فهرست مطالب

Journal of Mining and Environement
Volume:15 Issue: 3, Summer 2024

  • تاریخ انتشار: 1403/03/19
  • تعداد عناوین: 20
  • Prince Ofori Amponsah, Eric Dominic Forson * Pages 791-816

    This study was set out to delineate prospective zones of gold mineralization occurrence over the Julie tenement of Northwestern Ghana using two spatial statistical techniques, namely information value (IV) and weight of evidence (WofE) models. First, 110 locations, where gold (Au) mineralization has been observed, were identified by field survey results derived from highly anomalous geo-chemical assay datasets. Of these 110 locations, 77 (representing 70% of the known locations, where gold has been observed) were randomly selected for training the aforementioned models, and the remaining 33 (analogous to 30% of the known Au occurrence) were used for validation. Secondly, eleven mineral conditioning factors (evidential layers) comprising analytic signal, reduction-to-equator (RTE), lineament density (LD), porphyry density, potassium concentration, thorium concentration, uranium concentration, potassium-thorium ratio, uranium-thorium ratio, geology, and arsenic concentration layers were sourced from geo-physical, geological, and geo-chemical datasets. Subsequently, by synthesizing these eleven evidential layers using the two spatial statistical techniques, two mineral prospectivity models were created in a geographic information system (GIS) environment. Finally, the mineral prospectivity models produced were validated using the area under the receiver operating characteristics curve (AUC). The results obtained showed that the IV model produced had a higher prediction accuracy in comparison with the mineral predictive model produced by the WofE with their AUC scores being 0.751 and 0.743, respectively.

    Keywords: Mineral Prospectivity Modeling, Information Value, Weight Of Evidence, Aeromagnetic Data, Airborne Radiometric Data
  • Kaustubh Sinha, Priyangi Sharma, Anurag Sharma, Kanwarpreet Singh *, Murtaza Hassan Pages 817-843

    In this expansive study, a thorough analysis of land subsidence in the Joshimath area has been conducted, exercising remote sensing (RS) and Geographic Information System (Civilians) tools. The exploration encompasses colourful pivotal parameters, including Annual Rainfall, Geology, Geomorphology, and Lithology, rounded by the integration of different indicators. Joshimath, a fascinating city nestled within the rugged geography of the Indian state of Uttarakhand, stands out for its unique geographical features and its vulnerability to environmental vulnerabilities. The disquisition is carried out with the backing of ArcMap software, a technical Civilians tool, while exercising data sourced from the recognized Indian Space Research Organisation (ISRO) and the National Remote seeing Centre (NRSC). This comprehensive approach aims to give inestimable perceptivity into the dynamic processes associated with land subsidence in the region, offering critical data for disaster mitigation strategies and sustainable land operation in the area. It's noteworthy that the region endured a significant case of land subsidence in late December 2022, emphasizing the punctuality and applicability of this study. This event not only emphasizes the urgency of comprehending land subsidence in Joshimath but also underscores the necessity for ongoing monitoring and mitigation sweats. The integration of these different data sources and logical ways promises to enhance the understanding of land subsidence dynamics and inform decision- makers in the pursuit of flexible and sustainable land use practices in Joshimath and other also vulnerable regions.

    Keywords: Displacement, Geospatial, LANDSAT, Lithology
  • Israel Mamani, Angelica Vivanco *, Eslainer Avendaño Pages 845-861

    In open-pit mining operations, loading and haulage activities account for a significant portion, typically between 50% and 60%, of the operational costs of the entire mining process. Tires, in turn, rank second in terms of operating costs for most mining companies. Therefore, understanding and preserving the useful life of Off-The-Road (OTR) tires is a critical factor in ensuring the profitability of a mining project. This study focuses on a specific mine to analyze the causes of operational damage in the tires of Mining Trucks (MTs) and Front-End Loaders (FELs). It aims to identify the factors leading to the premature disposal of these tires, and propose solutions to increase their useful life. The study identifies four key aspects that influence the low performance of extraction equipment, namely operator experience, environmental condition, raw materials, and equipment condition. Additionally, the study reveals that overinflation pressure significantly contributes to the premature disposal of tires, accounting for 70.5% of MT tire damage and 52.5% of FEL tire damage (primarily affecting MT rear and FEL front tires). The use of tire chains is proposed as a solution, with the potential to decrease the unit cost per labor hour by 28% for at least 50% of the tires.

