Original Research Paper
Mineral Processing
Kwang Sok Jong; Chang Il Kim; Kum Chon Jang; Song Chol Kim; Hyon Hui Jang
Abstract
In this study, the effects of various reagents-sodium carbonate and sodium hydroxide as pH regulators, calcium lignosulfonic acid as depressant, and combined sodium oleate and amide as collector on the flotation of apatite ore were investigated using flotation experiments, and adsorption mechanism of ...
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In this study, the effects of various reagents-sodium carbonate and sodium hydroxide as pH regulators, calcium lignosulfonic acid as depressant, and combined sodium oleate and amide as collector on the flotation of apatite ore were investigated using flotation experiments, and adsorption mechanism of collector on apatite surface were evaluated using quantum mechanical simulations. The flotation experiments showed that the addition of 4 kg/t sodium carbonate and 1.5 kg/t sodium hydroxide as pH regulators, 3 kg/t calcium lignosulfonic acid as depressant and 60 g/t combined sodium oleic acid and oleamide (acid number of collector; 105 mgKOH/g) as collector exhibited excellent collecting ability for apatite. From low-grade apatite ore with P2O5 7.05%, a concentrate with P2O5 31.42% was obtained with 81.08% recovery in rougher flotation. Compared with the simulation results for the interaction energy between apatite {001} surface and collectors, and the relative concentration of collector on apatite {001} surface, adsorption strength has following order; combined sodium oleic acid and oleamide > sodium oleic acid > oleamide. From the simulation results on the equilibrium configuration of the collector with the fluorapatite {001} surface in the liquid environment, it was revealed that the two atoms (N and H) of the oleamide can form a strong bidentate conformation, and O atom in the C-O group and that in -C=O group of oleic acid anion can bond with the Ca atom on the surface {001} to form monodentate conformation.
Original Research Paper
Exploration
Kamran Mostafaei; Mohammad Nabi Kianpour; Mahyar Yousefi; Meisam Saleki
Abstract
Discrimination of geochemical anomalies from background is a challenge in that elemental dispersion patterns are affected by a variety of geological factors, which vary from one to another area. While statistical and fractal methods are commonly employed for anomaly detection, they struggle with selecting ...
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Discrimination of geochemical anomalies from background is a challenge in that elemental dispersion patterns are affected by a variety of geological factors, which vary from one to another area. While statistical and fractal methods are commonly employed for anomaly detection, they struggle with selecting optimal thresholds. This study proposes the Grey Wolf Optimizer (GWO) algorithm as a novel approach for identifying the optimal boundary between anomalies and background. Stream sediment geochemical data from a copper-mineralized area of the Sarduiyeh-Baft sheets in southeast Iran were utilized for analysis. The Geochemical Mineralization Probability Index (GMPI) was first calculated for Cu-Au, Mo-As, Pb-Zn, and porphyry distributions. Subsequently, fractal methods were used to identify anomalous populations within each GMPI. The GWO algorithm was then applied to these distributions to determine the optimal thresholds. Risk analysis, calculated as the ratio of covered copper occurrences to the covered area, revealed superior reliability for the GWO-derived limit compared to those obtained using fractal methods. For porphyry GMPI values, while the fractal reliability indices are 0.127, 0.44, and 0.5, the GWO limit achieved a value of 0.66. Risk analysis for Cu-Au distribution also caused more desired result for GWO limit rather that fractal ones. This demonstrates the enhanced performance and superior reliability of the GWO algorithm for optimizing anomaly detection thresholds in GMPI data.
Original Research Paper
Exploitation
Gopinath Samanta; Tapan Dey; Suranjan Sinha
Abstract
The optimal layout of the stope (stope boundary) in an underground metal mine maximizes the profit of a deposit, subject to the geotechnical and operational mining constraints such as stope length, stope width, stope height. Various approaches have been introduced to address the stope boundary optimization ...
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The optimal layout of the stope (stope boundary) in an underground metal mine maximizes the profit of a deposit, subject to the geotechnical and operational mining constraints such as stope length, stope width, stope height. Various approaches have been introduced to address the stope boundary optimization problem, but due to the computational complexity and numerous practical constraints, the existing models offer partial solutions to the problem. In the present work, a mixed integer programming model has been developed by incorporating mining constraints in a three-dimensional framework. This model is developed based on profit maximization. The sensitivity analysis applied in a case study mine indicates that the model is efficient in assessing the upside potential and downside risk of profit under fluctuating metal prices and mining costs. Additionally, it can be applied at different stages of mine design to facilitate resource appraisal, selection of stoping methods, and comprehensive mine planning. In a practical application on a real orebody, it shows that the proposed model can generate upto 37.32% more profit compared to current stope design practice in the mines.
Original Research Paper
Exploitation
Marco Antonio Cotrina Teatino; Jairo Jhonatan Marquina Araujo; Jose Nestor Mamani Quispe; Solio Marino Arango-Retamozo; Johnny Henrry Ccatamayo-Barrios; Joe Alexis Gonzalez-Vasquez; Teofilo Donaires-Flores; Maxgabriel Alexis Calla-Huayapa
Abstract
Mining plays a crucial role in the economy of many countries, contributing significantly to GDP, employment, and industrial development. However, optimizing drilling and blasting operations remains a key challenge in open-pit mining due to its direct impact on operational costs and rock fragmentation ...
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Mining plays a crucial role in the economy of many countries, contributing significantly to GDP, employment, and industrial development. However, optimizing drilling and blasting operations remains a key challenge in open-pit mining due to its direct impact on operational costs and rock fragmentation efficiency. This work aims to optimize fragmentation (X50) and drilling and blasting costs using hybrid machine learning models, an innovative approach that improves predictive accuracy and economic feasibility. Six models were developed: Artificial Neural Networks (ANNs), Decision Trees (DT), Extreme Gradient Boosting (XGBoost), Random Forest (RF), and Support Vector Regression (SVR), optimized using Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). The dataset, comprising 100 blasts, was split into 70% for training and 30% for testing. The SVR+PSO model achieved the highest accuracy for fragmentation prediction, with an RMSE of 0.27, MAE of 0.21, and R2 of 0.92. The RF+GA model was most effective for cost prediction, with an RMSE of 414.58, MAE of 354.14, and R2 of 0.99. Optimization scenarios were implemented by reducing burden (4.3 m to 3.8 m) and spacing (5.0 m to 4.5 m), achieving a 5.7% reduction in X50 (17.6 cm to 16.6 cm) and a 9.5% cost decrease (63,000 USD to 57,000 USD per blast). Predictions for 30 future blasts using the RF + GA model estimated a total cost of 1.7 MUSD, averaging 55,180 USD per blast. These findings confirm the effectiveness of machine learning in cost optimization and improving blasting efficiency, presenting a robust data-driven approach to optimizing mining operations.
Original Research Paper
Environment
Ahmed Amara KONATE; Djénébou Bourama SANGARE; Baba Faradji N'DIAYE; N'dji dit Jack DEMBELE
Abstract
Artisanal gold mining (AGM) is one of Mali's most significant economic activities. This activity become a serious environmental concern because of the chemicals used for AGM. The artisanal gold processing method uses a significant amount of water. The operating waste, often toxic by chemical mercury, ...
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Artisanal gold mining (AGM) is one of Mali's most significant economic activities. This activity become a serious environmental concern because of the chemicals used for AGM. The artisanal gold processing method uses a significant amount of water. The operating waste, often toxic by chemical mercury, is discharged or drained into watercourses. This study describes the main actors and the different methods of exploration, extraction, and processing of gold by artisanal miners, as well as their perception of environmental impacts. The methodology adopted is based on surveys and observations conducted at the Lollè and Sinty sites. The results show that no mining legislative rules were applied. Still, non-standard customary rules for a very long time. AGM has a particular organizational chart that actors manage. This study shows that AGM can cause health and environmental problems, especially with the use of chemicals, contamination of water resources, land degradation, and destruction of fauna and flora. Analysis of water samples shows that the mercury concentration at Lollè exceeds the WHO standard, while most mercury concentrations at Sinty are below this standard. The geomorphology of the study area shows a watershed with an area of 88.40 km2 with four orders of the hydrographic network in Lollè and 404.02 km2 with five orders in Sinty. While the slopes range from very weak to strong, and the study areas are practically flat. This study will provide accurate information to policy-makers for implementing environmental management strategies in a manner that miners can understand and evaluate.
