Original Research Paper
Rock Mechanics
Barkat Ullah; Raja Khurram Mahmood Khan
Abstract
Uniaxial compressive strength (UCS) is an essential feature for characterizing and classifying rock masses, forming a critical component of rock failure criteria with extensive applications in mining and geotechnical engineering. This study aims to evaluate the performance of different machine learning ...
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Uniaxial compressive strength (UCS) is an essential feature for characterizing and classifying rock masses, forming a critical component of rock failure criteria with extensive applications in mining and geotechnical engineering. This study aims to evaluate the performance of different machine learning (ML) models in forecasting the UCS of sandstone obtained from the Murree and Kamlial formations in the Muzaffarabad area, northwestern Himalayas, Pakistan. The ML models—namely artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS), support vector regressor (SVR), random forest (RF), and extreme gradient boosting (XGBoost)—were developed to predict UCS (MPa) based on porosity (η), point load index (Is(50)), Schmidt hammer rebound value (Rn), and aggregate impact value (AIV) as input variables. A dataset containing 80 points was divided using a 70:30 split ratio for training and testing sets. K-fold cross-validation (with 5 to 10 folds) was employed to enhance the models' generalization ability. The performance of the models was evaluated using mean absolute error (MAE), mean square error (MSE), root mean square error (RMSE), and coefficient of determination (R²). Results revealed that the XGBoost model outperformed the other models, achieving a high R² value of 0.99 and low error values for MAE (0.789), MSE (1.168), and RMSE (1.080). The overall accuracy of the models can be ranked as follows: XGBoost > RF > ANN > ANFIS > SVR. This study provides a benchmark for predicting the UCS of sandstones and similar rocks where complex geology complicates the collection of intact samples.
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
Exploitation
Feng Yang; Pengjie Li; Qiang Sun
Abstract
Large coal pillars result in significant resource waste. The high stress concentration within these pillars also creates safety hazards for the working face. To address this, a cooperative mining method for section coal pillars is proposed. This method is designed for seams with large inclination angles ...
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Large coal pillars result in significant resource waste. The high stress concentration within these pillars also creates safety hazards for the working face. To address this, a cooperative mining method for section coal pillars is proposed. This method is designed for seams with large inclination angles and that are extremely close to overlying pillars. The technical principles are explained. First, FLAC3D simulation software was used to investigate the effect of the spacing between the lower roadway and the section coal pillar, which determined the optimal roadway position. Then, a coupled FLAC-PFC method was employed to optimize the coal drawing process parameters. The optimal scheme was analyzed to characterize roof deformation, stress distribution, and hydraulic support loads. An engineering case study demonstrates that a spacing greater than 18 m minimizes the influence of concentrated stress, resulting in limited deformation and improved roadway stability. The study investigates coal drawing under different sequences, port widths, and methods. The optimal process was identified as downward drawing, with a 1.5 m coal drawing port width and a two-wheel sequential method. This process achieves a drawing rate of 85.62% and a gangue content of 4.61%. Analysis shows that during the pillar drawing process, the concentrated stress on the roof plate is significantly reduced, with a maximum stress decrease of 21.1 MPa, effectively alleviating stress concentration. The total force on the section hydraulic support in fully mechanized caving is 1.6×10⁴ kN, while the force in the fully mechanized mining section is 1.4×10⁴ kN.
Original Research Paper
Exploitation
Pintu Kumar Mandal; Niroj Kumar Mohalik; Manoj Kumar Mishra; Gautam Chandra Mondal
Abstract
The swift extraction from underground coal mines in the Raniganj coalfield (RCF) encounters various safety challenges, including multi-seam operations, extraction of water-logged seams, areas where upper seams have been depleted, strata management issues, subsidence, ventilation problems, heat, humidity, ...
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The swift extraction from underground coal mines in the Raniganj coalfield (RCF) encounters various safety challenges, including multi-seam operations, extraction of water-logged seams, areas where upper seams have been depleted, strata management issues, subsidence, ventilation problems, heat, humidity, spontaneous combustion, and mine fires. Among these challenges, many underground coal mines continue to operate after dewatering the coal seams for production purposes. Spontaneous combustion poses a significant risk in the dewatered coal seams of underground mines, impacting the safety of both the mines and the miners. This study aims to assess the risk of spontaneous combustion in a water-immersed coal seam of RCF by conducting proximate analysis, TGA/DSC, FTIR studies, and water analysis. One coal sample was obtained from the RV seam at the Kottadih coal mine in RCF and was immersed in tap water at a ratio of 1:10. The water-immersed coal samples were removed after 15, 30, and 90 days for sample preparation and other experimental investigations. The experimental results indicate that the water-immersed coal samples exhibit optimal moisture levels (4–8%), a higher volatile matter content (>30.0%) compared to fresh samples, and a gradual decrease in the ignition temperature of the water-immersed coal over time. There is an increase in concentrations of functional groups such as Ar-, -CHO, >C=O, and -C=C- due to the adsorption of dissolved organic compounds onto the coal surface. All analyses suggest that the rise in organic compounds contributes to the accelerated risk of spontaneous combustion.
Original Research Paper
Environment
Zaenal Zaenal; Noor Fauzi Isniarno; Delina Mutiara; Sofie Nur’aini; Hasyim Fadhilah; Elfida Moralista; Andrieanto Nurrochman
Abstract
Blasting is a fundamental open-pit mining operation necessary for rock breakage, but it also generates significant environmental noise pollution. Excessive noise from blasting not only endangers health but also poses problems to compliance with regulations, particularly in regions where acoustic standards ...
