various aspects, including the timing of deployment and periodic relocation of in-pit crushers. Nevertheless, significant challenges remain in integrating the in-pit crusher problem with production planning. This paper introduces a new mixed-integer linear programming model for long-term open-pit mine production planning, incorporating constrained pit deepening to enforce predominantly lateral progression throughout the planning horizon. To achieve this, the number of active benches in each time period was reduced, thereby decreasing the need for equipment movement between working benches. Furthermore, with the horizontal progression of the pit, more workspace became available for deploying in-pit crushers, reducing equipment movement costs between benches and overall transportation costs, ultimately lowering the mine's operational expenses. Finally, the proposed model was implemented at the Miduk copper mine. The results demonstrated that the proposed model successfully achieved the expected objectives, resulting in a 52.45% improvement in reducing the number of active benches and regarding execution time reduction, the model showed a 53.32% improvement.
Original Research Paper
Rock Mechanics
Hossein Azad; Hamid Chakeri; Hadi Shakeri
Abstract
Mechanized tunnelling in soft soils often results in ground settlement both around the tunnel and at the surface, which can potentially damage urban infrastructure and surrounding buildings. Several geological and operational factors influence the extent of ground settlement. This paper investigates ...
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Mechanized tunnelling in soft soils often results in ground settlement both around the tunnel and at the surface, which can potentially damage urban infrastructure and surrounding buildings. Several geological and operational factors influence the extent of ground settlement. This paper investigates the actual ground settlement caused by over 10 kilometers of tunnelling along Tabriz Metro Line 2, with a particular focus on the materials and positions of the tunnelling machine. The results show that 55-60% of the total settlements occur behind the shield of the tunnelling machine, which is consistent with Thewes’ (2009) diagram. The surrounding soil was categorized, and using data from settlement pins, the actual Volume Loss (VL) was analyzed across three geological sections consisting of sandy, clayey, and mixed materials. The findings reveal that volume loss in sandy materials is greater than in clayey and mixed soils, at approximately 1.02%. Additionally, the volume loss in mixed soils was calculated to be 0.82%, while in clay soils, it was 0.53%. To assess the impact of different materials on surface settlement, numerical modeling was carried out using Plaxis 3D software. The numerical results, considering volume losses of 1.05% for sandy materials, 0.8% for mixed materials, and 0.5% for clay materials, closely matched the actual settlement data.
Original Research Paper
Exploration
Mohammadreza Agharezaei; Ardeshir Hezarkhani
Abstract
Geochemical exploration as an advantageous exploration method mostly deals with anomaly separation and related endeavors. Many experts have suggested various types of anomaly identification methods. The intention of this research is introduction of a new method for separating geochemical anomalies based ...
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Geochemical exploration as an advantageous exploration method mostly deals with anomaly separation and related endeavors. Many experts have suggested various types of anomaly identification methods. The intention of this research is introduction of a new method for separating geochemical anomalies based on the Fibonacci sequence for the first time. The Fibonacci features of datasets were clarified and the method was introduced and applied on a dataset of bore-hole samples of Hired gold deposit located in southern Khorasan province, Iran. The main result of this study is the successfully establishing of a Fibonacci-based procedure that leads to separate geochemical anomalies. The determined thresholds by this method were compared with U-statistics and Concentration-Volume fractal modeling. Evaluation of the results revealed high consistence between the outcomes of the methods. The U-Statistics threshold for background was 105 ppb for gold and the Fibonacci Transformation method’s threshold was 109 ppb. This new method specified 170 ppb for gold moderate anomaly and the C-V fractal determined 169 ppb which are almost the same. The performance of the new method was assessed by calculating misclassification errors. The average total misclassification error was 0.023 which is acceptable and quite reasonable since the methods are fundamentally different. As the other main results of this study, it is confirmed that one of the Fibonacci features which is defined as Fibonacci index (FI) fluctuates among element pairs in the same way that geochemical correlation does. The FI could be considered as a genetic-related factor in geochemical studies and ore evolution researches.