    Keywords: Off-The-Road, Tires, OTR, Mining Trucks, Front End Loaders
  • Aditi Nag *, Smriti Mishra Pages 863-887

    This review paper delves into the burgeoning cultural phenomenon of dark tourism, specifically exploring its connection with Mining Heritage Towns (MHTs). The paper navigates the intricate interplay between tourism competitiveness and ethical considerations in these sites laden with historical trauma through a meticulous analysis of existing literature, case studies, and ethical frameworks. Dark tourism, characterised by exploring locations associated with tragedy, has emerged as a global trend, prompting a critical examination of its economic, cultural, and ethical dimensions within mining heritage contexts. Drawing on a wide array of sources, this comprehensive review elucidates the challenges confronting managers of heritage sites, shedding light on the complex ethical dilemmas they face. The paper comprehensively analyses the complex relationship between tourism competitiveness and ethical practices. It critically evaluates the impact of dark tourism on MHTs' economic landscape, explores its cultural implications, and delves into the ethical complexities of such visits, enriching academic discourse and offering valuable guidance for practitioners and policy-makers. The study enhances understanding of dark tourism's role in MHTs and advocates for sustainable tourism development, emphasising ethical considerations in shaping the future of these unique and historically significant sites.

    Keywords: Dark Tourism, Mining Heritage, Tourism Competitiveness, Ethical Considerations, Historical Trauma
  • Ashraf Ismael, Abdelrahem Embaby *, Faissal Ali, Hussin Farag, Sayed Gomaa, Mohamed Elwageeh, Bahaa Mousa Pages 889-905

    The mineral resource estimation process necessitates a precise prediction of the grade based on limited drilling data. Grade is crucial factor in the selection of various mining projects for investment and development. When stationary requirements are not met, geo-statistical approaches for reserve estimation are challenging to apply. Artificial Neural Networks (ANNs) are a better alternative to geo-statistical techniques since they take less processing time to create and apply. For forecasting the iron ore grade at El-Gezera region in El- Baharya Oasis, Western Desert of Egypt, a novel Artificial Neural Network (ANN) model, geo-statistical methods (Variograms and Ordinary kriging), and Triangulation Irregular Network (TIN) were employed in this study. The geo-statistical models and TIN technique revealed a distinct distribution of iron ore elements in the studied area. Initially, the tan sigmoid and logistic sigmoid functions at various numbers of neurons were compared to choose the best ANN model of one and two hidden layers using the Levenberg-Marquardt pure-linear output function. The presented ANN model estimates the iron ore as a function of the grades of Cl%, SiO2%, and MnO% with a correlation factor of 0.94. The proposed ANN model can be applied to any other dataset within the range with acceptable accuracy.

    Keywords: Artificial Neural Network, Iron Ore Grade, El-Gezera Area, Geostatistics, GIS
  • Arun Sahoo *, Debi Tripathy, Singam Jayanthu Pages 907-921

    The mining industry needs to accept new-age autonomous technologies and intelligent systems to stay up with the modernization of technology, to benefit the shake of investors and stakeholders, and most significantly, for the nation, and to protect health and safety. An essential part of geo-technical engineering is doing slope stability analysis to determine the likelihood of slope failure and how to prevent it. A reliable, cost-effective, and generally applicable technique for evaluating slope stability is urgently needed. Numerous research studies have been conducted, each employing a unique strategy. An alternate method that uses machine learning (ML) techniques is to study the relationship between stability conditions and slope characteristics by analyzing the data collected from slope monitoring and testing. This paper is an attempt by the authors to comprehensively review the literature on using the ML techniques in slope stability analysis. It was found that most researchers relied on data-driven approaches with limited input variables, and it was also verified that the ML techniques could be utilized effectively to predict slope failure analysis. SVM and RF were the most popular types of ML models being used. RMSE and AUC were used extensively in assessing the performance of the ML models.