Original Research Paper
Exploitation
Patrick Adeniyi Adesida; Sunday Adex Adaramola
Abstract
This study focuses on predicting the drillability of granitic rocks—precisely the wear rate of button bits, by integrating rock strength and mineralogical properties. The objective is to develop a predictive model for bit wear rate using a Rock Engineering System (RES) approach. Key rock parameters ...
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This study focuses on predicting the drillability of granitic rocks—precisely the wear rate of button bits, by integrating rock strength and mineralogical properties. The objective is to develop a predictive model for bit wear rate using a Rock Engineering System (RES) approach. Key rock parameters (uniaxial compressive strength, porosity, specific gravity, and the mineral content of quartz, plagioclase, hornblende, and biotite) were analysed via a RES interaction matrix to derive a new Drillability Index capturing their combined influence. This analysis revealed that UCS and porosity are the most influential factors in the system. The resulting RES-based model correlates strongly with observed bit wear rates, achieving a high coefficient of determination (R² ≈ 0.93) and low prediction errors (RMSE = 2.79, MAE = 2.14). The MAPE (= 38%) indicates a marked improvement in accuracy over traditional regression methods. Integrating mechanical and mineralogical factors is a novel approach to drillability prediction, providing a more comprehensive account of rock characteristics than conventional models. Validation results show that the RES-derived Drillability Index reliably predicts field performance, offering practical value for optimising drilling operations and guiding geomechanical analysis. Additionally, the study proposes a drillability classification scheme to further support the field application of the findings.
Original Research Paper
Exploitation
Gebremariam Mesele; Miruts Hagos; Bheemalingeswara Konka; Tsegabrhan Gebreset; Misgan Molla; N Rao Cheepurupalli; Girmay Hailu; Negassi Debeb; Assefa Hailesilasie
Abstract
The Dallol Depression, located in the northern Danakil Depression, has a complex geological history shaped by Afar rifting, containing approximately 1.7 km of evaporite deposits. These deposits, heavily influenced by volcanic activity and extensional tectonic faulting, exhibit significant structural ...
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The Dallol Depression, located in the northern Danakil Depression, has a complex geological history shaped by Afar rifting, containing approximately 1.7 km of evaporite deposits. These deposits, heavily influenced by volcanic activity and extensional tectonic faulting, exhibit significant structural variability. This research focuses on the potash-bearing section of the salt sequence, which consists of several distinct layers including the marker bed, sylvinite member, upper carnallitite member, bischofitite member, lower carnallitite member, and kainitite member. Employing satellite imagery (Landsat Thematic Mapper), geological and structural mapping, borehole data, and seismic analysis, this study characterizes the sub-surface features of the evaporites and estimates their reserves. The RockWorks software facilitated the development of a subsurface stratigraphic map and a three-dimensional fence diagram for enhanced interpretation. Seismic data indicate that while the upper layers of the evaporite deposits are largely horizontal and undeformed, deeper layers exhibit considerable tectonic disturbance. Thickness variations were observed, with evaporite and alluvial deposits being thinner at the southeastern rim and thicker in the eastern concession center. The total potash reserve is estimated at approximately 2.96 billion tons, of which 877.76 million tons (29.60%) remain unexploited. Current borehole designs restrict the company's extraction capacity to 24.64%. This study recommends revising mining strategies, incorporating updated borehole designs and advanced geophysical methods to improve potash recovery and promote sustainable practices in the Dallol region.
Original Research Paper
Rock Mechanics
Mahdi Bajolvand; Ahmad Ramezanzadeh; Amin Hekmatnejad; Mohammad Mehrad; Shadfar Davoodi; Mohammad Teimuri; Mohammad Reza Hajsaeedi; Mahya Safari
Abstract
Bit wear is one of the fundamental challenges affecting the performance and cost of drilling operations in oil, gas, and geothermal wells. Since identifying the factors influencing bit wear rate (BWR) is essential, and the ability to predict its variations during drilling operations is influenced by ...
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Bit wear is one of the fundamental challenges affecting the performance and cost of drilling operations in oil, gas, and geothermal wells. Since identifying the factors influencing bit wear rate (BWR) is essential, and the ability to predict its variations during drilling operations is influenced by environmental and operational factors, this study aims to develop an Adaptive Bit Wear Rate Predictor (ABWRP) algorithm for estimating the BWR during drilling operations for new wells. The structure of this algorithm consists of a data transmitter, data processor, deep learning-based bit wear rate estimator, and a bit wear updating module. To develop a model for the BWR estimation module, data from two wells in an oil field in southwest Iran were collected and analyzed, including petrophysical data, drilling data, and bit wear and run records. Both studied wells were drilled using PDC bits with a diameter of 8.5 inches. After preprocessing the data, the key factors affecting the bit wear rate were identified using the Wrapper method, including depth, confined compressive strength, maximum horizontal stress, bit wear percentage, weight on bit, bit rotational speed, and pump flow rate. Subsequently, seven machine learning (ML) and deep learning (DL) algorithms were used to develop the bit wear rate estimation module within the ABWRP algorithm. Among them, the convolutional neural network (CNN) model demonstrated the best performance, with Root Mean Square Error (RMSE) values of 0.0011 and 0.0017 and R-square (R²) values of 0.96 and 0.92 for the training and testing datasets, respectively. Therefore, the CNN model was selected as the most efficient model among the evaluated models. Finally, a simulation-based experiment was designed to evaluate the performance of the ABWRP algorithm. In this experiment, unseen data from one of the studied wells were used as data from a newly drilled well. The results demonstrated that the ABWRP algorithm could estimate final bit wear with a 14% error. Thus, the algorithm developed in this study can play a significant role in the design and planning of new wells, particularly in optimizing drilling parameters while considering bit wear effects.
Original Research Paper
Rock Mechanics
Amin Jamshidi; Deniz Akbay
Abstract
Brazilian tensile strength (BTS) is an important parameter in mining activities, particularly in conditions that rocks are under tensile stresses. This test measures the indirect tensile strength of rocks, which is crucial for understanding the mechanical behavior and quality of rocks in the mining context, ...
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Brazilian tensile strength (BTS) is an important parameter in mining activities, particularly in conditions that rocks are under tensile stresses. This test measures the indirect tensile strength of rocks, which is crucial for understanding the mechanical behavior and quality of rocks in the mining context, including slope stability analysis, blast design, rock support systems, excavation and equipment selection, fracture propagation, and hydraulic fracturing and drilling. So far, no classification of tensile strength of rock for mining applications has been presented. In the present study, a new rock classification based on BTS for the various rocks was proposed. To achieve this purpose, by a reviewing previous studies, uniaxial compressive strength (UCS) and BTS of various rock classes, including igneous, sedimentary, and metamorphic were collected. For each rock class, the correlation equations between UCS and BTS were developed using simple regression analysis. Using data analyses, the rocks was categorized into to seven BTS classes. The findings revealed that igneous, sedimentary, and metamorphic rocks have a wide range of BTS values, and subsequent fall into the different BTS classes. The validity of BTS classification was verified using data of BTS and UCS of various rock classes published in the literature, and results showed that BTS can be as a suitable indicator for preliminary assessment of rock quality. This can lead to a better understand from the strength behavior of the rock under tensile stresses in site a mining activity, and therefore, a more accurate design of a mining project.
Review Paper
Environment
Subhash Chandra Devrath; Aditi Nag; Sanjeev Pareek
Abstract
This paper explores sustainable redevelopment strategies for post-mining regions by integrating urban voids and underground housing solutions. Mining landscapes, often characterized by degraded environments, socio-economic stagnation, and underutilized spaces present significant challenges and opportunities ...