Original Research Paper
Exploitation
Sina Ghavami; Ebrahim Ghasemi; Mohammad Hossein Kadkhodaei; Ali Farhadian
Abstract
Consumption of cutting and polishing tools is a critical economical parameter during quarrying and processing of granitic building stones, which is highly affected by stone abrasivity. So, estimation of abrasivity for these stones is a very important issue. There are several methods to determine the ...
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Consumption of cutting and polishing tools is a critical economical parameter during quarrying and processing of granitic building stones, which is highly affected by stone abrasivity. So, estimation of abrasivity for these stones is a very important issue. There are several methods to determine the stone abrasivity. One of the most commonly used methods is Cerchar abrasivity index (CAI). This study mainly focuses on investigating the relationship between CAI with petrographic and physico-mechanical properties of granitic building stones. For this purpose, 14 different types of commercial granitic building stones, collected from different regions of Iran, were subjected to laboratory investigations and the effect of the petrographic and physico-mechanical properties of these stones on CAI was examined using simple and multiple regression analysis. Meaningful and reasonable relationships were observed. According to the obtained results, equivalent quartz content (EQC) of granitic building stones was found to be the most effective parameter on CAI. Using linear and nonlinear regression analysis, two empirical correlations for CAI prediction based on EQC were developed. The results showed that both linear and nonlinear correlations have high performance with determination coefficients (R2) of 0.876 and 0.882, respectively. These correlations can determine the CAI with acceptable error, with root mean square error (RMSE) and mean absolute error (MAE) values of 0.135 and 0.105, respectively. Furthermore, the relationship between the diamond segment wear (SW) and CAI was investigated for the studied stones. The results showed that SW is directly related to the CAI, and there is a strong linear correlation between these two parameters with R2 of 0.787. The proposed correlation can be applied for fast prediction of cutting tool wear for practical applications in building stone processing plants with circular sawing machine, which can lead to enhanced cutting efficiency and productivity.
Original Research Paper
Exploration
Marco Antonio Cotrina Teatino; Jairo Jhonatan Marquina-Araujo; Jose Nestor Mamani-Quispe; Solio Marino Arango-Retamozo; Joe Alexis Gonzalez-Vasquez; Kevin Daniel Rondo-Jalca
Abstract
The classification of mineral resources significantly impacts mine planning, economic feasibility, and regulatory compliance. Despite its importance, such classification frequently depends on the subjective judgment of the Qualified Person (QP), owing to the absence of internationally standardized technical ...
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The classification of mineral resources significantly impacts mine planning, economic feasibility, and regulatory compliance. Despite its importance, such classification frequently depends on the subjective judgment of the Qualified Person (QP), owing to the absence of internationally standardized technical criteria for delineating resource categories. To mitigate this limitation, an innovative methodology integrating clustering based on Riemannian geometry with machine learning techniques was developed for mineral resource classification. A database of 5,654 composited samples from 185 diamond drill holes in a copper deposit in central Peru was utilized to classify 318,443 blocks. Copper grades were estimated through Ordinary Kriging (RMSE = 0.102; MAE = 0.069), generating geostatistical variables kriging variance, average distance to samples, and number of samples that served as input features for the classification. Clustering was performed using both classical KMeans and Riemannian KMeans, followed by spatial smoothing via XGBoost and Random Forest algorithms. Absolute coordinates were incorporated to address spatial discontinuities in classification outputs. The combination of the Riemannian model with Random Forest produced the highest classification performance, with a Silhouette index of 0.26 and a Davies-Bouldin index of 0.72. The resulting metal content was estimated at 4.24 Mt of copper at 0.44% grade (measured), 6.49 Mt at 0.34% Cu (indicated), and 7.68 Mt at 0.32% Cu (inferred), demonstrating close alignment with QP estimates while exhibiting improved spatial coherence. In summary, the Riemannian-based approach outperformed classical KMeans and conventional classification methods, providing a more robust, objective, and globally consistent alternative.
Original Research Paper
Exploration
Hamed Norouzi; Aliakbar Daya
Abstract
Estimating mineral reserves in exploration or extraction projects is a critical and challenging process. It must be conducted precisely, regardless of the mining scale and mineral type. With the growing significance of mineral resources in economic and industrial development, the importance of adopting ...