    Keywords: Slope Stability, Factor Of Safety, Machine Learning Models, Support Vector Machine, Random Forest
  • Jitendra Pramanik *, Singam Jayanthu, Dr Abhaya Kumar Samal Pages 923-942

    The environmental conditions present in underground (UG) mines working site significantly impacts the productivity, efficiency, effectiveness as well as threatened security levels. Consequently, maintaining safety in mineral excavation process requires continuous monitoring of the intricate and perilous operating conditions within the mining work site. At this juncture of time, in this information age, when all walks of life is undergoing continuous modernization, with today's workplace being no exception, Internet of Things (IoT) technology is playing a key role in acquiring relevant information to support monitoring vital operational man and machine safety parameters such as temperature, pressure, humidity, luminance and noise levels, and miner's location in subterranean mining operations. This study has attempted to exhaustively explore state of current research on the use of IoT in underground mining applications. This paper examines the utilization of IoT applications for monitoring several environmental parameters, including obnoxious mine gases and dust concentrations, temperature, humidity, groundwater levels, and strata behaviour to facilitate ground support activities. This paper attempts exploitation of possible scopes of IoT integration from the implementation perspective to monitor and control the various aspects that contribute towards various types and incidents of mine accidents. This research elucidates the primary obstacles that impede the widespread implementation of IoT-enabled systems in underground mining applications.

    Keywords: Wireless Sensor Network, Wireless Communication, Underground Communication, Safe Mining, Ambiance Monitoring
  • Sahrul Salu *, Bima Bima Pages 943-959

    Expansion of mining pit is associated with an increased risk of slope instability and high costs. This is because changes in geometry of the mine slope significantly affect slope stability, alter the stripping ratio, and potentially threaten the continuity of mining operations. Therefore, this research work aimed to investigate the impact of changes in geometry of mining pit on slope stability to provide insight into safety, economic assurances, and ensure the sustainability of mining operations. This research work was applied by the 2D numerical modeling method using the Slide Software V. 6.0 Rocscience to analyze geometry of mining pit and impact on slope safety factors. The investigation was conducted at Pit Block A of Pt. Hikari Jeindo, managing nickel mining activities in the Langgikima District, North Konawe, Regency, Southeast Sulawesi Province, Indonesia. The results showed that the modeling method successfully showed changes in slope geometry, ensuring safe and economically viable slope safety factors. However, to obtain a more comprehensive understanding of slope stability conditions, a 3D numerical modeling method is required to capture the area affected by expansion of mining pit.

    Keywords: Slope Stability, Stripping Ratio, Construction, Pit
  • Ravi Kiranpodicheti *, Ram Chandar Karra Pages 961-976

    Opencast coal mines play a crucial role in meeting the energy demands of a country. However, the operations will result in deterioration of ambient air quality, particularly due to particulate emissions. The dispersion of particulate matter will vary based on the mining parameters and local meteorological conditions. There is a need to establish a suitable model for predicting the concentration of particulate matter on a regional basis. Though a number of dispersion models exist for prediction of dust concentration due to opencast mining, machine learning offers several advantages over traditional modeling techniques in terms of data driven insights, non-linearity, flexibility, handling complex interactions, anomaly detection, etc. An attempt has been made to assess the dispersion of particulate matter using machine learning techniques by considering the mining and meteorological parameters. Historical data comprising of mine working parameters, meteorological conditions, and particulate matter pertaining to one of the operating opencast coal mines in southern India has been utilized for the study. The data has been analyzed using different machine learning techniques like bagging, random forest, and decision tree. The performance metrics of test data are compared for different models in order to find the best fit model among the three techniques. It is found that for PM10, many of the times bagging technique gave a better accuracy, and for PM2.5, decision tree technique gave a better accuracy. Integration of mine working parameters with meteorological conditions and historical data of particulate matter in developing the model using machine learning techniques has helped in making more accurate predictions.