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This paper explores sustainable redevelopment strategies for post-mining regions by integrating urban voids and underground housing solutions. Mining landscapes, often characterized by degraded environments, socio-economic stagnation, and underutilized spaces present significant challenges and opportunities for transformation. Urban voids such as abandoned pits, industrial complexes, and obsolete worker settlements can be repurposed into green infrastructure, public amenities, or residential spaces. Underground housing, leveraging the natural insulation of subsurface environments, offers energy-efficient solutions, while preserving surface land for ecological and communal uses. The research proposes a conceptual framework that combines the adaptive reuse of urban voids with innovative underground housing designs to enhance urban attractiveness, sustainability, and inclusivity. key indexing metrics, including environmental, socio-economic, and urban attractiveness indicators, are developed to evaluate the effectiveness of redevelopment efforts. Case studies from Germany, Belgium, France, and the USA illustrate these strategies' practical applications and transformative potential. The findings emphasize the importance of addressing socio-economic constraints, environmental remediation, and regulatory challenges through participatory planning, innovative governance, and public-private partnerships. The paper concludes by identifying areas for future research, including socio-cultural acceptance of underground housing, region-specific policy frameworks, and advanced remediation technologies. This study provides a comprehensive roadmap for transforming mining regions into vibrant, sustainable, resilient urban environments.
Original Research Paper
Exploration
V S S A Naidu Badireddi; Vije durga raju Mullagiri; mvs sekhar Bezawada; K S N Reddy; Ambili V
Abstract
The Bavanapadu-Nuvvalarevu coastal sector in Andhra Pradesh, India, hosts substantial subsurface heavy mineral (HM) resources, presenting significant economic potential. This study employs ArcGIS raster techniques to estimate Total Heavy Mineral (THM) and Total Economic Heavy Mineral (TEHM) resources ...
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The Bavanapadu-Nuvvalarevu coastal sector in Andhra Pradesh, India, hosts substantial subsurface heavy mineral (HM) resources, presenting significant economic potential. This study employs ArcGIS raster techniques to estimate Total Heavy Mineral (THM) and Total Economic Heavy Mineral (TEHM) resources in a 39 square kilometers area, integrating geospatial analysis with field data from core sediment samples. The findings reveal a total of 2.681953 million tons of THM, including 2.434422 million tons of TEHM, with the highest concentration observed in the top 1-meter sea bed sediment layer (1.605286 million tons). Ilmenite, garnet, and sillimanite dominate the mineral assemblage, accompanied by smaller quantities of zircon, monazite, and rutile, offering an estimated revenue potential of $634 to $851 million USD. The application of ArcGIS methodologies, particularly inverse distance weighting (IDW) interpolation, enabled precise mapping of HM distribution, despite challenges such as wide sample spacing and shallow core penetration. While the study highlights the economic and industrial significance of the Bavanapadu sector, it also underscores environmental concerns, including habitat disruption and sediment degradation, associated with mining. Sustainable practices, such as advanced separation technologies, site rehabilitation, and comprehensive environmental impact assessments (EIAs), are essential to mitigate ecological impacts. This research demonstrates the efficacy of GIS-based techniques in resource estimation and sustainable mining, offering a replicable framework for coastal and offshore mineral resource management globally. The findings provide critical insights into balancing economic growth with environmental preservation, setting a benchmark for responsible heavy mineral extraction in dynamic coastal environments.
Original Research Paper
Environment
Triyani Dewi; Zakirah Raihani Ya’la; Ali Husni; Tri Joko Santoso; Samliok Ndobe; Eka Rosyida; Maemunah Maemunah; Marhawati Mappatoba; Muhammad Saleh Nurdin
Abstract
This study was conducted to determine heavy metal concentrations in sediments, assess the level of contamination using a contamination index, and identify potential sources of heavy metal contamination using multivariate analysis. This study employed contamination indices to evaluate sediment pollution ...
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This study was conducted to determine heavy metal concentrations in sediments, assess the level of contamination using a contamination index, and identify potential sources of heavy metal contamination using multivariate analysis. This study employed contamination indices to evaluate sediment pollution levels. Heavy metal concentrations were analyzed statistically by determining the minimum, maximum, mean, and standard deviation (SD) values. According to the contamination factor (Cf), Cd showed very high contamination levels, whereas Sn, Ni, and Pb indicated moderate contamination. Hg, As, Cr, and Cu were classified as having low levels of contamination. The degree of contamination (Cdeg) ranged from low to high across the sampled sites, reflecting the varied levels of pollution severity. Multivariate statistical analyses, including Principal Component Analysis (PCA), Pearson correlation matrix, and Cluster Analysis (CA), were used to identify potential sources of heavy metal contamination. Cu, Sn, Ni, Hg, and Cr are attributed to natural geological processes, whereas Pb, Cd, and As are linked to anthropogenic activities, likely originating from the nickel mining industry. In conclusion, this study underscores the complex environmental impact of nickel mining in Morowali, highlighting the need for stringent environmental management practices to mitigate further degradation and safeguard the coastal ecosystems in Central Sulawesi.
Original Research Paper
Environment
Feridon Ghadimi; Amirhossein Solaimani
Abstract
Chogan region is located in the west of the Urmia-Dokhtar volcanic belt and northwest of the Markazi province in Komijan City. Copper mineralization has a vein type with a length of 260 meters and an average thickness of 4 meters. Mineralization was taken in a sheared silica vein. Eighty three samples ...
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Chogan region is located in the west of the Urmia-Dokhtar volcanic belt and northwest of the Markazi province in Komijan City. Copper mineralization has a vein type with a length of 260 meters and an average thickness of 4 meters. Mineralization was taken in a sheared silica vein. Eighty three samples were taken from the surface ground, in the trenches and it determined the concentration of 10 elements such as Fe, Al, Ca, Ba, S, Mn, As, Pb, Zn, and Cu. It was determined, that S, Ba, Mn, Fe, and Cu are secondary elements in the tuffs by the method of factor and cluster analysis. The constituent mineral such as barite and malachite are vein-shaped, but iron oxides such as hematite and goethite in the form of iron gossan. Geochemical, mineralogical, and geophysical (IP/RS) indices were investigated to separate copper oxide and copper sulfide zones. Sulfur and Ba were used in barite and excess S was chosen as sulfide index (Is). Chalcopyrite and metal factor were chosen as separating oxide and sulfide zones. By combining the geochemical and metal factor, it was approximated the apparent sulfide zone depth and confirmed with actual depth in borehole and error was less than 12%.
Original Research Paper
Exploration
Bardiya Sadraeifar; Maysam Abedi; Seyed Hossein Hosseini
Abstract
The Shavaz iron deposit, located in the southwest Yazd province in Central Iranian Block, near The Bafq metallogenic belt, is a significant and economically valuable iron oxide-apatite resource. It features hematite and a minor content of magnetite, detectable through potential field geophysical ...
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The Shavaz iron deposit, located in the southwest Yazd province in Central Iranian Block, near The Bafq metallogenic belt, is a significant and economically valuable iron oxide-apatite resource. It features hematite and a minor content of magnetite, detectable through potential field geophysical surveys. This study aimed to target magnetite mineralization within the deposit using constrained susceptibility inversion. We began by simulating a multi-source synthetic model with three identical cubes at different depths to evaluate the sparse norm inversion approach. The method was then applied to the case study after the essential magnetic data corrections. To refine the interpretation of residual magnetic anomalies and gain insights into their source and depth, the analytic signal and upward continuation methods were employed. Inversion results across different cross-sections revealed two distinct, shallow, lens-shaped magnetite mineralizations with an average vertical extent of 60 meters. Notably, one magnetite body lies approximately 30 meters deeper due to the Dehshir-Baft fault influence. Low normalized mis-fit values confirmed the successful minimization of the objective function during inversion. Additionally, the reconstructed susceptibility models align well with the previous geological studies and borehole data, demonstrating the efficiency of the sparse norm inversion algorithm.
Original Research Paper
Exploitation
Abbas Khajouei Sirjani; Farhang Sereshki; Mohammad Ataei; Mohammad Amiri Hossaini
Abstract
The most significant detrimental consequence of blasting operations is ground vibration. This phenomenon not only causes instability in the mine walls but also extends its destructive effects to various facilities and structures over several kilometers. Various researchers have proposed equations for ...
