Imran Khan; Ravi Kumar Sharma
Abstract
An experimental study is carried out to improve the bearing capacity of soils by using geotextile. In the present study geotextile (tire reinforcement) is used as geotextile, whereas sand is used as a soil medium. This research work presents the results of laboratory load tests on model square footings ...
Read More
An experimental study is carried out to improve the bearing capacity of soils by using geotextile. In the present study geotextile (tire reinforcement) is used as geotextile, whereas sand is used as a soil medium. This research work presents the results of laboratory load tests on model square footings supported on reinforced sand beds. A total of twenty-seven load tests are conducted to evaluate the effects of single layer reinforcement placed below square model footings. The parameters of the testing program of the research work are the depth of reinforcement, the plan area of reinforcement, and the number of reinforcements. From the experimental data, it is indicated that there is an optimum reinforcement depth at which the bearing capacity is the highest. Also, the optimum size of reinforcement is found to be 1.5 B×1.5 B irrespective of the type of reinforcing materials used. The bearing capacity of reinforced sand is also found to increase with the number of reinforcement layer and reinforcement size when the reinforcement is placed within a certain effective zone with high relative density. The optimum placement position of geotextile is found to be 0.5B to 0.75B from the base of the footing .The tests are done at two different relative densities, i.e., 40% and 60%. The bulk unit weight of sandy soil is 14.81 KN/m³. Maximum gain in load carrying capacity is obtained when depth of reinforcement/width of footing (Dr/B) is 0.5 at relative density of 40% and 0.75 at a relative density of 60%.In addition, the data indicate that increasing reinforcement beyond a certain value would not bring about further increase in the bearing capacity of the soil.
J. Ghiasi-Freez; M. Ziaii; A. Moradzadeh
Abstract
An accurate reservoir characterization is a crucial task for the development of quantitative geological models and reservoir simulation. In the present research work, a novel view is presented on the reservoir characterization using the advantages of thin section image analysis and intelligent classification ...
Read More
An accurate reservoir characterization is a crucial task for the development of quantitative geological models and reservoir simulation. In the present research work, a novel view is presented on the reservoir characterization using the advantages of thin section image analysis and intelligent classification algorithms. The proposed methodology comprises three main steps. First, four classes of reservoir intervals are defined using a limited number of porosity and permeability values obtained from the core plugs of Kangan and Dalan formations. Then seven micro-scale features including distribution of pore types (interparticle, interaparticle, moldic, and vuggy), pore complexity, and cement distribution as well as textural characteristics are extracted from thin section images. Finally, the features extracted from each photomicrograph and its corresponding reservoir class are used as the training data for several intelligent classifiers including decision trees, discriminant analysis functions, support vector machines, K-nearest neighbor models and two ensemble algorithms, named bagging and boosting. The relationship between the micro-scale features and the reservoir classes was studied. Performance of all classifiers is evaluated using the concepts of accuracy, precision, recall, and harmonic average. The results obtained showed that the bagging decision tree delivered the best performance among the models and improved the accuracy of simple models up to 7.7% compared with the best single classifier.
Exploitation
H. Bakhshandeh Amnieh; M. Hakimiyan Bidgoli; H. Mokhtari; A. Aghajani Bazzazi
Abstract
Estimating the costs of blasting operations is an important parameter in open-pit mining. Blasting and rock fragmentation depend on two groups of variables. The first group consists of mass properties, which are uncontrollable, and the second one is the drill-and-blast design parameters, which can be ...