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Estimating mineral reserves in exploration or extraction projects is a critical and challenging process. It must be conducted precisely, regardless of the mining scale and mineral type. With the growing significance of mineral resources in economic and industrial development, the importance of adopting advanced technologies in mineral assessment has also surged. Modern spatial grade modeling techniques can play a pivotal role in decision-making processes. This study aims to compare the performance and capabilities of two popular machine learning methods, including Gaussian Process Regression (GPR) and Multilayer Perceptron Artificial Neural Network (MLP-ANN) in spatial grade modeling of copper at the Chehel Kureh Copper deposit. The dataset comprises 42 drill holes with an average copper grade of 0.18%. Each core sample data point includes seven variables: three spatial coordinates (X, Y, and Depth), lead grade, zinc grade, lithology and copper grade, which serves as the target variable. The Gaussian Process Regression (GPR) and Multilayer Perceptron (MLP-ANN) neural network were employed for copper grade estimation. To make a better assessment, the hyperparameters of both models were optimized using the Bayesian Optimization algorithm. The results showed that the Gaussian Process Regression outperformed MLP-ANN, achieving an RMSE of 0.04 and a coefficient of determination (R²) of 0.89 compared to an RMSE of 0.05 and a coefficient of determination (R²) of for MLP-ANN, suggest the superiority of the Gaussian Process Regression method in estimating copper grade spatial variability.
Original Research Paper
Exploitation
Mehrnaz Mohtasham
Abstract
In open-pit mining, haulage equipment accounts for a significant portion of total operating costs. Optimizing fleet performance is therefore crucial for reducing costs and improving productivity. Within this system, loading equipment plays a key role, as truck efficiency depends heavily on loader performance. ...
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In open-pit mining, haulage equipment accounts for a significant portion of total operating costs. Optimizing fleet performance is therefore crucial for reducing costs and improving productivity. Within this system, loading equipment plays a key role, as truck efficiency depends heavily on loader performance. The match factor, a metric that evaluates compatibility between loaders and trucks, is commonly used to enhance fleet efficiency. However, many existing approaches fail to account for practical mining conditions such as equipment downtime, accurate truck cycle times, and material fragmentation resulting from blasting. These omissions can lead to inaccurate fleet performance evaluations and higher operational costs. This study proposes an improved match factor method that incorporates these critical variables. It includes equipment downtime, truck cycle time estimates based on travel routes, and material fragmentation. The model applies to both homogeneous and heterogeneous fleet configurations and integrates the operational efficiency coefficient of each machine to reflect real conditions more accurately. The model was tested using data from the Sungun copper mine. The match factor values were calculated both with and without accounting for equipment downtime, and loader capacities were adjusted according to the size distribution of blasted material. Results showed that in heterogeneous fleet operations, the match factor increased from 0.74 to 0.85 when operational efficiency was included. Subsystem analyses also revealed match factor values below 1, indicating a need for additional trucks. Overall, the enhanced model enables more efficient equipment use, reduces loader idle time, and contributes to substantial operating-cost savings.
Review Paper
Exploitation
Arman Khosravi; Mohammad Ataei
Abstract
The selection of an appropriate mining method is a complex decision-making problem influenced by a multitude of geological, technical, economic, environmental, and safety-related parameters. This study presents a comprehensive review of multi-criteria decision-making (MCDM) approaches applied to mining ...
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The selection of an appropriate mining method is a complex decision-making problem influenced by a multitude of geological, technical, economic, environmental, and safety-related parameters. This study presents a comprehensive review of multi-criteria decision-making (MCDM) approaches applied to mining method selection, with a focus on their historical evolution, integration with fuzzy logic, artificial intelligence, and machine learning, as well as bibliometric trends and parameter analysis. The findings reveal a growing tendency toward hybrid and intelligent MCDM models that enhance decision accuracy and adaptability under uncertainty. A bibliometric analysis of key authors, countries, journals, and citation patterns highlights the global scope and scientific impact of research in this area. Furthermore, the study categorizes influencing parameters into intrinsic and extrinsic groups, identifying ore geometry, grade distribution, and rock mass properties as dominant intrinsic factors, while economic, environmental, and operational considerations represent significant extrinsic influences. This review emphasizes the vital role of MCDM techniques in optimizing mining operations, and advocates for further development of dynamic, data-driven models to meet the evolving challenges of modern mining.