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The most significant detrimental consequence of blasting operations is ground vibration. This phenomenon not only causes instability in the mine walls but also extends its destructive effects to various facilities and structures over several kilometers. Various researchers have proposed equations for predicting Peak Particle Velocity (PPV), which are typically based on two parameters: the charge per delay and the distance to the blast site. However, according to different studies, the results of blasting operations are influenced by several factors, including the blast pattern, rock mass properties, and the type of explosives used. Since artificial intelligence technology has not yet been fully assessed in the mining industry, this study employs linear and nonlinear statistical models to estimate PPV at Golgohar Iron Ore Mine No. 1. To achieve this goal, 58 sets of blasting data were collected and analyzed, including parameters such as blast hole length, burden thickness, row spacing of the blast holes, stemming length, the number of blast holes, total explosive charge, the seismograph's distance from the blast site, and the PPV recorded by an explosive system using a detonating fuse. In the first stage, ground vibration was predicted using linear and nonlinear multivariate statistical models. In the second stage, to determine the objective function for optimizing the blast design using the shuffled frog-leaping algorithm, the performance of the statistical models was evaluated using R², RMSE, and MAPE indices. The multivariate linear statistical model, with R² = 0.9247, RMSE = 9.235, and MAPE = 12.525, was proposed and used as the objective function. Ultimately, the results showed that the combination of the statistical model technique with the shuffled frog-leaping algorithm could reduce PPV by up to 31%.
Original Research Paper
Exploration
mobin saremi; Abbas Maghsoudi; Reza Ghezelbash; mahyar yousefi; Ardeshir Hezarkhani
Abstract
Mineral prospectivity mapping (MPM) is a multi-step and complex process designed to narrow down the target areas for exploratory activities in subsequent stages. To pinpoint promising zones of porphyry copper mineralization in the Varzaghan district, NW Iran, various exploration evidence layers were ...
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Mineral prospectivity mapping (MPM) is a multi-step and complex process designed to narrow down the target areas for exploratory activities in subsequent stages. To pinpoint promising zones of porphyry copper mineralization in the Varzaghan district, NW Iran, various exploration evidence layers were employed in alignment with the conceptual model of these deposits. These layers encompass fault density, proximity to intrusive rocks, multi-element geochemical anomalies, and distances to phyllic and argillic alterations. The geochemical anomaly maps, recognized as the most effective layers, were generated through staged factor analysis (SFA) and the geochemical mineralization probability index (GMPI). Other layers were weighted using a logistic function, and their values were transformed into 0 -1 interval. Ultimately, to integrate the weighted layers, the fuzzy gamma operator and the geometric average method were applied. The normalized density index and prediction-area (P-A) plot were employed to evaluate the MPM models. The findings indicate that the developed models possess considerable validity and can be effectively utilized for planning future exploration endeavors.
Original Research Paper
Mineral Processing
Mohammad Jahani Chegeni; Sajad Kolahi; Asghar Azizi
Abstract
Consumed energy is the most important issue and concern in industrial ball mills, and includes a major part of the costs of mineral processing plants. By using suitable liners and the optimal lifter count, the energy of the mill is properly transferred to the balls. In Part 1 of this research work, five ...
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Consumed energy is the most important issue and concern in industrial ball mills, and includes a major part of the costs of mineral processing plants. By using suitable liners and the optimal lifter count, the energy of the mill is properly transferred to the balls. In Part 1 of this research work, five types of liners, i.e. Lorain, Osborn, Rib, cuboid, and Hi-lo, are examined. These liners all have separate lifters with the same volume. Their difference is in the width, height, and type of lifter profile. First, all types of liners are simulated with four lifters using the Discrete Element Method (DEM). Then the lifter count is increased four by four to fill the entire wall of the mill with lifters. Based on this, Lorain liner from 4 to 24 lifters, Osborn liner from 4 to 120 lifters, Rib liner from 4 to 40 lifters, and cuboid and Hi-lo liners from 4 to 64 lifters are simulated. For the first time, the kinetic (KE) and potential (PE) energies as well as the sum of these two energies (TE) of all the balls are calculated, and compared in the entire duration of the simulation from 0–13s for all the liner types and lifter counts mentioned above. Finally, by using data related to KE, PE, and TE for each type of liner, the optimal lifter count is obtained. Accordingly, 16 to 20 lifters are recommended for the Lorain liner, 64 to 76 lifters for the Osborn liner, 24 to 32 lifters for the Rib liner, 44 lifters for the cuboid liner, and 36 to 44 lifters for the Hi-lo liner.
Original Research Paper
Environment
amirhossein karimi; Amin Falamaki; farid soltani; mehdi homaee; nader shariatmadari
Abstract
Mining activities have led to the accumulation of large quantities of mineral tailings containing potentially hazardous metals, contaminating the surrounding soil. This study investigated the effectiveness of electrokinetic remediation combined with washing solvents for the decontamination of zinc and ...
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Mining activities have led to the accumulation of large quantities of mineral tailings containing potentially hazardous metals, contaminating the surrounding soil. This study investigated the effectiveness of electrokinetic remediation combined with washing solvents for the decontamination of zinc and lead from mine tailings. Samples were collected from various locations within the Angouran mine in Zanjan, Iran, and analyzed for total metal concentration using the standard ICP method. Electrokinetic tests were conducted using different washing solutions—hydrochloric acid, nitric acid, acetic acid, and sulfuric acid—each at a concentration of 0.1 M and mixed with soil in a 1:2 solution-to-solid ratio. A voltage of 1.5 V/cm was applied throughout the experiments. To mitigate heavy metal precipitation near the cathode, the same chemical solutions were used in the cathode chamber. The results demonstrated that distilled water resulted in the lowest removal efficiency for zinc (16%) and lead (11.5%), while hydrochloric acid showed the highest removal efficiencies of 64% for zinc and 45% for lead. These findings indicated that electrokinetic remediation, particularly when using hydrochloric acid as a complexing agent, was an effective method for removing significant quantities of zinc and lead from contaminated soil.
Original Research Paper
Exploitation
Hamid Saberi; Mohammad Golmohammadi; Mohammadali Zanjani; Yaghoub Saberi
Abstract
The Bavanapadu-Nuvvalarevu coastal sector in Andhra Pradesh, India, hosts substantial subsurface heavy mineral (HM) resources, presenting significant economic potential. This study employs ArcGIS raster techniques to estimate Total Heavy Mineral (THM) and Total Economic Heavy Mineral (TEHM) resources ...
Read More
The Bavanapadu-Nuvvalarevu coastal sector in Andhra Pradesh, India, hosts substantial subsurface heavy mineral (HM) resources, presenting significant economic potential. This study employs ArcGIS raster techniques to estimate Total Heavy Mineral (THM) and Total Economic Heavy Mineral (TEHM) resources in a 39 square kilometers area, integrating geospatial analysis with field data from core sediment samples. The findings reveal a total of 2.681953 million tons of THM, including 2.434422 million tons of TEHM, with the highest concentration observed in the top 1-meter sea bed sediment layer (1.605286 million tons). Ilmenite, garnet, and sillimanite dominate the mineral assemblage, accompanied by smaller quantities of zircon, monazite, and rutile, offering an estimated revenue potential of $634 to $851 million USD. The application of ArcGIS methodologies, particularly inverse distance weighting (IDW) interpolation, enabled precise mapping of HM distribution, despite challenges such as wide sample spacing and shallow core penetration. While the study highlights the economic and industrial significance of the Bavanapadu sector, it also underscores environmental concerns, including habitat disruption and sediment degradation, associated with mining. Sustainable practices, such as advanced separation technologies, site rehabilitation, and comprehensive environmental impact assessments (EIAs), are essential to mitigate ecological impacts. This research demonstrates the efficacy of GIS-based techniques in resource estimation and sustainable mining, offering a replicable framework for coastal and offshore mineral resource management globally. The findings provide critical insights into balancing economic growth with environmental preservation, setting a benchmark for responsible heavy mineral extraction in dynamic coastal environments.
Original Research Paper
Exploitation
Hadi Fattahi; Mohammad Amirabadifarahani; Hossein Ghaedi
Abstract
This study introduces an innovative application of the Power Deck method to optimize drilling and blasting operations in open-pit mining, with a focus on the Nizar cement factory in Qom, Iran. Unlike traditional blasting techniques, this method strategically utilizes a controlled air gap at the end of ...