    Keywords: Opencast Coal Mine, PM10, PM2.5, Dispersion, Machine Learning
  • Ekin Koken * Pages 977-990

    In this study, several soft computing analyses are performed to build some predictive models to estimate the uniaxial compressive strength (UCS) of the pyroclastic rocks from central Anatolia, Turkey. For this purpose, a series of laboratory studies are conducted to reveal physico-mechanical rock properties such as dry density (ρd), effective porosity (ne), pulse wave velocity (Vp), and UCS. In soft computing analyses, ρd, ne, and Vp are adopted as the input parameters since they are practical and cost-effective non-destructive rock properties. As a result of the soft computing analyses based on the classification and regression trees (CART), multiple adaptive regression spline (MARS), adaptive neuro-fuzzy inference system (ANFIS), artificial neural networks (ANN), and gene expression programming (GEP), five robust predictive models are proposed in this study. The performance of the proposed predictive models is evaluated by some statistical indicators, and it is found that the correlation of determination (R2) value for the models varies between 0.82 – 0.88. Based on these statistical indicators, the proposed predictive models can be reliably used to estimate the UCS of the pyroclastic rocks.

    Keywords: Pyroclastic Rocks, Uniaxial Compressive Strength, Rock Property, Soft Computing
  • Irshad Khan, Afayou Afayou, Naeem Abbas *, Asghar Khan, Numan Alam, Kausar Sultan Shah Pages 991-1010

    The study utilizes the Limit Equilibrium Method (LEM) to investigate slope movements. These movements were initially generated by construction activities at the slope's base, and subsequent events were driven by seismic activities, as the study studied area lies within the Main Karakoram Thrust (MKT) and Main Mantle Thrust (MMT) zones. Soil samples, characterized by a moisture content of 13% and a dry unit weight of 18.14 kN/m³ were analyzed. The study revealed that an increase in saturation caused by rainwater infiltration, resulted in a reduction in unconfined compression strength, decreasing from 712 kPa to 349 kPa. The shear strength and deformation parameters (cohesion, angle of internal friction, and deformation modulus) were also examined with varied degrees of saturation. The results revealed a decrease in these parameters as the percentage of saturation increased from 30% to 90%. The slope stability study revealed that the Factor of Safety (FOS) reduced from 1.85 to 0.86 as the saturation of the material raised from 30% to 90%. To assess the influence of unit weight, cohesion, and angle of internal friction on the FOS, multiple cases were considered. The analysis revealed that the FOS increased with higher cohesion and angle of internal friction, while an increase in unit weight resulted in a lower factor of safety. Furthermore, stability of the slope was evaluated by modifying the slope geometry such as lowering the height. According to the GeoStudio investigation, the slope remained steady even at saturation levels exceeding 80%.

    Keywords: Landslide, Saturation, Geostudio, Cohesion, Limit Equilibrium Analysis
  • Jairo Marquina Araujo *, Marco Cotrina Teatino, José Mamani Quispe, Eduardo Noriega Vidal, Juan Vega Gonzalez, Juan Vega-Gonzalez, Juan Cruz-Galvez Pages 1011-1027

    The objective of this research work to employ machine learning techniques including Multilayer Perceptron Artificial Neural Networks (ANN-MLP), Random Forests (RFs), Extreme Gradient Boosting (XGBoost), and Support Vector Regression (SVR) to predict copper ore grades in a copper deposit located in Peru. The models were developed using 5654 composites containing available geological information (rock type), as well as the locations of the samples (east, north, and altitude) and secondary ore grade (Mo) obtained from drilling wells. The data was divided into 10% (565 composites) for testing, 10% (565 composites) for validation, and 80% (4523 composites) for training. The evaluation metrics included SSE (Sum of Squared Errors), RMSE (Root Mean Squared Error), NMSE (Normalized Mean Squared Error), and R² (Coefficient of Determination). The XGBoost model could predict the ore grade with an SSE of 15.67, RMSE = 0.17, NMSE = 0.34, and R² = 0.66, the RFs model with an SSE of 16.40, RMSE = 0.17, NMSE = 0.36, and R² = 0.65, the SVR model with an SSE of 19.94, RMSE = 0.19, NMSE = 0.43, and R² = 0.57, and the ANN-MLP model with an SSE = 21.00, RMSE = 0.19, NMSE = 0.46, and R² = 0.55. In conclusion, the XGBoost model was the most effective in predicting copper ore grades.