Original Research Paper
Exploitation
saeideh Qaedrahmat; Javad Gholamnejad; Ali dabagh
Abstract
The scheduling of short-term production in open-pit mining requires determining an optimal extraction sequence for blocks to fulfill multiple goals over a short-term monthly, weekly and daily planning horizon. These goals include meeting required limits on ore grade, production tonnage, waste removal, ...
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The scheduling of short-term production in open-pit mining requires determining an optimal extraction sequence for blocks to fulfill multiple goals over a short-term monthly, weekly and daily planning horizon. These goals include meeting required limits on ore grade, production tonnage, waste removal, and slope constraints. One of the key objectives of Short-Term Production Scheduling (STPS) is to ensure a stable and continuous supply of ore to the processing plant, while minimizing operating costs through measures such as reducing unnecessary equipment movements and variation in feed quality. However, one of the major obstacles to the operational feasibility of STPS is the limited working space available for equipment, as well as the excessive equipment movement between benches within each scheduling period. To tackle these challenges, this paper employs an Integer Goal Programming (IGP) with a new constraint that limits active benches per period, enhancing the practicality of production schedules. Unlike previous GP-based STPS models, it improves operational feasibility by ensuring extraction continuity and minimizing equipment movement. The model was tested on a copper deposit using GAMS software. The results show that by applying this new constraint, the average number of active benches per month was reduced from 14 to 10 )36% reduction) and the number of extraction periods per bench from 6 to 4 (33% reduction) without violating the existing constraints such as ore grade, tonnage, or slope. This approach improves equipment efficiency, reduces fuel consumption, reducing equipment relocation costs, promoting operational continuity of extraction and enhances operational feasibility in real conditions.
Original Research Paper
Exploitation
Mohammad Reza Rezaei; Majid Noorian-Bidgoli
Abstract
Drilling and blasting are crucial operations in open-pit mining, aimed at optimizing rock fragmentation, minimizing negative effects like backbreak and flyrock, and reducing costs, while enhancing efficiency and minimizing environmental and infrastructure impacts. This study focuses on optimizing drilling ...
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Drilling and blasting are crucial operations in open-pit mining, aimed at optimizing rock fragmentation, minimizing negative effects like backbreak and flyrock, and reducing costs, while enhancing efficiency and minimizing environmental and infrastructure impacts. This study focuses on optimizing drilling and blasting patterns at the Miduk copper mine using Multi-Criteria Decision-Making (MCDM) methods. The primary objectives were to achieve optimal fragmentation, minimize specific charge and drilling costs, and reduce undesirable phenomena like backbreak and flyrock caused by blasting. A total of 52 blasting patterns implemented at the mine were evaluated using various MCDM techniques, including TOPSIS, ELECTRE, VIKOR, and COCOSO. By constructing decision matrices and ranking the alternatives in each method, the most suitable blasting pattern was identified. The Copeland method was further applied to integrate the results from the decision models and establish a consensus on the final ranking of blasting patterns based on the criteria. The study's innovation lies in the application of advanced MCDM techniques to optimize drilling and blasting patterns, as well as the integration of results to enhance the decision-making process's accuracy. The optimal blasting pattern (M_Patt_03) was found to feature a burden of 6.5 meters, a spacing of 8 meters, and a borehole diameter of 150 millimetres, offering the best balance of fragmentation, charge efficiency, and drilling costs, while minimizing backbreak and flyrock. This study demonstrates the effectiveness of MCDM methods in optimizing complex engineering challenges in surface mining, providing a comprehensive framework for evaluating multiple criteria simultaneously and enabling more informed and balanced decision-making.
Original Research Paper
Environment
Fatemeh Vesmoridi; Feridon Ghadimi
Abstract
A total of 400 stream sediment samples were analyzed for 13 elements, and stepwise factor analysis was employed to generate geochemical maps indicative of mineralization. This method was utilized to develop a Geochemical Mineralization Probabilistic Index (GMPI) through a novel approach that produces ...