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This study introduces an innovative application of the Power Deck method to optimize drilling and blasting operations in open-pit mining, with a focus on the Nizar cement factory in Qom, Iran. Unlike traditional blasting techniques, this method strategically utilizes a controlled air gap at the end of each blast hole to enhance explosive energy distribution, thereby reducing excessive drilling and minimizing explosive consumption. Through five blast phases, optimal hole diameters (76 mm and 90 mm) were implemented while maintaining a standardized 1-meter air gap, eliminating the need for additional drilling tests. The findings demonstrate a significant improvement in blasting efficiency, leading to a 12.5% reduction in specific charge and a 9% decrease in specific drilling compared to conventional methods. Post-blast fragmentation analysis, validated using the F50 index from Split-Desktop software, confirmed particle sizes ranging from 10 to 32 cm, aligning with predictions from the Kaz-Ram, Kaznetsov, and Swedifo models. Furthermore, the adoption of the Power Deck method resulted in a 1,448-ton increase in processed material over two months, minimizing crusher downtime due to oversized fragments. This study provides a novel, cost-effective approach to improving rock fragmentation, reducing blasting-related inefficiencies, and enhancing the overall economic performance of open-pit mining operations.
Original Research Paper
Exploration
Shoopala Uugulu; Nazlene Poulton; Akaha Tse; Martin Harris; Taiwo Bolaji
Abstract
The long mining history in Namibia has resulted in numerous abandoned mining sites scattered throughout the country. Past research around the Klein Aub abandoned Copper mine highlighted environmental concerns related to past mining. Considering that residents of Klein Aub depend solely on groundwater ...
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The long mining history in Namibia has resulted in numerous abandoned mining sites scattered throughout the country. Past research around the Klein Aub abandoned Copper mine highlighted environmental concerns related to past mining. Considering that residents of Klein Aub depend solely on groundwater for their domestic, irrigation and other uses, there is a need to thoroughly investigate groundwater quality in the area to ascertain the extent of the contamination. This study characterises groundwater quality using a comprehensive quality assessment approach. On-site parameters reveal that pH ranges between 6.82-7.8, electrical conductivity ranges between 678 - 2270 μS/cm, and dissolved oxygen ranges between 1.4 -5.77 mg/L. With the exception of two samples, the onsite parameters indicate that water is of excellent quality according to the Namibian guidelines. The stable isotopic composition ranges from -7.26 to -5.82‰ and -45.1 to -35.9‰ for δ18O and δ2H, respectively. The groundwater plots on and above the Global Meteoric Water Line, and the best-fit line is characterised by a slope of 4.9, implying the evaporation effect. Hydrochemical analyses indicate bicarbonate and chloride as dominant anions, while calcium and sodium are dominant cations, indicating groundwater dissolving halite and mixing with water from a recharge zone. The Heavy Metal Pollution Index suggested that the water samples are free from heavy metal pollution. The Heavy Metal Evaluation Index clustered around 3, implying that heavy metals moderately affect groundwater. The groundwater quality is suitable for irrigation purposes. The findings offer valuable insights into the area's hydrochemistry and highlight potential environmental risks; hence, groundwater monitoring is recommended.
Case Study
Environment
Aditi Nag; Anurag Singh Rathore
Abstract
This research is focused on analyzing the possibilities and challenges of developing tourism in mining heritage cities (MHCs) within conflict areas. These cities simultaneously have vibrant historical and cultural resources and tourism possibilities in the context of security threats and infrastructural ...
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This research is focused on analyzing the possibilities and challenges of developing tourism in mining heritage cities (MHCs) within conflict areas. These cities simultaneously have vibrant historical and cultural resources and tourism possibilities in the context of security threats and infrastructural inadequacy, which usually characterize conflict areas. The study aims to find ways of boosting tourism competitiveness for such areas with a specific interest in formulating sustainable tourist management policies that foster community involvement and cultural heritage protection. The case study analyzes different conflict areas, representing the best practices and the most effective way of exploiting heritage in mining and luring tourist attractions based on the authentic experience. The results exhibit how tourism can serve as an agent towards economic recovery and social empowerment and acts towards peacebuilding in conflict-affected areas. This study furnishes pragmatic recommendations for legislators, the tourism sector, and community members to favor a more robust and inclusive tourism model that benefits the local community and cultural heritage conservation. Finally, the paper underlines the need to understand the complexity of tourism in conflict areas, using some invisible resources for renewal and growth.
Original Research Paper
Rock Mechanics
Manthri Rakesh; Ashish Kumar Dash; Sunny murmu
Abstract
India's growing energy demand has intensified the need for efficient and safe coal extraction methods, particularly in underground mining, where mechanized depillaring using Continuous Miner (CM) technology has gained prominence. This study explores the critical role of Cut-Out Distance (COD) in optimizing ...
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India's growing energy demand has intensified the need for efficient and safe coal extraction methods, particularly in underground mining, where mechanized depillaring using Continuous Miner (CM) technology has gained prominence. This study explores the critical role of Cut-Out Distance (COD) in optimizing production and ensuring safety during mechanized depillaring operations. COD, defined as the stable drivage length that can be cut without support, directly impacts productivity, roof stability, and operational safety. Despite its importance, there are no standardized guidelines for determining COD in Indian coal mines, leading to trial-and-error practices that compromise efficiency and safety. This paper reviews global and domestic practices, highlighting the inadequacies in existing methods for COD estimation. It identifies key factors influencing COD, including Rock Mass Rating (RMR), roof elasticity, geological conditions, and machinery capabilities. The work also examines case studies of strata control failures in Indian coal mines, highlighting the consequences of improper strata assessment in mines. The research work advocates for the development of standardized guidelines tailored to Indian mining conditions by integrating numerical simulations and machine learning tools for precise COD estimation. A flow chart of methodology for the development of guidelines is proposed; the findings aim to enhance safety, reduce accidents, and improve productivity, paving the way for sustainable growth in India's underground coal mining sector.
Case Study
Exploration
reza Shahnavehsi; Farnusch Hajizadeh
Abstract
The present work is mainly about a method for illustrating the relation between the raw data in the same time; clustering is a key procedure to solve the problem of data division; also illustrating the connection among the elements of the research area simultaneously is important. Therefore, we propose ...
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The present work is mainly about a method for illustrating the relation between the raw data in the same time; clustering is a key procedure to solve the problem of data division; also illustrating the connection among the elements of the research area simultaneously is important. Therefore, we propose a novel kind of clustering for data mining in the gravity field to reach the presenting connection among all elements in the same time. For this research work, 867 gravity surveying points were collected in the southern part of Iran (near diapir of Larestan) with a range of absolute gravity from 978579.672 to 978981.186. In this paper, clustering by self-organizing- maps, by utilizing scatter plot matrix is utilized for detecting the relation between the easting, northing, elevation, and absolute gravity simultaneously. In the proposed method, the relations between arrays, two by two, are defined, and like matrix, each raw and column has different i and j values, which represent elements of the studied area, instead of number; for example, array A23 is data division between i = 2 or raw two (in our case northing) and j = 3 or column, three (in our case elevation). In this algorithm, firstly, by using self-organizing maps, clustering is done, and this processing is generated to all arrays by scatter plot matrix, and in all arrays, three clusters are proposed; the result of this clustering shows that in arrays A12, A13, A14, A21, A23, A24, A31, A32, A41, A42, clustering is performed perfectly, and the relationship between the parameters of the studied area near Larestan salt, diaper, can be useful in notifying the properties of this salt diapir.
Original Research Paper
Mineral Processing
Sajad Kolahi; Mohammad Jahani Chegeni; Asghar Azizi
Abstract
In Part 2 of this research work, five types of liners, i.e. wave, step, step@, ship-lap, and ship-lap@, are examined. These liners all have similar connected lifters with different volumes. Their difference is in the width, height, and type of the lifter profile. All the five liner types, from 8 to 64 ...