    Keywords: Multilayer Perceptron Artificial Neural Network, Random Forests, Extreme Gradient Boosting, Support Vector Regression
  • Morteza Niromand, Reza Mikaeil *, Mehran Advay, Masoud Zare Naghadehi Pages 1029-1049

    Slope instability can occur due to external loads such as earthquakes, explosions, and pore pressures. In addition, under natural conditions, slope instability can be caused by factors such as the erosion of some parts of the slope due to water or wind currents and the gradual rise of groundwater levels. Another factor leading to slope instability is human activities involving various types of loading and unloading on the slope. The instability of slopes may be associated with limited or large displacements, which either can cause problems or damage structures on the slope. Therefore, this phenomenon needs due care at all slope design and implementation stages. In general, slope stability is influenced by natural factors such as rock type (lithology), tectonic conditions of the area, rock mass joint conditions, and climatic conditions of the area. Furthermore, it is a function of design factors such as dip, height, explosive pattern, and explosion method. The present study offers a multi-factorial fuzzy classification system using the multi-criteria fuzzy approach to evaluate the slope stability. The evaluation is performed in five classes, namely “high stability”, “stable”, “relatively stable”, “unstable”, and “highly unstable”. Next, the viability of 28 slopes of 8 large open-pit mines in different parts of the world was evaluated. According to the fuzzy classification results, 4 and 6 slopes were evaluated in relatively stable and unstable conditions, respectively, with the other slopes classified as stable class. Afterward, the developed fuzzy classification system was assessed based on the actual behavior of the slopes. The results revealed a general large and local failure in most slopes in unstable and relatively stable conditions. Hence, a non-linear multi-factorial fuzzy classification system with good reliability can be used to evaluate the stability of the slopes.

    Keywords: Slope Stability, Fuzzy Theory, Multi-Factorial Fuzzy Classification
  • Vahab Sarfarazi, Hadi Haeri *, Mohammad Fatehi Marji, Gholamreza Saeedi Pages 1051-1070

    The mechanical behaviour of transversely isotropic elastic rocks can be numerically simulated by the discrete element method. The successive bedding layers in these rocks may have different mechanical properties. The aim of this research work is to investigate numerically the effect of anisotropy on the tensile behaviour of transversely isotropic rocks. Therefore, the numerical simulation procedure should be well-calibrated by using the conventional laboratory tests, i.e. tensile (Brazilian), uniaxial, and triaxial compression tests. In this study, two transversely isotropic layers were considered in 72 circular models. These models were prepared with the diameter of 54 mm to investigate the anisotropic effects of the bedding layers on the mechanical behaviour of brittle geo-materials. All these layers were mutually perpendicular in the simulated models, which contained three pairs of thicknesses 5 mm/10 mm, 10 mm/10 mm, and 20 mm/10 mm. Three different diameters for models were chosen, i.e. 5 cm, 10 cm, and 15 cm. These samples were subjected under two different loading rates, i.e. 0.01 mm/min and 10 mm/min. The results gained from these numerically simulated models showed that in the weak layers, the shear cracks with the inclination angles 0° to 90° were developed (considering 15° increment). Also there was no change in the number of shear cracks as the layer thickness was increased. Some tensile cracks were also induced in the intact material of the models. There was no failure in the interface plane toward the layer of higher strength in this research work. The branching was increased by increasing the loading rate. Also the model strength was decreased by increasing the model scale.

    Keywords: Bedding Layer, Intersection Plane, Conventional Strength Tests, Discrete Element, Layer Inclination Angle
  • Mohsen Khanizadeh Bahabadi *, Alireza Yarahamdi Bafghi, Mohammad Fatehi Marji, Hossein Shahami, Abolfazl Abdollahipour Pages 1071-1087

    Complexity of geomaterial’s behavior is beyond the capabilities of conventional numerical methods alone for realistically model rock structures. Coupling of numerical methods can make the numerical modeling more realistic. Discontinuous Deformation Analysis (DDA) and Displacement Discontinuous Method (DDM) are hybridized for modeling block displacement and crack propagation mechanism in a blocky rock mass. DDA is used to compute the displacements of the blocks, and DDM is used to predict the crack propagation paths due to the specified boundary conditions. The displacements obtained from DDA are converted into stress and considering Kelvin's solution of the problem the crack propagation mechanism within each block is investigated. Boundary stresses are updated due to crack propagation and new stress boundary conditions in DDA. This cycle continued until crack propagation stopped or a new block formed. Numerical solutions of the experimental rock samples including two random cracks with crack 1 fixed and crack 2 created with different angles and one crack with a slope angle of 30 degrees are compared with the existing experimental and numerical results. This comparison validates the accuracy and effectiveness of the proposed procedure because crack propagation paths predicted are in good agreement with the corresponding experimental results of rock samples.