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A total of 400 stream sediment samples were analyzed for 13 elements, and stepwise factor analysis was employed to generate geochemical maps indicative of mineralization. This method was utilized to develop a Geochemical Mineralization Probabilistic Index (GMPI) through a novel approach that produces geochemical evidence maps derived from stream sediment data. The study comprised a three-stage factor analysis of geochemical data collected from the Khomain Dehno region. The first factor included Zn, Pb, As, and Cd, accounting for 41.63% of the variance. The second factor comprised Mn, Mo, and Zr, explaining 21.86% of the variance, while the third factor consisted of Fe, Cu, and Ti, representing 7.79% of the variance. The cumulative variance explained by these three factors was 81%. Furthermore, a novel intelligent methodology, termed Relevant Vector Regression (RVR), enhanced with Cocoa Search (CS) and Harmony Search (HS) algorithms, is proposed for the prediction of the GMPI. The HS and CS algorithms were integrated with the RVR model to optimize its hyperparameters. In these models, Zn, Pb, As, and Cd served as input variables, while the GMPI was designated as the output variable. The performance of the predictive models was evaluated using Mean Squared Error (MSE) and the Coefficient of Determination (R²). The results indicated that the RVR model optimized with the HS algorithm exhibits superior performance, achieving an R² value of 0.99256 and an MSE of 0.0031455. These findings underscore the efficacy of the proposed approach for accurate GMPI estimation.
Original Research Paper
Environment
Mohammad Hadi Salehzadeh; Hadi Farhadian; Saeed Yousefi; Mohammad Dehju
Abstract
This study aims to assess the environmental impacts of coal mining in the Eastern Alborz region, focusing on coal mines from 2013 to 2021, using remote sensing techniques. Landsat 8 satellite images were digitized based on key environmental indices, including NDVI, NDWI, NDSI, and NDBI, and subsequent ...
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This study aims to assess the environmental impacts of coal mining in the Eastern Alborz region, focusing on coal mines from 2013 to 2021, using remote sensing techniques. Landsat 8 satellite images were digitized based on key environmental indices, including NDVI, NDWI, NDSI, and NDBI, and subsequent statistical analyses and evaluations were conducted for the study areas. To distinguish the effects of mining from those of climate change, the results were compared with a reference area located within a natural resource block (baseline area), and the outcomes were thoroughly analyzed. The findings indicate that the combined impacts of mining and climate change have caused significant environmental degradation in the region. In particular, vegetation cover has experienced a sharp decline in recent years, while soil erosion has increased at a slower rate. Projections of mining impacts on vegetation and soil were made by calculating the average NDVI and NDSI indices for 2030 and 2050 in the studied areas. These projections suggest that NDVI is expected to decrease by 0.25 by 2030 and by 0.72 by 2050, indicating further vegetation loss in the coming decades. In contrast, analysis of the NDWI index reveals no clear trend in soil moisture changes over the study period. Given the climatic conditions of the selected areas, it is essential to monitor, manage, and mitigate environmental risk factors to prevent the expansion of drought into northern forests, highlighting the need for appropriate intervention measures.
Original Research Paper
Exploration
Marco Antonio Cotrina Teatino; Jairo Jhonatan Marquina-Araujo; Jose Nestor Mamani-Quispe; Solio Marino Arango-Retamozo; Joe Alexis Gonzalez-Vasquez
Abstract
The geochemical and spatial characterization of legacy mine tailings is essential for identifying reprocessing opportunities and informing environmental management. However, the high compositional complexity of polymetallic tailings requires robust multivariate approaches. This study evaluates and compares ...