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In Part 2 of this research work, five types of liners, i.e. wave, step, step@, ship-lap, and ship-lap@, are examined. These liners all have similar connected lifters with different volumes. Their difference is in the width, height, and type of the lifter profile. All the five liner types, from 8 to 64 lifters, are simulated using the Discrete Element Method (DEM). In this research work, for the first time, data from the sum of the kinetic and potential energies of individual balls (79,553 particles) are used to find the appropriate range for the number of lifters. In other words, the kinetic and potential energies of all particles within the system (inside the ball mill) are the basis for determining the appropriate number of lifters. The results suggest that for the wave liner, the appropriate range of the number of lifters is between 8 and 16, for the step, step@, and ship-lap liners; it is between 12 and 20, and for the ship-lap@ liner, it is between 8 and 20. In fact, using the data on the kinetic and potential energies of the balls inside the mill, it is possible to determine the appropriate range of the number of lifters, which is done for the first time in this study. In general, it is suggested that the data on the kinetic and potential energies of the balls can be used to determine the number of mill lifters, and unlike what has been done. So far, by other researchers, the number of mill lifters should not be determined solely by using its diameter or the dimensions of the lifters. Also the effect of mill-rotation direction on the values of kinetic and potential energies in step and ship-lap liners is investigated. It is shown that the step@ and ship-lap@ liners transfer more energy to the balls than the step and ship-lap liners, and have a suitable direction of rotation.
Original Research Paper
Rock Mechanics
Sina Alizadeh; Mohammad Reza Ghassemi; Mehran Arian; Ali Solgi; Zahra Maleki; Reza Mikaeil
Abstract
One of the most significant risks for investors in the dimension stone industry is the presence of natural discontinuities in the rock mass, which affect the quality of the extracted stone blocks. These discontinuities not only reduce extraction efficiency but also hinder the optimal utilization of the ...
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One of the most significant risks for investors in the dimension stone industry is the presence of natural discontinuities in the rock mass, which affect the quality of the extracted stone blocks. These discontinuities not only reduce extraction efficiency but also hinder the optimal utilization of the quarry. Therefore, it is essential to identify and analyze discontinuities in the rock before initiating any extraction activities and to assess the optimization of the extraction direction in dimension stone quarries. This study examines the key characteristics of discontinuities and joint sets, including their coordinates, strike, dip, spacing and aperture, in the Melika marble dimension stone quarry in Kerman. The collected data are then analyzed using 3DEC software to construct a quarry block model. Additionally, the azimuth rotation of different joint sets is investigated in four categories. The results obtained from the modeling indicate that, to achieve maximum blocking, the current extraction direction should be shifted 70° westward. This adjustment increases the number of blocks to 14,550, the average block volume to 5.5 m³, and the total volume of extracted stone to 79,918.9 m³. These changes are projected to generate approximately $3,180,000 in revenue for the quarry. The study highlights a practical optimization strategy that can significantly enhance the efficiency and profitability of dimension stone quarries by improving extraction direction based on discontinuity analysis.
Original Research Paper
Exploration
Hamid Reza Baghzendani; Hamid Aghajani; Gholam Hossein Karami
Abstract
Karsts are important sources of groundwater, and it is crucial to determine their water volume and quality. The Ravansar Karst spring in the Kermanshah province is a significant water resource with a substantial water volume in the area. The source of this spring is the carbonate rock unit from the Cretaceous ...
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Karsts are important sources of groundwater, and it is crucial to determine their water volume and quality. The Ravansar Karst spring in the Kermanshah province is a significant water resource with a substantial water volume in the area. The source of this spring is the carbonate rock unit from the Cretaceous period and is affected by tectonic changes and faulting caused by movements related to the Zagros folding. In this work, geophysical methods of microgravity, electrical resistivity, and induced polarization have been utilized to identify the extent of karst development in the limestone units. The minimum residual gravity values are associated with karstification. The field dataset comprised two electrical profiles with the dipole- dipole and pole-dipole arrays. The resistivity and gravity data were inverted using a 2D algorithm based on the least square’s technique with a smoothing constraint. According to the processing and 3D modelling of gravity data; not only cavity-shaped voids and spacious cavity chambers were identified but also sub-structures and micro-karstification in carbonate rocks were detected. The most significant finding from the field survey is the detection of low gravimetric values, indicating relatively large holes and chambers that were previously unknown and inaccessible from ground level. These findings are consistent with known collapse and sediment infill features, as seen in surface sinkholes, cavities, and karstification systems. Geophysical surveys and field surveys show that the holes and karsts in the area are related to tectonic phenomena and faulting and are conduits for transporting water to the Ravansar spring.
Original Research Paper
Rock Mechanics
Farhad Mollaei; Ali Moradzadeh; Reza Mohebian
Abstract
The important aspects of this study are to estimate the mechanical parameters of reservoir rock including Uniaxial Compressive Strength (UCS) and friction (FR) angle using well log data. The aim of this research is to estimate the UCS and FR angle (φ) using new deep learning (DL) methods including ...
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The important aspects of this study are to estimate the mechanical parameters of reservoir rock including Uniaxial Compressive Strength (UCS) and friction (FR) angle using well log data. The aim of this research is to estimate the UCS and FR angle (φ) using new deep learning (DL) methods including Multi-Layer Perceptron (MLP), Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN), and CNN + LSTM (CL) by well log and core test data of one Iranian hydrocarbon field. As only 12 UCS and 6 FR core tests of single well in this field were available, they were firstly calculated, and then generalized to other depths using two newly derived equations and relevant logs. Next, the effective input logs' data for predicting these parameters have been selected by an auto-encoder DL method, and finally, the values of UCS and φ angle were predicted by the MLP, LSTM, CNN, and CL networks. The efficiency of these four prediction models was then evaluated using a blind dataset, and a range of statistical measures applied to training, testing, and blind datasets. Results show that all four models achieve satisfactory prediction accuracy. However, the CL model outperformed the others, yielding the lowest RMSE of 1.0052 and the highest R² of 0.9983 for UCS prediction, along with an RMSE of 0.0201 and R² of 0.9917 for φ angle prediction on the blind dataset. These findings highlight the high accuracy of deep learning algorithms, particularly the CL algorithm, which demonstrates superior precision compared to the MLP method.
Original Research Paper
Exploration
Zohre Hoseinzade; Mohammad Hassan Bazoobandi
Abstract
Anomaly detection is the process of recognizing patterns in data that differ from the typical behavior. In geochemistry, this involves identifying hidden patterns and unusual components within the context of exploratory target identification. This issue is particularly significant when limited information ...
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Anomaly detection is the process of recognizing patterns in data that differ from the typical behavior. In geochemistry, this involves identifying hidden patterns and unusual components within the context of exploratory target identification. This issue is particularly significant when limited information is available about the area of interest. Therefore, employing methods that can aid in the exploration process under such conditions and with limited data is highly valuable. In this study, the Deep-Embedded Self-Organizing Map (DE-SOM), an unsupervised deep learning approach, was used to detect geochemical anomalies. The research focused on identifying multivariate geochemical anomalies in the Moalleman region. After detecting the region's geochemical anomalies, the effectiveness of the algorithm was assessed alongside two other types of SOM algorithms. For this purpose, the prediction area plot was utilized, with the intersection points for DE-SOM, Batch SOM, and SOM were determined to be 0.75, 0.67, and 0.65, respectively. The multivariate geochemical anomaly in the Moalleman area shows a good correlation with known mineral occurrences and the andesite and dacite units. Based on this, it can be stated that the DE-SOM method is a useful tool for identifying anomalies and patterns associated with mineralization.
Original Research Paper
Rock Mechanics
Shahla Miri Darmarani; Erfan Khoshzaher; Hamid Chakeri
Abstract
Shotcrete is used as a component of the support system in tunnels, and one of the methods to enhance its mechanical properties is by incorporating fibers. Fibers can significantly improve the mechanical properties of shotcrete, including compressive and tensile strength. This leads to savings in time, ...