    Keywords: Geomaterials, Discontinuous Deformation Analysis, Displacement Discontinuity Method, Crack Propagation, Coupled Numerical Methods
  • Mobin Saremi, Saeed Yousefi *, Mahyar Yousefi Pages 1089-1101

    The Mineral Prospectivity Mapping (MPM) is a procedure of integrating various exploration data to identify promising areas for follow up mineral exploration programs. MPM facilitates identification of mineral deposit prospects through reducing search spaces for the purpose of mitigating cost and time shortages. In this regard, geochemical anomaly maps constitute one of the most important evidential layers for MPM. In this research work, to produce an efficient geochemical evidential layer, the Staged Factor Analysis (SFA) method and Geochemical Mineralization Probability Index (GMPI) were performed on a dataset of 657 stream sediment samples. In addition to the mentioned maps, a layer of proximity to faults was used to efficiently identify the intended targets of copper hydrothermal deposits. The layers were then weighted and combined using logistic functions and the geometric average method. Based on the obtained results, the promising areas were found in three parts including western, central, and northern areas, which correspond to the faulted units of andesite, tuff, granite, and granodiorite intrusive masses. Finally, in order to evaluate the generated model, the prediction-area (P-A) plot was used, which shows the relative success of the generated map in specifying the desired exploration targets. The P-A plot showed that this model has a prediction rate of 64%. It seems that the proposed method by considering multi-element geochemical signatures and combination by another exploratory layer target the promising areas, those that are simultaneously present with other exploration evidence.

    Keywords: Stage Factor Analysis, Geochemical Mineralization Probability Index, Geometric Average, Mineral Prospectivity Mapping, Feizabad
  • Sadegh Amoun, Hamid Chakeri * Pages 1103-1129

    This study is an attempt to design and manufacture a tunnel boring machine (TBM) simulator to better understand the interaction between soil and cutting tools, due to the lack of an accepted method for this issue. In this paper, Sahand Soil Abrasion Test (SSAT) is introduced, which is built by the Sahand University of Technology. The experimental and real results of tool wear are presented. The results firstly demonstrate that the cutting tools wear in the coarse-grained soils can be less than in the fine-grained ones in the real conditions. However, in the soils with fine grains higher than 10%, the wear of cuttings tools increase in the laboratory condition when grading parameters increase. In soils with fine grains less than 10%, the wear of tools decreases by increasing the grading parameters. Also the results reveal that the coefficient of gradation depend on the amount of silt and clay in the soil samples. The investigations show that sorting is another good criterion for investigating the power of soil abrasively. Furthermore, it indicates that the cutting tools wear increases when the moisture content of the soil structure in the dense condition approaches the optimal moisture content. Finally, the results indicate that the wear and torque of the cutterhead could be reduced by 58% and 34%, respectively, when the excavated materials have the appropriate conditioning.

    Keywords: Wear, Cutting Tool, Abrasion, Grading, Moisture
  • Reza Khodadadi Bordboland, Asghar Azizi *, Mohammad Khani Pages 1131-1148

    The global growth of aluminum demand with the modernization of our society has led to the interest in developing alternative methods to produce aluminum from non-bauxite and low-grade resources such as shale bauxites. For such reserves, the conventional Bayer process is challenging and is not efficient to extract aluminum, and the sintering process is known to be effective. Thus, this study aimed to scrutinize the technical feasibility of alumina extraction from an Iranian low-grade (shale) bauxite ore containing 36.22% Al2O3, 22.11% SiO2, 20.42% Fe2O3, 3.33% TiO2, and 3.13% CaO. In this regard, the sintering process with lime-soda followed by alkaline leaching was adopted to extract alumina, and response surface modeling was employed to assess the important parameters such as the sintering temperature, Na2O(caustic) concentration, CaO/SiO2 molar ratio, and Na2O/Al2O3 molar ratio. The findings indicated that the extraction rate improved by increasing the sintering temperature and CaO/SiO2 ratio and decreasing the Na2O(caustic) dose and Na2O/Al2O3 ratio. It was also found that the Na2O(caustic) concentration, sintering temperature, and interactive effect of Na2O(caustic) concentration with Na2O/Al2O3 ratio had the greatest influence on the extraction efficiency. The process optimization was conducted applying the desirability function approach, and more than 71% of Al2O3 was extracted at 1150 °C sintering temperature, 2.1 CaO/SiO2 molar ratio, 0.9 Na2O/Al2O3 molar ratio and 30 g/L Na2O(caustic) dose. Ultimately, it was concluded that a lime-soda sintering process at 1150 °C followed by one-step alkaline leaching with Na2O(caustic) at 90 °C could be metallurgically efficient for treating the low-grade (shale) bauxites.