Read More
Estimating the costs of blasting operations is an important parameter in open-pit mining. Blasting and rock fragmentation depend on two groups of variables. The first group consists of mass properties, which are uncontrollable, and the second one is the drill-and-blast design parameters, which can be controlled and optimized. The design parameters include burden, spacing, hole length, hole diameter, sub-drilling, charge weight, charge length, stemming length, and charge density. Blasting costs vary depending on the size of these parameters. Moreover, blasting brings about some undesirable results such as air overpressure, fly rock, back-break, and ground vibration. This paper proposes a mathematical model for estimating the costs of blasting operations in the Baghak gypsum mine. The cost of blasting operations in the objective function is divided into three parts: drilling costs, costs of blasting system, and costs of blasting labours. The decision variables used to minimize the costs include burden, spacing, hole diameter, stemming length, charge density, and charge weight. Constraints of the model include the boundary and operational limitations. Air overpressure in the mine is also anticipated as one of the model constraints. The non-linear model obtained with consideration of constraints is optimized by simulated annealing (SA). After optimizing the model by SA, the best values for the decision variables are determined. The value obtained for the cost was obtained to be equal to 2259 $ per 7700 tons for the desired block, which is less than the blasting costs in the Baghak gypsum mine.
Rock Mechanics
Arun Kumar Sahoo; Debi Prasad Tripathy; Singam Jayanthu
Abstract
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 ...
Read More
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.
Muhammad Ahsan M.; T. Celik; B. Genc
Abstract
The distribution of stream sediments is usually considered as an important and very useful tool for the early-stage exploration of mineralization at the regional scale. The collection of stream samples is not only time-consuming but also very costly. However, the advancements in space remote sensing ...
Read More
The distribution of stream sediments is usually considered as an important and very useful tool for the early-stage exploration of mineralization at the regional scale. The collection of stream samples is not only time-consuming but also very costly. However, the advancements in space remote sensing has made it a suitable alternative for mapping of the geochemical elements using satellite spectral reflectance. In this research work, 407 surface stream sediment samples of the zinc (Zn) and lead (Pb) elements are collected from Central Wales. Five machine learning models, namely the Support Vector Regression (SVR), Generalized Linear Model (GLM), Deep Neural Network (DNN), Decision Tree (DT), and Random Forest (RF) regression, are applied for prediction of the Zn and Pb concentrations using the Sentinel-2 satellite multi-spectral images. The results obtained based on the 10 m spatial resolution show that Zn is best predicted with RF with significant R2 values of 0.74 (p < 0.01) and 0.7 (p < 0.01) during training and testing. However, for Pb, the best prediction is made by SVR with significant R2 values of 0.72 (p < 0.01) and 0.64 (p < 0.01) for training and testing, respectively. Overall, the performance of SVR and RF outperforms the other machine learning models with the highest testing R2 values.
Kamran Abbas; Adeel Nawazish; Navid Feroze; Nasar Male Ahmed
Abstract
In this work, an attempt is made to fit and identify the most appropriate probability distribution(s) for the analysis of seventeen rock samples including diorite, gypsum, marble, basalt, sandstone, limestone, apatite, slate, dolomite, granite-II, schist, gneiss, amphibolite, hematitle, magnetite, Shale, ...
Read More
In this work, an attempt is made to fit and identify the most appropriate probability distribution(s) for the analysis of seventeen rock samples including diorite, gypsum, marble, basalt, sandstone, limestone, apatite, slate, dolomite, granite-II, schist, gneiss, amphibolite, hematitle, magnetite, Shale, and granite-I using laser-induced breakdown spectroscopy. The graphical assessment and visualization endorse that the rock dataset series are positively skewed. Therefore, Frechet, Weibull, log-logistic, log-normal, and generalized extreme value distributions are considered as candidate distributions, and the parameters of these distributions are estimated by maximum likelihood and Bayesian estimation methods. The goodness of fit test and model selection criteria such as the Kolmogorov-Smirnov test, Akaike Information Criterion, and Bayesian Information Criterion are used to quantify the accuracy of the predicted data using theoretical probability distributions. The results show that the Frechet, Weibull, and log-logistic distributions are the best-fitted probability distribution for rock dataset. Cluster analysis is also used to classify the selected rocks that share common characteristics, and it is observed that diorite and gypsum are placed in one cluster. However, slate, dolomite, marble, basalt, sandstone, schist, granite-II, and gneiss rocks belong to different clusters. Similarly, limestone and apatite appeare in one cluster. Likewise, shale, granite-I, magnetite, amphibolite, and hematitle appeare in a different cluster. The current work demonstrate that coupling of laser-induced breakdown spectroscopy with suitable statistical tools can identify and classify the rocks very efficiently.