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Shotcrete is used as a component of the support system in tunnels, and one of the methods to enhance its mechanical properties is by incorporating fibers. Fibers can significantly improve the mechanical properties of shotcrete, including compressive and tensile strength. This leads to savings in time, cost, and post-installation maintenance. In recent years, due to the environmental pollution caused by the production of synthetic fibers, there has been increasing interest in using recycled materials, mainly recycled steel fibers from worn tires. The present study is a laboratory-based research program investigating the feasibility of using recycled fibers to improve the mechanical properties of shotcrete. In this study, recycled steel fibers from worn tires and shaves of basalt stone were used to create laboratory samples. The laboratory samples included cubic (10×10 cm) and cylindrical (15×30 cm) specimens with five different mix designs: ordinary shotcrete, shotcrete containing 0.5%, 1%, 1.5%, and 2% recycled fibers. These fibers were categorized into three length groups: coarse, mixed, and fine. The laboratory tests included compressive and tensile (Brazilian) strength tests at 3-day intervals. The results of the laboratory studies indicated that recycled fibers from worn tires could significantly enhance the mechanical properties of shotcrete, with a two-fold increase in compressive strength observed when the fiber content was increased by 2%. Moreover, the inclusion of basalt stone shaves not only improved the compressive strength of the samples but also had a substantial effect on enhancing the tensile strength.
Original Research Paper
Rock Mechanics
Amin Maleki; Hamid Chakeri; Hadi Shakeri; Erfan Khoshzaher; Mohammad Darbor
Abstract
Today, due to technological advancements and increasing demand, various types of Tunnel Boring Machines (TBM) are extensively used for tunneling in both soil and rock. The mechanical excavation method has become attractive in tunnel excavation and underground spaces due to its high safety, rapid progress ...
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Today, due to technological advancements and increasing demand, various types of Tunnel Boring Machines (TBM) are extensively used for tunneling in both soil and rock. The mechanical excavation method has become attractive in tunnel excavation and underground spaces due to its high safety, rapid progress rate, low human labor requirement, and mechanization capability. The high capital costs of mechanical excavation make it essential to conduct laboratory tests, such as linear cutting tests on rocks, before selecting the machine type and adjusting the cutter head blade. The main objective of this study is to investigate the impact of rock mechanical properties on the cutting tool wear using a newly developed small-scale Linear Cutting Machine (LCM). To achieve this, laboratory linear cutting tests on rocks were conducted after constructing the small-scale linear cutting machine. To evaluate the rock cuttability and analyze the performance of disc cutters, 5 rock samples were used at three different penetration depths of 1, 1.5, and 2 mm. The results showed that the wear values of the cutting discs increased with penetration depth in all rock types, with the highest wear observed in basalt. Additionally, Brazilian tensile strength exhibited the highest correlation with cutting disc wear parameters. Furthermore, these studies indicated that determining the mineralogical and physical characteristics of rocks, such as texture, crystal size, and porosity, alongside their mechanical properties, is crucial for predicting rock wear.
Original Research Paper
Environment
Reyhaneh Khashtabeh; Morteza Akbari; Ava Heidari; Ali Asghar Najafpour; Rokhsareh Khashtabeh
Abstract
The Heavy Metal (HM) contamination in surface soils poses significant environmental and health concerns near the mining operations. This study examined the concentrations and health risks of the five HMs lead (Pb), nickel (Ni), copper (Cu), arsenic (As), and iron (Fe) in soils surrounding the Sangan ...
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The Heavy Metal (HM) contamination in surface soils poses significant environmental and health concerns near the mining operations. This study examined the concentrations and health risks of the five HMs lead (Pb), nickel (Ni), copper (Cu), arsenic (As), and iron (Fe) in soils surrounding the Sangan iron ore mines in eastern Iran. Sixty soil samples were collected at depths of 0-20 cm from sites adjacent to the mining area and one control site. The HM concentrations were compared to the global shale values. Soil contamination was quantified using the geo-accumulation index (Igeo). Health risks to the local residents were assessed using the US Environmental Protection Agency's Human Health Risk Evaluation Index. The analysis revealed that the lead concentrations near the mine exceeded the global shale standards, while the arsenic levels remained marginally below permissible limits established by global soil standards. The Igeo values indicated low to moderate the contamination levels for both Pb and As in the mining-adjacent areas. The risk assessment results showed that non-carcinogenic risk indices were within acceptable limits for both children and adults. However, arsenic posed a significant carcinogenic risk to adults through two exposure pathways: ingestion (3.36E-04) and dermal absorption (1.36E-04). These findings highlight the importance of implementing regular monitoring protocols for potentially hazardous elements in the mining region to prevent and mitigate pollution-related health risks.
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
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
Environment
Amirmahmood Razavian; Alireza Arab Amiri; Abolghasem Kamkar Rouhani; Meysam Davoodabadi Farahani
Abstract
Mining activities cause environmental pollution. Satellite remote sensing is considered an effective strategy for monitoring pollution, as other direct methods of testing soil pollution levels are often costly and face accessibility challenges in certain areas. Unlike optical sensors, radar systems can ...
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Mining activities cause environmental pollution. Satellite remote sensing is considered an effective strategy for monitoring pollution, as other direct methods of testing soil pollution levels are often costly and face accessibility challenges in certain areas. Unlike optical sensors, radar systems can capture data in all weather conditions and operate around the clock. However, radar systems do not display details and borders of zones and lack multispectral data collection capability. Consequently, combining various characteristics of optical images and radar data offers a comprehensive approach to monitoring pollution. Given these pros and cons, a combination of optical and radar images from the Sentinel satellite was employed in this study to identify surface and physical pollution areas caused by mining activities. The proposed method is a combination of Curvelet Transform, Simple Linear Iterative Clustering, Principle Components Analysis, and integration of radar and optical results using a statistical based clustering scheme, which allows the detection of contaminated zones. This research benefits from several innovative strategies, such as the separate processing and integration of optical and radar images, the simultaneous application of the curvelet transform and principle component analysis, and the utilization of two distinct clustering methods. Finally, the results obtained from radar and optical images of the Damghan region in Semnan province, Iran, on a 1 to 100.000 scale showed the proposed methodology can segment the contaminated zone caused by the eastern Alborz coal preparation plant through soil pollution modelling.
Original Research Paper
Mineral Processing
Mostafa Maleki Moghaddam; Hosein Najmaddaini; Saeid Zare; Masoud Rezaei; Mohammad Ali Motamedineya; Gholamreza Biniaz
Abstract
Abstract
The structural characteristics of mill liners, such as lifter shape and mill speed, significantly influence the grinding process. At the Sarcheshmeh slag flotation plant, the 6×6 meters SAG mill was initially equipped with 48 rows of liners, designed in a Hi-Lo configuration for the first ...
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Abstract
The structural characteristics of mill liners, such as lifter shape and mill speed, significantly influence the grinding process. At the Sarcheshmeh slag flotation plant, the 6×6 meters SAG mill was initially equipped with 48 rows of liners, designed in a Hi-Lo configuration for the first half and a Lo-Lo configuration for the second. Throughout the mill shell liner's 1700-hour operational period, monitoring identified 30 failures. Investigations revealed that defects in the liner design and improper charge motion were the main causes. This study proposes modifications and standardization of the shell liner design, tailored to the specific circuit conditions, to enhance performance and reliability. The redesign included several key changes: 1) Reducing the number of rows: The number of liner rows was decreased from 48 to 32. 2) Adjusting lifter angle: The lifter angle was increased from 23 to 30o to optimize performance. 3) Eliminating Hi-Lo design liners: The Hi-Lo design liners were changed to Hi-Hi, and 4) Reducing liner variety: The variety of liners was streamlined from 5 types to 2. The installation of the proposed liners optimized the charge trajectory for grinding, resulting in higher liner's lifetime. It extended the liner life by 30% and eliminated liner failures, reducing them from 30 to zero. The wear rate for the proposed design was 0.05 mm/hour, while the original design had a wear rate of 0.11 mm/hour. This difference corresponds to a factor of 2.3 times improvement.
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
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
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
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
Environment
Hosein Esmaeili; Mohammad Ali Afshar Kazemi; Reza Radfar; Nazanin Pilevari
Abstract
This study introduces a Hybrid Markov–Bayesian Framework for predicting and managing accident risks in high-risk industries, with a specific focus on the mining sector. The framework integrates Markov models to analyze dynamic risk transitions and Bayesian networks to infer causal relationships ...