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Blasting is a fundamental open-pit mining operation necessary for rock breakage, but it also generates significant environmental noise pollution. Excessive noise from blasting not only endangers health but also poses problems to compliance with regulations, particularly in regions where acoustic standards differ, such as Indonesia's use of both dBL and dBA standards. This research addresses the need for reliable and context-dependent predictive models for blasting noise, aiming to compare analytical and empirical formulas with machine learning techniques in dBA prediction. Measurements were conducted at 30 blasts at an open-pit coal mine in Indonesia, South Sumatra, using homogeneous acoustic sensors. The measured data points for frequency, dBL, and dBA were matched to calculated data using equations. Random Forest (RF) and Artificial Neural Network (ANN) predictive models using measured frequency and dBL as predictive variables were also derived. Results show that used Finn-derived equation has poor predictive accuracy, with errors exceeding 80%. Among the analytical and empirical models, Equation 3 performed the best, with an average error of 9%, while a site-spesific regression model based on measurements had an improved error rate of 5%. Machine learning models outperformed all models, with the RF model exhibiting an average error of 2% and demonstrating higher stability and consistency. The ANN model also did well, but with more variation and some overestimations.
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
Environment
Tulika Gupta; Mahasakti Mahamaya; Shamshad Alam
Abstract
The dumping of mining waste occupies extensive areas of land and poses environmental hazards, including heavy metal leaching, dust pollution, and slope failure. Iron mine overburden (MO), a byproduct of iron mining, exacerbates these issues when dumped. To address the challenges of storing MO, it was ...
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The dumping of mining waste occupies extensive areas of land and poses environmental hazards, including heavy metal leaching, dust pollution, and slope failure. Iron mine overburden (MO), a byproduct of iron mining, exacerbates these issues when dumped. To address the challenges of storing MO, it was combined with fly ash and cement to develop controlled low-strength material (CLSM). Initially, the raw materials were examined for their physical, chemical, and mineralogical properties. Subsequently, 24 different CLSM mixtures were prepared by varying cement, fly ash, MO, and water-to-binder ratios. The fresh mixes were tested for flowability, bleeding, and fresh density, while the hardened properties, including density, unconfined compressive strength (UCS), and durability, were also evaluated. Results showed that all CLSM mixes were highly flowable, with flow diameters exceeding 150 mm, and some exhibited self-leveling behavior. The 28-day compressive strength ranged from 0.52 MPa to 4.28 MPa, with a few mixes being soft enough for manual excavation. Durability tests indicated that approximately 60% of the mass remained intact after eight wet-dry cycles, demonstrating good resistance to erosion. This study highlights the potential for utilizing mining waste in sustainable construction materials.
Original Research Paper
Environment
Aditi Nag
Abstract
This research evaluates the viability of mining heritage tourism (MHT) as a strategic pathway for sustainable regional development, using the Barr Conglomerate in Pali, Rajasthan, as a case exemplar. Positioned within the broader discourse on reactivating post-industrial landscapes, the study adopts ...
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This research evaluates the viability of mining heritage tourism (MHT) as a strategic pathway for sustainable regional development, using the Barr Conglomerate in Pali, Rajasthan, as a case exemplar. Positioned within the broader discourse on reactivating post-industrial landscapes, the study adopts a mixed-method design that integrates perceptual surveys (n = 440) with multivariate tools—including Exploratory Factor Analysis (EFA), Principal Component Analysis (PCA), and Discriminant Function Analysis (DFA)—to decode stakeholder attitudes and assess spatially differentiated tourism potential. Eight experiential themes emerge from the PCA, encompassing infrastructure adequacy, site distinctiveness, safety perception, interpretive depth, and cultural resonance. While respondents recognize Barr’s strong geo-heritage value and visual appeal, persistent deficiencies in accessibility, safety management, and narrative infrastructure constrain its tourism readiness. Findings demonstrate the site’s potential to be repositioned through themed geo-trails, multi-sensory interpretive environments, and community-based tourism models. Segment-specific discriminant profiles reveal differing perceptual priorities across tourists, residents, and experts, underscoring the need for tailored branding strategies rooted in geological authenticity, memory landscapes, and living community heritage. Benchmarking against Rajasthan’s regional tourism motivations—adventure, authenticity, storytelling, and geotourism—further highlights the competitive niche Barr can occupy within state-level heritage circuits. The study proposes a scalable, data-driven framework that couples perceptual clustering with participatory planning, offering a replicable model for transforming abandoned extraction sites into culturally rich, economically resilient, and ecologically responsive heritage destinations.
Original Research Paper
Environment
Elena Drobinina; Marina Kitaeva; Artem Mizev; Elizaveta Romanova
Abstract
The study presents an integrated approach to karst susceptibility assessment using Geographic Information Systems (GIS) and Remote Sensing (RS) data for sinkhole mapping and spatial analysis. The approach enables rapid and reliable karst susceptibility assessment in areas where linear infrastructure ...
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The study presents an integrated approach to karst susceptibility assessment using Geographic Information Systems (GIS) and Remote Sensing (RS) data for sinkhole mapping and spatial analysis. The approach enables rapid and reliable karst susceptibility assessment in areas where linear infrastructure has been designed within the Pivovarovo karst area (Vladimir Region, Russia). The research highlights the advantages of automated zoning along the construction route based on both sinkhole distribution and environmental conditions. A significant methodological contribution to the assessment of karst susceptibility is the development of a custom Python-based tool for the automated morphometric analysis of sinkholes, including diameter measurement and orientation assessment. This approach provides an effective solution for karst susceptibility assessment, because it enables the rapid processing of large datasets, producing high-quality results that can support engineering design decisions.
Original Research Paper
Environment
Abolfazl Shafaei; Abdolmotaleb Hajati; Feridon Ghadimi
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
Mohammad Mohammadi; Saeed Mahdavi; 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
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.