    Keywords: Low-Grade Bauxite, Aluminum Leaching, Sintering, Dissolution Yield, Optimization
  • Sajjad Khalili, Masoud Monjezi *, Hasel Amini Khoshalan, Amir Saghat Foroush Pages 1149-1160

    Determining the appropriate blasting pattern is important to prevent any damage to the tunnel perimeter in conventional tunneling by blasting operation in hard rocks. In this research work, the LS-DYNA software and numerical finite element method (FEM) are used for simulation of the blasting process in the Miyaneh-Ardabil railway tunnel. For this aim, the strong explosive model and nonlinear kinematic plastic material model are considered. Furthermore, the parameters required for the Johnson-Holmquist behavioral model are based on the Johnson-Holmquist-Ceramic material model relationships and are determined for the andesitic rock mass around studied tunnel. The model geometry is designed using AUTOCAD software and Hyper-mesh software is applied for meshing simulation. After introducing elements properties and material behavioral models and applying control and output parameters in LS-PrePost software, the modeling process is performed by LS-DYNA software. Different patterns of blastholes including 66, 23, and 19 holes, with diameters of 40 and 51 mm, and depths of 3 to 3.8 m are investigated by three-dimensional FEM. The borehole pressure caused by the ammonium nitrate-fuel oil (ANFO) detonation is considered based on the Jones-Wilkins-Lee (JWL) equation of state in the LS-DYNA software. The outer boundaries of the model are considered non-reflective to prevent the wave’s return. The results showed that LS-DYNA software can efficiently simulate the blasting process. Moreover, the post-failure rate of the blasting is reduced by more than 30% using the main charge with less explosive power and reducing the distance and diameter of contour holes.

    Keywords: Drilling, Blasting, Railway Tunnel, Finite Element Method, LS-DYNA Software, Post-Failure
  • Ali Kazempour Osalou, Sayfoddin Moosazadeh *, Ali Nouri Qarahasanlou, Mohammadreza Baghban Golpasand Pages 1161-1175

    Nowadays, tunnel excavation plays a major role in the development of countries. Due to the complex and challenging ground conditions, a comprehensive study and analysis must be done before, during, and after the excavation of tunnels. Hence, the importance of study and evaluation of ground settlement is dramatically increased since many tunnel projects are performed in urban areas, where there are plenty of constructions, buildings, and facilities. For this reason, the control and prediction of ground settlement is one of the complicated topics in the field of risk engineering. Therefore, in this paper, the proportional hazard model (PHM) is used to analyze and study the ground settlement induced by Tabriz Metro Line 2 (TML2) tunneling. The PHM method is a semi-parametric regression method that can enter environmental conditions or factors affecting settlement probability. These influential factors are used as risk factors in the analysis. After establishing a database for a case study and using a proportional hazard model for surface settlement analysis, and then by evaluating the effect of environmental conditions on the ground surface settlement, it has been found that the risk factors of grouting pressure behind the segment, the ratio of tunnel depth to groundwater level, and drained cohesion strength at a significant level of 5% have a direct effect on the probability of settlement. The results also showed that the effect of grout injection pressure on ground subsidence is more than other parameters, and with increasing injection pressure, the probability of exceeding safe subsidence values decreases. In addition, it has been found that increasing the risk factor for the ratio of tunnel depth to groundwater level reduces the probability of exceeding the safe ground settlement. Finally, increasing the number of risk factors for drained cohesion strength increases the probability of exceeding safe settlement.

    Keywords: Ground Surface Settlement, Proportional Hazard Model, Probability, Tunneling