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This study introduces a Hybrid Markov–Bayesian Framework for predicting and managing accident risks in high-risk industries, with a specific focus on the mining sector. The framework integrates Markov models to analyze dynamic risk transitions and Bayesian networks to infer causal relationships among key human and environmental factors. Drawing from a comprehensive dataset of mining operations, the framework evaluates variables such as age, experience, task type, and injury characteristics to predict and control accident risks. The results highlight the model's high performance, achieving an accuracy of 87%, precision of 85%, and an F1-score of 0.84. This innovative approach enables real-time safety interventions and proactive risk management strategies. The findings underscore the framework's potential to improve workplace safety and serve as a scalable tool for accident prevention in other high-risk industries. Future research will focus on enhancing the framework’s adaptability and incorporating additional contextual variables for broader applicability.
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
Mineral Processing
Fatemeh Kazemi; Ali akbar Abdollahzadeh
Abstract
This research work aims to explore the intricate mineralogy and texture of the tailing piles of iron ore processing plants to present a particle-based prediction for magnetite recovery. Three samples were taken from different points of tailings piles of an iron ore processing plant. Davis tube tests ...
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This research work aims to explore the intricate mineralogy and texture of the tailing piles of iron ore processing plants to present a particle-based prediction for magnetite recovery. Three samples were taken from different points of tailings piles of an iron ore processing plant. Davis tube tests were performed on each sample under various operating conditions. Process mineralogy studies were conducted to determine the mineralogy modal of the feed and product of each test. An Artificial Neural Network (ANN) model was used to make a model that related the grade and recovery of magnetite in the product to the mineralogy modal of the tailing piles. The magnetite grade and association index of feed, the magnetic intensity, and the water flow rate were the inputs to this network. The grade and magnetite recovery correlation coefficients were 0.954 and 0.86, respectively. The grade of magnetite in the feed emerged as a limiting factor on the grade and recovery of magnetite in concentrate. An increase of one unit in magnetite grade in the feed resulted in a 1.68 decrease in the recovery. The association index changes with the coefficients of -0.173 cause the changes in predicted magnetite recovery in the concentrate.
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
Exploitation
Hemant Agrawal; SIDDHARTHA ROY; Chitranjan Prasad Singh
Abstract
Deep hole blasting is essential for high-capacity excavators like draglines and shovels to achieve high production targets in opencast coal mining. However, a critical challenge associated with deep hole blasting is ground vibration, which poses risks to nearby infrastructure, including power plants, ...
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Deep hole blasting is essential for high-capacity excavators like draglines and shovels to achieve high production targets in opencast coal mining. However, a critical challenge associated with deep hole blasting is ground vibration, which poses risks to nearby infrastructure, including power plants, the Rihand Dam, and local settlements near the Khadia Opencast coal mine. This study aims to analyze the effect of blast hole diameter on peak particle velocity (PPV) to improve vibration control. Experimental investigations were conducted by executing multiple blasts using hole diameters of 159 mm, 269 mm, and 311 mm across different benches of the Khadia mine, with PPV values recorded at various scaled distances. The observed relationship between PPV and hole diameter was further validated through explicit dynamic modeling of the mine’s geology and blast conditions using ANSYS-Autodyn software. The results presents some exclusive observation that with same charge per delay, for smaller distances i.e. for less than 90 m the values of PPV is always higher in large diameter hole blasting while for distance above 500 m the PPV values are higher in smaller diameter holes blasting. The results provide a unique insight for optimizing blast parameters to minimize ground vibrations while maintaining production efficiency.
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
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
Environment
ali rasuli; 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
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
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
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
Exploration
Hassanreza Ghasemi Tabar; Sajjad Talesh Hosseini; Andisheh Alimoradi; mahdi fathi; Maryam Sahafzadeh
Abstract
Estimating ore grades during the exploration phase is often time-consuming and costly due to the need for extensive drilling. Geophysical surveys, as the last indirect exploration method before drilling, offer valuable insights into subsurface mineralization. This study introduces a novel approach for ...
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Estimating ore grades during the exploration phase is often time-consuming and costly due to the need for extensive drilling. Geophysical surveys, as the last indirect exploration method before drilling, offer valuable insights into subsurface mineralization. This study introduces a novel approach for simulating “identical twins” of borehole copper grade values using geophysical attributes derived from the geoelectrical method in the Kahang porphyry copper deposit, central Iran. By treating the simulated values as digital twins of actual borehole grades, we employed four machine learning algorithms—Linear Regression (LR), Gradient Boosting (GB), Random Forest (RF), and Support Vector Machine (SVM)—to model the complex relationships between geophysical inputs and copper grades. After data preprocessing with Principal Component Analysis (PCA), a refined dataset was used to train, test, and validate each model. The results demonstrate that GB yielded the highest predictive accuracy, generating grade estimates closely aligned with actual values. This identical twin modeling approach highlights the potential of machine learning to enhance early-stage mineral exploration by reducing dependence on costly drilling.
Original Research Paper
Mineral Processing
Meysam Nikfarjam; Ardeshir Hezarkhani; Farhad Azizafshari; Hamidreza Golchin
Abstract
Geometallurgical modeling (GM) plays a crucial role in the mining industry, enabling a comprehensive understanding of the complex relationship between geological and metallurgical factors. This study focuses on evaluating metallurgical varibles at the Sungun Copper mine in Iran by measuring and predicting ...
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Geometallurgical modeling (GM) plays a crucial role in the mining industry, enabling a comprehensive understanding of the complex relationship between geological and metallurgical factors. This study focuses on evaluating metallurgical varibles at the Sungun Copper mine in Iran by measuring and predicting process properties, including semi-autogenous power index (SPI), recovery (Re), and concentration grade. To overcome the additivity limitations of geostatistical methods, we utilized machine learning algorithms for enhanced predictive modeling, aiming to improve decision-making and optimize mining operations in geometallurgy. The research incorporates crucial data inputs such as sample coordinates, grades, lithology, mineralization zones, and alteration to assess the accuracy and reliability of different machine learning regression methods. The Relative Standard Deviation (RSD) is highlighted as a significant metric for comparing the accuracy of predicted process properties. Evaluation metrics such as Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and coefficient of determination (R2) further confirm the superiority of specific modeling methods in certain scenarios. The K-Nearest Neighbors (KNN) method exhibits superior accuracy, lower error metrics (RMSE and MAE), and a higher R2 for modeling the SPI test. For modeling Cu grade in concentrate, Support Vector Regression (SVR) proves to be effective and reliable, outperforming the Multilayer Perceptron (MLP) method. Despite MLP's high R2, its higher RSD suggests increased uncertainty and variability in the predictions. Therefore, SVR is considered more suitable for modeling Cu grade in concentrate. Findings optimize operations at Sungun Copper mine, improving decision-making, efficiency, and profitability.
Original Research Paper
Exploitation
Yaşar Ağan; Türker Hüdaverdi
Abstract
The purpose of this research work is to predict blast induced ground vibration in surface mine by using classical and machine learning algorithms. For the purpose of minimizing blast-induced ground vibration to acceptable levels, the level of vibration must be predicted. Blast-induced ground vibration ...
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The purpose of this research work is to predict blast induced ground vibration in surface mine by using classical and machine learning algorithms. For the purpose of minimizing blast-induced ground vibration to acceptable levels, the level of vibration must be predicted. Blast-induced ground vibration is defined peak particle velocity (ppv) in the ground. All data used to estimation were obtained by observing real blasting operations. After the measuring of the peak particle velocity, models of the prediction were created using independent site parameters. Most of the data is used to train the model, while remaining part is used for testing. The models were created using independent blasting parameters proportionally. Thus, more parameters are included in the models without complicating the models. A thorough validation process was conducted utilizing a diverse set of nine error criteria. Artificial intelligence models have been found to outperform traditional methods in predicting ground vibration. The mean absolute error values were found to be 1.42, 1.54, and 1.78 for ANFIS, GPR, and SVM, respectively. A similar situation is observed for other error criteria as well. ANFIS appears to be the most effective model for predicting ground vibration.