V. Sarfarazi; A. Tabaroei
Abstract
In this work, the effect of rock bolt angle on the shear behavior of Rock Bridges is investigated using the particle flow code in two dimensions (PFC2D) for three different Rock Bridge lengths. Firstly, the calibration of PF2D is performed to reproduce the gypsum sample. Then the numerical models with ...
Read More
In this work, the effect of rock bolt angle on the shear behavior of Rock Bridges is investigated using the particle flow code in two dimensions (PFC2D) for three different Rock Bridge lengths. Firstly, the calibration of PF2D is performed to reproduce the gypsum sample. Then the numerical models with the dimensions of 100 mm * 100 mm are prepared. The Rock Bridge is created in the middle of the model by removal of the narrow bands of discs from it. The uniaxial compressive strength of the Rock Bridge is 7.4 MPa. The Rock Bridge lengths are 30 mm, 50 mm, and 70 mm. The rock bolt is calibrated by a parallel bond. The tensile strength of the simulated rock bolt is 360 MPa.One rock bolt is implemented in the Rock Bridge. The rock bolt angles related to the horizontal axis are the changes from 0 to 75 degrees. Totally, 18 models are prepared. The shear test condition is added to the models. The normal stress is fixed at 2 MPa, and the shear load is added to the model till failure occurs. The results obtained show that in a fixed rock bolt angle, the tensile crack initiates from the joint tip and propagates parallel to the shear loading axis till coalescence to rock bolt. In a constant Rock Bridge length, the shear strength decreases with increase in the rock bolt angle. The highest shear strength occurs when the rock bolt angle is 0°.
Environment
Şener Ceryan; Pijush Samui; Osman Samed Özkan; Samet Berber; Şule Tüdeş; Hakan Elci; Nurcihan Ceryan
Abstract
Balikesir province Akcay district (Biga Peninsula, South Marmara Region, Turkey); the studied area is located on the southern branch of the North Anatolian Fault Zone, where some earthquake, 1867 Edremit (Mw =7.0), 1919 Ayvalik-Sarmisakli (Mw = 7.0), 1944 Edremit (Mw =6.4) and 1953 Yenice (Mw = 7.2) ...
Read More
Balikesir province Akcay district (Biga Peninsula, South Marmara Region, Turkey); the studied area is located on the southern branch of the North Anatolian Fault Zone, where some earthquake, 1867 Edremit (Mw =7.0), 1919 Ayvalik-Sarmisakli (Mw = 7.0), 1944 Edremit (Mw =6.4) and 1953 Yenice (Mw = 7.2) earthquakes occurred in the historical and the instrumental period. In the said area, generally, the groundwater level is high and sandy soils are widespread. In this study, therefore topography, depth of groundwater table and soil characteristics of the said area were investigated in terms of susceptibility to liquefaction. In addition, the safety factor against liquefaction (FL) for the soil layers were determined by using simple procedure based on SPT-N values. Then the spatial distributions of the safety factor at 3 m, 6 m, 9 m, 12 m, 15 m and 18 m depths were obtained. Taking into considering FL values obtained, the liquefaction potential index and the liquefaction severity index of soil profile in the location of boring were calculated, then the spatial distributions of these index were obtained. According to the maps obtained, 5.8% of the studied area has low liquefaction potential, 10.7% medium liquefaction potential, 18.3% high liquefaction potential, and 53.8% very high liquefaction potential, and 22.7% of the study area has very low liquefaction severity, 17.1% low liquefaction severity, 47.7% moderate liquefaction severity, and 1.1% high liquefaction severity and 11.4% of the studied area has none-liquefiable soil.