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The geochemical and spatial characterization of legacy mine tailings is essential for identifying reprocessing opportunities and informing environmental management. However, the high compositional complexity of polymetallic tailings requires robust multivariate approaches. This study evaluates and compares the performance of four unsupervised clustering algorithms Euclidean K-Means, Riemannian K-Means, Gaussian Mixture Model (GMM), and Agglomerative Clustering applied to 927 samples from the Quiulacocha tailings deposit in Peru, using six major elements (Zn, Pb, Cu, Fe, Ag, Au) and spatial coordinates. All methods consistently identified three main geochemical domains. Cluster 1 was enriched in Cu and Au, Cluster 2 in Pb and Fe, and Cluster 3 in Zn, Ag, and Fe. Covariance-based methods (Riemannian K-Means and Agglomerative Clustering) outperformed others in internal validation (Silhouette scores up to 0.58) and consistency (Adjusted Rand Index = 1.00), offering more interpretable and geologically coherent partitions. CLR transformation reduced clustering performance, highlighting the importance of preserving raw geochemical variance for spatial segmentation. These findings demonstrate the effectiveness of multivariate clustering for unraveling compositional heterogeneity in tailings and delineating domains of potential economic value. The approach provides a quantitative framework for supporting reprocessing decisions, reducing risk, and guiding future research on mine waste valorization.
Original Research Paper
Environment
Aditi Nag
Abstract
The transformation of post-industrial mining sites into heritage tourism destinations represents a growing global trend, yet remains underexplored in India. This paper investigates the repositioning potential of Dhori, Jharkhand—a site with dual significance as a devotional landmark and a post-mining ...
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The transformation of post-industrial mining sites into heritage tourism destinations represents a growing global trend, yet remains underexplored in India. This paper investigates the repositioning potential of Dhori, Jharkhand—a site with dual significance as a devotional landmark and a post-mining landscape—through the application of two established competitiveness frameworks: Dwyer & Kim’s Integrated Destination Competitiveness model and Porter’s Diamond Model. Drawing from a robust dataset of 441 stakeholder responses and employing perceptual mapping, cluster analysis, and ANOVA, the study identifies key strengths in cultural identity and community engagement, contrasted by critical weaknesses in interpretive infrastructure, service integration, and institutional coordination. Comparative analysis with national (Kenapara, Raniganj) and international (Ruhr Valley, Wieliczka Salt Mine) case studies further underscores the structural and narrative gaps Dhori must address. The findings inform a phased strategy—short-, mid-, and long-term—accompanied by a data-driven Competitiveness Monitoring Toolkit grounded in nine thematic criteria. The study contributes an India-specific empirical model for post-mining tourism transitions, highlighting how dual-identity sites can achieve competitive positioning through integrated cultural, environmental, and economic strategies.
Original Research Paper
Rock Mechanics
Shadman Mohammadi Bolbanabad; Masoud Monjezi; Vahab Sarfarazi
Abstract
The characteristics of fragment size distribution caused by blasting operations in open-pit mines have a direct impact on the economic performance and productivity of mining companies. In this study, dynamic impact loading tests were carried out using the Split Hopkinson Pressure Bar (SHPB) system under ...
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The characteristics of fragment size distribution caused by blasting operations in open-pit mines have a direct impact on the economic performance and productivity of mining companies. In this study, dynamic impact loading tests were carried out using the Split Hopkinson Pressure Bar (SHPB) system under a constant pressure of 12.5 MPa to investigate the influence of both the edge notch length and its position relative to the incident bar on the size distribution of fragmented iron ore. By analyzing the fragmentation distribution characteristics of specimens subjected to controlled laboratory impact loading, this study focuses on fundamental rock breakage mechanisms relevant to blasting operations in open-pit iron ore mines, where the fragmented material classified into three particle size categories: large, medium, and fine fragments. Based on this classification, the variation in the mass percentage of fragments with respect to notch length and its position relative to the incident bar was investigated. Ultimately, within the context of laboratory-scale fragmentation analysis, an effective range of notch lengths and positions relative to the incident bar was identified for achieving optimal fragmentation. The results revealed that a notch length between 0.2 and 0.4 and a notch position between L/2 and 2L/3 from the incident bar (where L equals sample length), produced the most favorable fragment size distribution. These findings can help link laboratory-scale fracture behavior to field-scale rock fragmentation considerations and contribute to a broader understanding of breakage processes in mining engineering.
Original Research Paper
Rock Mechanics
Navid Afrasiabi; Mehdi Noroozi; Ahmad Ramezanzadeh
Abstract
In this research, the effect of geometric parameters of closely joints on rock cutting efficiency by TBM disc cutter is studied using PFC3D software. A validated numerical model of linear cutting machine test is developed and the efficiency of disc cutter is investigated on rock mass specimens with different ...
