Case Study
Exploitation
Rahul Shende; Omkar Navagire; Himanshu Kumar Jangir; Srinivasan V.; Anirban Mandal; Anjan Patel
Abstract
Due to its intricate challenges, Black cotton (BC) soil is dumped separately in mining areas, and this study focuses on a BC soil dump at an open-cast mine site. This soil, characterized by cohesion of 26-40 kPa and internal friction angle of 13°-17°, exhibits significant expansion and contraction ...
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Due to its intricate challenges, Black cotton (BC) soil is dumped separately in mining areas, and this study focuses on a BC soil dump at an open-cast mine site. This soil, characterized by cohesion of 26-40 kPa and internal friction angle of 13°-17°, exhibits significant expansion and contraction with moisture fluctuations, swelling during wet periods, and shrinking during dry spells, posing considerable challenges in mining areas. The Ministry of Environment, Forest and Climate Change (MoEFCC) has suggested constructing a 15-20m wall to protect the village on the periphery of the BC soil dump of 36m height. This study aimed to identify sustainable and economical alternative feasible remedial solutions. Field testing, including borehole investigation, was conducted to determine the stratigraphy beneath the dump. Numerical analysis using SLOPE/W software was performed for slope optimization and to evaluate remedial measures such as stone pitching and rockfill trench. The study shows that the dump can be stabilized using the design modification and possible cost-effective measures. Based on field observations, dump material testing, and numerical analysis, alternative remedial measures were proposed and implemented. The study also includes a cost-benefit analysis of the recommended remedial measures.
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.
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.
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.
Original Research Paper
Exploitation
Yasar Agan; Turker Hudaverdi
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.
Original Research Paper
Exploitation
Kamel Menacer; Abderrazak Saadoun; Abdellah Hafsaoui; Mohamed Fredj; Abdelhak Tabet; Djamel Eddine Boudjellal; Riadh Boukarm; Radouane Nakache
Abstract
Mining blasting efficiency is essential for mining operations for economic and technical reasons. Rock blasting operations should be conducted optimally to obtain a particle size distribution that optimises downstream operations, such as loading, transport, crushing, and grinding. The nature of the stemming ...
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Mining blasting efficiency is essential for mining operations for economic and technical reasons. Rock blasting operations should be conducted optimally to obtain a particle size distribution that optimises downstream operations, such as loading, transport, crushing, and grinding. The nature of the stemming material significantly impacts the degree of rock fragmentation during mining operations. Stemming refers to the material used to fill the space above explosives in a borehole, which helps confine the explosive energy and optimise rock fragmentation during detonation.This study aims to evaluate the stemming materials and their effect on the particle size distribution of blasted rocks at the Chouf Amar quarry in M'Sila, Algeria. The analyses performed in this study indicate that the blasting results obtained by the company reflect poor fragmentation quality, with a significant quantity of oversized fragments, making up 20–23% of the total pieces. To address this issue, a new operational blasting plan is proposed to enhance fragmentation quality. This plan employed three stemming materials: drill cuttings, 3/8 crushed aggregates, and sand. The test blasts were performed in a limestone quarry, and the results were evaluated using the highly reliable and widely respected image analysis software WipFrag 3.3. The results reveal that using crushed aggregates as stemming material significantly improves fragmentation quality, reducing the proportion of oversized fragments from an average of 23% (with sand stemming) to 2.6%.
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
Exploration
Mahbubeh Arabzadeh Bani asadi; Habib Ghasemi; Mehdi Rezaei-Kahkhaei; Lambrini Papadopoulou
Abstract
The lower Jurassic (180 ± 1.5 Ma) Gowd-e-Howz granitoid stock, as a part of the Sanandaj-Sirjan Metamorphic-Magmatic Zone (SSMMZ), SE Iran, intruded in the Upper Paleozoic metamorphic and Triassic igneous-sedimentary rocks. It consists of three main rock units including diorite, granodiorite and ...
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The lower Jurassic (180 ± 1.5 Ma) Gowd-e-Howz granitoid stock, as a part of the Sanandaj-Sirjan Metamorphic-Magmatic Zone (SSMMZ), SE Iran, intruded in the Upper Paleozoic metamorphic and Triassic igneous-sedimentary rocks. It consists of three main rock units including diorite, granodiorite and granite/alkali feldspar granite, which accompanied by minor amounts of gabbro. The stock is predominantly composed of medium to coarse-grained granular granitoids consisting of clinopyroxene, amphibole, biotite, plagioclase, alkali feldspar and quartz. Clinopyroxenes exhibit calcic compositions, ranging from diopside to augite and salite, while amphiboles are primarily calcic with hornblende as the dominant phase. Feldspar display compositional ranges from orthoclase and oligoclase to labradorite. Mineralogical and geochemical evidence indicates this I-type calc-alkaline granitic magma produced in an active continental margin arc setting with potential for Cu-Au mineralization. Geothermobarometry estimations based on clinopyroxene (T= 800 to 1300°C and P= ~12 to 4.5 kbar), amphiboles (T= 742 to 769°C and P=4.5- 2 kbar) and biotite (T = 589 to 875°C and P= 0.45- 2.27 kbar), offer three different magma chamber levels for magma storaging and plumbing at the lower (~45 Km), middle (~16 Km) and upper (~5 Km) continental crust in an active continental arc setting in the Late Triassic-Early Jurassic in the southern part of the SSMMZ, SE Iran.