E. von Sperling; C.A.P. Grandchamp
Abstract
The paper presents the case study of the current formation of a Brazilian pit lake from an iron ore mining activity. The water used for the filling of the lake comes from rain, ground water and the complementary pumpage from a close river. At its final stage, which will be reached around year 2018, Lake ...
Read More
The paper presents the case study of the current formation of a Brazilian pit lake from an iron ore mining activity. The water used for the filling of the lake comes from rain, ground water and the complementary pumpage from a close river. At its final stage, which will be reached around year 2018, Lake Aguas Claras will have a surface area of 0.67 km2 and the depth of 234 m, which will make it the deepest lake in the country. The filling of the lake began in the year 2001 and a monthly monitoring programme (physical, chemical and biological characteristics) is since then in course Theanalyses show that Lake Âguas Claras presents a very good water quality (well oxygenated, low values of colour and turbidity, limited degree of mineralization, pH slightly alkaline, low nutrient concentrations, excellent bacteriological conditions), together with a remarkable shift in the dominance of phytoplanktonic groups, indicating the high instability of lakes that are undergoing a process of formation. One relevant point in the management of this valuable water resource is to create adequate conditions for the protection of the aquatic environment. Considering the very probable maintenance of these favourable characteristics in future years, the possible uses of the lake will be directed to recreation (swimming, diving, sailing and fishing), amenity value and water supply.
J. Gholamnejad; A.R. Mojahedfar
Abstract
The determination of the Ultimate Pit Limit (UPL) is the first step in the open pit mine planning process. In this stage
that parts of the mineral deposit that are economic to mine are determined. There are several mathematical, heuristic
and meta-heuristic algorithms to determine UPL. The optimization ...
Read More
The determination of the Ultimate Pit Limit (UPL) is the first step in the open pit mine planning process. In this stage
that parts of the mineral deposit that are economic to mine are determined. There are several mathematical, heuristic
and meta-heuristic algorithms to determine UPL. The optimization criterion in these algorithms is maximization of the
total profit whilst satisfying the operational requirement of safe wall slopes. In this paper the concept of largest pit with
non- negative value is suggested. A mathematical model based on integer programming is then developed to deal with
this objective. This model was applied on an iron ore deposit. Results showed that obtained pit with this objective is
larger than that of obtained by using net profit maximization and contains more ore, whilst the total net profit of
ultimate pit is not negative. This strategy can also increase the life of mine which is in accordance to the sustainable
development principals.
Z. Bahri; S.Z. Shafaei; M. Karamoozian
Abstract
Investigations were carried out on coal tailings by conventional cell and column flotation techniques. Tests were conducted to assess processing coal tailings of Alborz Markazi coal washing plant in Iran by column flotation. The effects of reagent type/dosage were investigated with conventional flotation ...
Read More
Investigations were carried out on coal tailings by conventional cell and column flotation techniques. Tests were conducted to assess processing coal tailings of Alborz Markazi coal washing plant in Iran by column flotation. The effects of reagent type/dosage were investigated with conventional flotation and their results were used in the performance of column flotation. Also the effects of the air rate, the feeding rate, the wash water rate, the frother concentration, the collector dosage were evaluated with column flotation. These coal tailings have an average of 56% ash. This paper used factorial design to optimize grade and recovery of coal tailings. The column flotation results indicated concentrate produced under optimum conditions, kerosene, 2909 g/t; superficial air velocity, 0.96 cm/s; feeding rate, 3.6 lit/min; superficial wash water velocity, 0.98 lit/min; frother dosage, 350 g/t having an ash content of 12.11% and a combustible recovery of 28.51% was obtained.