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In this research, the effect of geometric parameters of closely joints on rock cutting efficiency by TBM disc cutter is studied using PFC3D software. A validated numerical model of linear cutting machine test is developed and the efficiency of disc cutter is investigated on rock mass specimens with different joint configurations (possible combination of dip angles of 30, 60, 90 degrees with joint spacings of 3, 5, 10, 15, 20 cm). Numerical modeling results reveal that in general, the joint spacing has a greater effect on rock cutting efficiency than joint orientation. If the joint spacing is less than 10 cm, the role of the joint angle is reduced and the distances between the joints control the efficiency. When the joints are close together and have a spacing of less than 10 cm, particularly 3 to 5 cm, the best cutting efficiency can be achieved for a joint angle of 90 degrees. The cutting coefficient is decreased by increasing the joint spacing and the maximum CC occurs at a joint spacing of 5 cm. For joint spacing more than 10 cm, the joints with a 90 degrees dip angle have the greatest impact on the specific energy and reduce cutting efficiency. The best disc cutter efficiency and the minimum required normal force is achieved when joint spacing is more than 10 cm and the angle between the joints and advance direction of the disc cutter is 60 degrees. In the tunnel excavation process, with increasing joint spacing, the TBM machine thrust is more important than its torque. The findings of this research provide a basis for predicting TBM efficiency through joint characteristics.
Original Research Paper
Exploration
Naresh Kumar Katariya; Bhanwar Singh Choudhary
Abstract
Slope stability and bench safety in iron ore open-pit mines in western India are comprehensively analysed in this research. To evaluate current mining conditions and identify areas at risk, the study integrates comprehensive field observations, laboratory testing, and advanced slope stability modelling ...
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Slope stability and bench safety in iron ore open-pit mines in western India are comprehensively analysed in this research. To evaluate current mining conditions and identify areas at risk, the study integrates comprehensive field observations, laboratory testing, and advanced slope stability modelling using Slide 6.0 software. Factors of safety (FOS) of various mining sections varied from 0.475 to 1.495, as per limit equilibrium analysis with Slide 6.0. This signifies the presence of possibly unstable slopes that require certain stabilisation measures to ensure operational safety. The research considers how significant environmental factors, like temperature, wind speed, rainfall, and soil moisture, influence slope stability in addition to the geotechnical analysis. Rainfall and soil moisture were found to have a high and statistically significant positive correlation (Pearson correlation = 0.706, p = 0.005), implying that an increase in rainfall results in increased soil moisture content, which in turn affects the behaviour of slopes. Also, a moderate degree of negative relationship between temperature and wind speed was revealed (partial correlation = -0.593, p = 0.042), meaning that smaller wind speeds are characteristically associated with increased temperatures. These findings highlight the importance of continuous monitoring of the environment in open-pit mine operations and the importance of considering environmental factors when assessing slope stability. The information collected in this study provides a solid foundation for developing valuable recommendations intended to enhance safety, better control slopes, and promote the long-term development of mining activities in the region.
Original Research Paper
Exploitation
Samia Chaoui; Adel Djellali; Benghazi Zied; Sarker Debojit
Abstract
This study aims to investigate the stability of rooms and pillars along the inclined zinc orebody at the Chaabet El Hamra underground mine (Setif, Algeria). Stability was initially assessed using an analytical shear strength model, with the results subsequently validated through numerical modeling. Geomechanical ...