Original Research Paper
Rock Mechanics
Mahan Amirkhani; Mojtaba Bahaaddini; Alireza Kargar; Amin Hekmatnejad
Abstract
The stability of tunnels and underground openings in jointed rock masses is significantly influenced by the development an Excavation Damage Zone (EDZ), where discontinuities alter stress distribution and the fractured propagation zone. In previous studies on EDZ, rock mass is commonly considered as ...
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The stability of tunnels and underground openings in jointed rock masses is significantly influenced by the development an Excavation Damage Zone (EDZ), where discontinuities alter stress distribution and the fractured propagation zone. In previous studies on EDZ, rock mass is commonly considered as a continuum medium, while the joint system can dictate the size of EDZ. This study aims to investigate the EDZ around a tunnel excavated in a jointed rock mass using the Discrete Fracture Network (DFN) and Discrete Element Method (DEM). Three DFN models with different fracture intensities of 0.5, 1.0, and 1.5 m2/m3 were simulated to explore the progressive failure mechanisms and damage evolution around a tunnel. The DFN models were then imported into the DEM code. The area of the plastic zone was considered a representative measure of the EDZ. The influence of joint mechanical properties, including cohesion, friction angle, normal, and shear stiffnesses, was investigated. A dimensionless sensitivity analysis was conducted to evaluate and compare the influence of each parameter. The results show that the joint friction angle is the most influential parameter in all fracture intensities. These insights provide a more precise understanding of joint behaviour and its impact on tunnel stability in different geological settings.
Original Research Paper
Exploration
Mohammad Ebdali; Ardeshir Hezarkhani; Adel Shirazy; Amin Beiranvand Pour
Abstract
This research endeavor concentrates on minerals exploration within the context of a hydrothermal polymetallic vein deposit environment. Stream sediment sampling was executed to acquire geochemical signatures pertinent to mineralization zones. The mineralization nature is classified as epithermal, predominantly ...
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This research endeavor concentrates on minerals exploration within the context of a hydrothermal polymetallic vein deposit environment. Stream sediment sampling was executed to acquire geochemical signatures pertinent to mineralization zones. The mineralization nature is classified as epithermal, predominantly involving polymetallic sulfides. The geochemical analyses yielded multi-element concentration maps, facilitating the identification of anomalies and the establishment of zoning. Although recent developments underscore the efficacy of machine learning, notably deep learning techniques, in the detection of geochemical anomalies, the majority of preceding studies were predicated on univariate statistical methodologies. To address this constraint, a multivariate approach was implemented, incorporating spatial characteristics such as shape, overlap, and zoning within anomalies and halos. Considering the limited availability of validated mineralized samples, unsupervised and semi-supervised methodologies—most notably Generative Adversarial Networks (GANs)—were employed. GANs were trained using multi-element geochemical maps, applying transfer learning to mitigate the challenges posed by restricted deposit data, thereby facilitating the delineation of prospective exploration zones. Quantitative analyses have indicated that the approach utilizing GANs attained an accuracy exceeding 92% alongside a minimal cross-entropy loss of approximately 0.07, thereby surpassing conventional methodologies in detecting of weak anomalies. The model effectively corroborated previously recognized anomalies while simultaneously detecting new prospective mineralization areas, thereby augmenting exploration opportunities. This investigation illustrates that GANs enable a more thorough utilization of geochemical datasets, integrating a wide range of variables and intricate spatial characteristics. Although GANs demonstrate superior proficiency in modeling weak anomalies, conventional techniques continue to be effective for more pronounced anomalies. The integration of both methodologies may enhance the efficiency of mineral exploration endeavors. In summary, the results emphasize the promise of GANs and sophisticated machine learning frameworks in enhancing anomaly detection and expanding mineral exploration within hydrothermal polymetallic systems.
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
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
Sina Samadi; Peyman Afzal; Mehran Arian; Ali Solgi; Zahra Maleki; Mohammad Seraj
Abstract
An important work for fractured reservoir modeling and development of oilfields is the delineation of geomechanical attributes such as permeability. The main aim of this research work is detection of permeability zones in the Asmari reservoir of Gachsaran oilfield (SW Iran) based on mud loss data. The ...
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An important work for fractured reservoir modeling and development of oilfields is the delineation of geomechanical attributes such as permeability. The main aim of this research work is detection of permeability zones in the Asmari reservoir of Gachsaran oilfield (SW Iran) based on mud loss data. The mud loss was 3D estimated by ordinary kriging method. Then, fractal number-size, concentration-volume, and concentration-distance to fault models were applied for permeability zone classification. The concentration-distance to fault fractal model shows three permeability zones, and the concentration-volume fractal modeling represents eight zones with an index multifractal behavior. Moreover, the number-size fractal analysis presented that a multifractal behavior with five societies. The correlation between the results obtained by these fractal methods reveals that the obtained zones have a proper overlap together. High value permeability zones based on the concentration-distance to fault and concentration-volume fractal models are began from 501 Barrel Per Day (BPD) mud loss, and 630 BPD obtained by the N-S modeling. Fractal modeling indicates that the permeability zones occur in the SW, NW and southern parts of the Gachsaran oilfield which can be the fractured section of the Asmari reservoir rock. Main faults from this oilfield are correlated with the permeability zones derived via fractal modeling.