Environment
Anna Perevoshchikova; Elizaveta Malyshkina; Natalia Mitrakova; Nikita Kobelev; Larisa Rudakova
Abstract
The utilisation of potash reserves has various environmental consequences, such as the generation of substantial volumes of solid waste containing high levels of sodium chloride. The accumulation of environmental harm gives rise to an unfavourable environmental scenario in potash production areas, which ...
Read More
The utilisation of potash reserves has various environmental consequences, such as the generation of substantial volumes of solid waste containing high levels of sodium chloride. The accumulation of environmental harm gives rise to an unfavourable environmental scenario in potash production areas, which requires the investigation of waste management solutions. The predominant approach to reducing surface waste involves backfilling mined areas. In other countries, salt dump reclamation is utilised alongside backfilling. The distinctive characteristic of salt dump reclamation lies in the water-solubility and phytotoxicity of the dump rock. This research aims to evaluate the morphometric and biochemical parameters (using phytotesting) of vegetation throughout the process of salt dump reclamation using different variants. A model reclamation was carried out in a laboratory setting, where three different variants were subjected to experimentation. A reduction in the thickness of the protective clay barrier resulted in a decline in morphometric aspects of the experimental crops as well as the woody vegetation. Reducing the thickness of the protective clay barrier leads to an elevation in the redox activity of the examined crops, thus pointing towards potential environmental toxicity. Superior morphometric and biochemical parameters were noted in vegetation possessing a substantial protective covering, hinting at the feasibility of utilising insulating layers for salt dump reclamation. Phytotesting serves as an indicative approach to assessing soil toxicity and as a parameter for determining soil resilience against pollution. The findings hold potential for application in further research within the field of biological reclamation in areas with dump sites.
Ali Asghar khodaiari; A Jafarnejad
Abstract
Maximizing economic earnings is the most common goal in cut-off grade optimization of open-pit mining operations. When this is the case, the price of the product has a critical effect on optimum value of cut-off grade. This paper investigates the relationship between optimum cut-off grade and price to ...
Read More
Maximizing economic earnings is the most common goal in cut-off grade optimization of open-pit mining operations. When this is the case, the price of the product has a critical effect on optimum value of cut-off grade. This paper investigates the relationship between optimum cut-off grade and price to maximize total cash flow and net percent value (NPV) of operation. In order to visualize this relationship, two hypothetical mines were employed. To determine the optimum value of cut-off grade in different cases, two nonlinear programming models were formulated, and then, all models were solved using Solver in Excel. The results show that the optimum cut-off grade would always be a descending function of price when we intend to maximize total cash flow. On the other hand, this function may be descending or ascending when we intend to maximize NPV. This result also reveals that both maximum cash flow and maximum NPV always increase and decrease, respectively when the price of product increases or decreases.
S. Mohammadi; M. Ataei; R. Khalokakaei; E. Pourzamani
Abstract
Optimization of the exploitation operation is one of the most important issues facing the mining engineers. Since several technical and economic parameters depend on the cut-off grade, optimization of this parameter is of particular importance. The aim of this optimization is to maximize the net present ...
Read More
Optimization of the exploitation operation is one of the most important issues facing the mining engineers. Since several technical and economic parameters depend on the cut-off grade, optimization of this parameter is of particular importance. The aim of this optimization is to maximize the net present value (NPV). Since the objective function of this problem is non-linear, three methods can be used to solve it: analytical, numerical, and meta-heuristic. In this study, the Golden Section Search (GSS) method and the Imperialist Competitive Algorithm (ICA) are used to optimize the cut-off grade in mine No. 1 of the Golgohar iron mine. Then the results obtained are compared. Consecuently, the optimum cut-off grades using both methods are calculated between 40.5% to 47.5%. The NPVs obtained using the GSS method and ICA were 18487 and 18142 billion Rials, respectively. Thus the value for GSS was higher. The annual number of iterations in the GSS method was equal to 18, and that for ICA was less than 18. Also computing and programming the process of golden section search method were easier than those for ICA. Therefore, the GSS method studied in this work is of a higher priority.