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This study aims to investigate the stability of rooms and pillars along the inclined zinc orebody at the Chaabet El Hamra underground mine (Setif, Algeria). Stability was initially assessed using an analytical shear strength model, with the results subsequently validated through numerical modeling. Geomechanical characterization revealed low interstitial porosity, strong to very strong uniaxial compressive strengths ranging from 50.4 MPa to 129 MPa, and significant fracture-related secondary porosity. Rock Mass Rating (RMR89) and Geological Strength Index (GSI) values suggest fair to good rock quality. The mine design features square pillars inclined at 10°, with walls originally oriented perpendicular to the orebody dip, measuring 5 m in width and 3 m in height. The rooms, situated under a cover depth of 145.3 m, are 9 m wide. This configuration yielded an effective extraction rate of 87.24% and a safety factor of 1.63, indicating stable mining conditions. Phase 2D finite-element simulation confirmed these findings, showing a maximum displacement of 3.96 mm, surface subsidence of 0.57 mm, and a safety factor of 1.66, suggesting minimal environmental impact and long-term stability. Shear/compressive stress results from tributary area theory, aligning with numerical results and validating both approaches for inclined orebodies. Finally, the pillar walls, originally perpendicular to the orebody dip, were modified to be vertical relative to the horizontal plane, while maintaining the same pillar and room dimensions and cover depth. This adjustment improved stability by enhancing stress distribution and pillar core confinement, increasing the safety factor to 1.85.
Original Research Paper
Exploitation
Tapan Dey; Gopinath Samanta
Abstract
Accurate grade prediction is an important step in the mining planning process. Various methods, namely the Inverse Distance Method and Kriging, are widely used. The application of machine learning is a new development in the grade estimation technique. The present study focused on the application of ...
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Accurate grade prediction is an important step in the mining planning process. Various methods, namely the Inverse Distance Method and Kriging, are widely used. The application of machine learning is a new development in the grade estimation technique. The present study focused on the application of XGBoost, Random Forests (RFs), Multi Layer Perceptron (MLP), and Gradient Boosting Regression (GBR) models to predict iron ore grades in an Indian mine. An ensemble model was also applied to obtain a more stable grade prediction in the deposit. Models were trained using 4,112 sample data, which have spatial coordinates (east, north, and altitude) and iron grades. The dataset was divided into two parts: 80% (3,289 samples) of the data was used for model training, and 20% (823 samples) was used for model testing. The performance of the models was assessed through the coefficient of determination (R²), Mean Squared Error (MSE), and Mean Absolute Percentage Error (MAPE). The results show that the XGBoost model performs better in the estimation process when compared with other methods, such as RFs, GBR, and MLP. The XGBoost model produced R² of 0.77, MSE of 2.87, and MAPE of 1.8%. The findings indicate that the XGBoost model is effective for predicting iron ore grades in this type of deposit. However, considering geological uncertainty, the application of an ensemble model may be beneficial for grade prediction in an iron deposit.
Case Study
Environment
Asep Nurohmat Majalis; Muhammad Razzaaq Al Giffari; R Arif Suryanegara; M Rifat Noor; Rachmat Ramadhan; Noviarso Wicaksono
Abstract
Due to its large nickel reserves, Indonesia has become one of the world's largest nickel mining sites and producers. Nickel is a mining commodity with high economic value. However, its mining activity can negatively impact the environment if not managed properly. Therefore, mitigation of the impact of ...
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Due to its large nickel reserves, Indonesia has become one of the world's largest nickel mining sites and producers. Nickel is a mining commodity with high economic value. However, its mining activity can negatively impact the environment if not managed properly. Therefore, mitigation of the impact of nickel mining is necessary. This research has conducted erosion and infiltration tests at various locations in pre-nickel mining zones to mitigate the environmental impact of nickel mining activity. Erosion tests were performed using a rainfall simulator with five nozzles on a 12.5 m² demo plot. Infiltration tests were conducted using a double-ring infiltrometer. The result shows that surface runoff coefficients for disposal, limonite, saprolite, and quarry zones were higher than those for vegetated zones such as grassland, pepper plantation, and forest. The saprolite zone released the highest sediment load, i.e., 484.3 kg ha-1 hour-1, followed by the limonite and the pepper plantation zone, with 243.6 kg ha-1 hour-1 and 185 kg ha-1 hour-1. The highest Cr(VI) concentration, 0.7 mg L-1, was released from the disposal zone, followed by the saprolite, limonite, and pepper plantation zones, with concentrations of 0.56, 0.06, and 0.06 mg L-1, respectively. The infiltration equation obtained from each zone shows that revegetation can significantly reduce runoff. Therefore, revegetation should be prioritized in addition to end-of-pipe treatment to mitigate the impact of nickel mining activities.