A. Zarean; R. Poormirzaee
Abstract
Shear-wave velocity ( ) is an important parameter used for site characterization in geotechnical engineering. However, dispersion curve inversion is challenging for most inversion methods due to its high non-linearity and mix-determined trait. In order to overcome these problems, in this study, a joint ...
Read More
Shear-wave velocity ( ) is an important parameter used for site characterization in geotechnical engineering. However, dispersion curve inversion is challenging for most inversion methods due to its high non-linearity and mix-determined trait. In order to overcome these problems, in this study, a joint inversion strategy is proposed based on the particle swarm optimization (PSO) algorithm. The seismic data considered for designing the objects are the Rayleigh wave dispersion curve and seismic refraction travel time. For joint inversion, the objective functions are combined into a single function. The proposed algorithm is tested on two synthetic datasets, and also on an experimental dataset. The synthetic models demonstrate that the joint inversion of Rayleigh wave and travel time return a more accurate estimation of VS in comparison with the single inversion Rayleigh wave dispersion curves. To prove the applicability of the proposed method, we apply it in a sample site in the city of Tabriz located in the NW of Iran. For a real dataset, we use refraction microtremor (ReMi) as a passive method for getting the Rayleigh wave dispersion curves. Using the PSO joint inversion, a three-layer subsurface model was delineated.The results obtained for the synthetic datasets and field dataset show that the proposed joint inversion method significantly reduces the uncertainties in the inverted models, and improves the revelation of boundaries.
H. R. Nejati; Seyed A. Moosavi
Abstract
Assessment of the correlation between rock brittleness and rock fracture toughness has been the subject of extensive research works in the recent years. Unfortunately, the brittleness measurement methods have not yet been standardized, and rock fracture toughness cannot be estimated satisfactorily by ...
Read More
Assessment of the correlation between rock brittleness and rock fracture toughness has been the subject of extensive research works in the recent years. Unfortunately, the brittleness measurement methods have not yet been standardized, and rock fracture toughness cannot be estimated satisfactorily by the proposed indices. In the present study, statistical analyses are performed on some data collected from the literature to develop two equations for estimation of modes I and II fracture toughness. Then a probabilistic sensitivity analysis is performed to determine the impact of the input parameters on the output ones. Based on the results obtained for the probabilistic analysis, a new empirical brittleness index including tensile strength, uniaxial compressive strength, and elastic modulus is suggested for estimating modes I and II fracture toughness. The analyses results reveal that the proposed index is capable of estimating rock fracture toughness with more satisfactory correlation compared to the previous indices.
L. Akpan; A. Celestine Tse; F. dumbari Giadom; C. Iorfa Adamu
Abstract
In this study, the chemical composition of water and soils contiguous to two abandoned coal mines in southeastern Nigeria, was assessed to evaluate the impact of water flow from the mines ponds on the geoenvironment and potential for acid mine drainage (AMD). Parameters including the pH, anions and cations, ...
Read More
In this study, the chemical composition of water and soils contiguous to two abandoned coal mines in southeastern Nigeria, was assessed to evaluate the impact of water flow from the mines ponds on the geoenvironment and potential for acid mine drainage (AMD). Parameters including the pH, anions and cations, and the heavy metals were measured. These were used to evaluate contamination/pollution using hybrid factors including Pollution Load Index, factors, enrichment factors, pollution load index and index of geoaccumulation. The pH range of 3.4 to 5.9 classified the water as weakly to strongly acidic, typical of AMD. The SO42– ion, which indicates pollution by mine waters, showed moderate to high concentrations. Iron, zinc lead and copper were the most abundant heavy metals. Pollution Load Index values were greater than unity which show progressive deterioration in water and sediment quality. The Enrichment Factor values of up to 1 indicated enrichment through lithogenic and anthropogenic sources. The mine dumps serve as pools that can release toxic heavy metals into the water bodies by various processes of remobilization. Based on the lithology, mineralogy, chemical concentrations and environmental factors, the study has shown that there exists a potential for the generation of AMD. The heavy metals enriched mine flow, especially iron, empty into the nearby water bodies which serve as sources of municipal water supply. Consumption of untreated water over a prolonged period from these water sources may be detrimental to health. Remedial measure and continuous monitoring are recommended for good environmental stewardship.
Rock Mechanics
S. Moshrefi; K. Shahriar; A. Ramezanzadeh; K. Goshtasbi
Abstract
A rock failure criterion is very important for prediction of the ultimate strength in rock mechanics and geotechnics; it is determined for rock mechanics studies in mining, civil, and oil wellborn drilling operations. Also shales are among the most difficult to treat formations. Therefore, in this research ...
Read More
A rock failure criterion is very important for prediction of the ultimate strength in rock mechanics and geotechnics; it is determined for rock mechanics studies in mining, civil, and oil wellborn drilling operations. Also shales are among the most difficult to treat formations. Therefore, in this research work, using the artificial neural network (ANN), a model was built to predict the ultimate strength of shale, and comparison was made with support vector machine (SVM), multiple linear regression models, and the widely used conventional polyaxial failure criteria in the stability analysis of rock structures, Drucker-Prager, and Mogi-Coulomb. For building the model, the corresponding results of triaxial and polyaxial tests have been performed on shales by various researchers. They were collected from reliable published articles. The results obtained showed that a feed forward back propagation multi-layer perceptron (MLP) was used and trained using the Levenberg–Marquardt algorithm, and the 2-4-1 architecture with root-mean-square-error (RMSE) of 24.41 exhibits a better performance in predicting the ultimate strength of shale in comparison with the investigated models. Also for further validation, triaxial tests were performed on the deep shale specimens. They were prepared from the Ramshire oilfield in SW Iran. The results obtained were compared with ANN, SVM, multiple linear regression models, and the conventional failure criterion prediction. They showed that the ANN model predicted ultimate strength with a minimum error and RMSE being equal to 43.81. Then the model was used for prediction of the threshold broken pressure shale layer in the Gachsaran oilfield in Iran. For this, a vertical and horizontal stress was calculated based on a depth of shale layer. The threshold broken pressure was calculated for the beginning and ending of a shale layer to be 154.21 and 167.98 Mpa, respectively.
Rock Mechanics
Seyed S. Mousavi; M. Nikkhah; Sh. Zare
Abstract
In this work, we tried to automatically optimize the cost of the concrete segmental lining used as a support system in the case study of Mashhad Urban Railway Line 2 located in NE Iran. Two meta-heuristic optimization methods including particle swarm optimization (PSO) and imperialist competitive algorithm ...
Read More
In this work, we tried to automatically optimize the cost of the concrete segmental lining used as a support system in the case study of Mashhad Urban Railway Line 2 located in NE Iran. Two meta-heuristic optimization methods including particle swarm optimization (PSO) and imperialist competitive algorithm (ICA) were presented. The penalty function was used for unfeasible solutions, and the segmental lining structure was defined by nine design variables: the geometrical parameters of the lining cross-section, the reinforced feature parameters, and the dowel feature parameters used among the joints to connect the segment pieces. Furthermore, the design constrains were implemented in accordance with the American Concrete Institute code (ACI318M-08) and guidelines of lining design proposed by the International Tunnel Association (ITA). The objective function consisted of the total cost of structure preparation and implementation. Consequently, the optimum design of the system was analyzed using the PSO and ICA algorithms. The results obtained showed that the objective function of the support system by the PSO and ICA algorithms reduced 12.6% and 14% per meter, respectively.
Sonu Singh; Vijay Shankar; Joseph Tripura
Abstract
With an emphasis on establishing a connection between electrical and sub-surface hydro-geophysical features of soils, a critical evaluation of electrical resistivity technique applications is conducted in the current work. In order to identify diverse subsurface soil characteristics at different stratifications, ...
Read More
With an emphasis on establishing a connection between electrical and sub-surface hydro-geophysical features of soils, a critical evaluation of electrical resistivity technique applications is conducted in the current work. In order to identify diverse subsurface soil characteristics at different stratifications, the electrical resistivity approach is a widely utilized geophysical method that is extensively adopted in various Earth landforms. The assessment of sub-surface hydro-geophysical features of soils, on the other hand, offers information on the hydrogeological and geological properties including the classification of aquifer types, groundwater pollution, and seismic data. The vast majority of the information compiled in this work may help the researchers better understand some basic fundamental issues relating the hydrogeology.
M. Alipour Shahsavari; P. Afzal; A. Hekmatnejad
Abstract
The Urumieh-Dokhtar Magmatic Arc (UDMA) is recognized as an important porphyry, disseminated, vein-type and polymetallic mineralization arc. The aim of this study is to identify and subsequently determine geochemical anomalies for exploration of Pb, Zn and Cu mineralization in Mial district situated ...
Read More
The Urumieh-Dokhtar Magmatic Arc (UDMA) is recognized as an important porphyry, disseminated, vein-type and polymetallic mineralization arc. The aim of this study is to identify and subsequently determine geochemical anomalies for exploration of Pb, Zn and Cu mineralization in Mial district situated in UDMA. Factor analysis, Concentration-Number (C-N) fractal model and Local Linear Model Tree (LOLIMOT) algorithm used for this purpose. Factor analysis utilized in recognition of the correlation between elements and their classification. This classified data used for training the LOLIMOT algorithm based on relevant elements. The results of the LOLIMOT algorithm represent anomalies in areas with no lithogeochemical samples. Although, the C-N log-log plot for target elements were generated based on stream sediment and lithogeochemical samples which could be delineated mineral potential maps of the target elements. Results obtained by the LOLIMOT and fractal modeling show that the SW and the Eastern parts of the area are proper for further exploration of Cu, Pb, and Zn.
F. Doulati Ardejani; S. Maghsoudy; M. Shahhosseini; B. Jodeiri Shokri; Sh. Doulati Ardejani; F. Shafaei; F. Amirkhani Shiraz; A. Rajaee
Abstract
Considering that mining has many environmental impacts from the exploration phase to production and finally closure, it is necessary to plan the activities so that the concept of green mining is realized in its true meaning. This means that mining is carried out in order to obtain the minerals that are ...
Read More
Considering that mining has many environmental impacts from the exploration phase to production and finally closure, it is necessary to plan the activities so that the concept of green mining is realized in its true meaning. This means that mining is carried out in order to obtain the minerals that are used in various industries; however, by taking appropriate measures, the impacts of mining on the environment are reduced to a minimum level. Since there is little information about the environmental, ecological, hydrological, and hydrogeological status in most mining areas, a comprehensive study of the area's water, soil, plants, and animal species should be conducted. The existence of permanent and seasonal rivers in the vicinity of some mines, in some cases being located in protected areas of the Iranian Department of Environment, and the presence of vegetation near some mines are among the matters that cause many environmental challenges in the mining areas. For this purpose, a series of comprehensive studies are critical in the pre-mining, during mining, and closure phases of the mine life. In addition, detailed studies should be done on factories such as smelters located in the mining areas. Life cycle assessment (LCA) is widely used in order to determine the environmental status of these factories. Furthermore, the issue of process water and water recycling, as well as waste management, should be considered. Nowadays, the environmental monitoring technology is one of the widely used tools in many mines in the world. Moreover, these mining companies' green space management system should be given special attention according to the obligatory standards of the Iranian Department of Environment. In this paper, a conceptual framework for the green mining method will be introduced for the coal mines to consider the economic and social aspects, and we pay a special attention to the health, safety, and environmental requirements.
Mine Economic and Management
Aditi Nag; Smriti Mishra
Abstract
Integrating Artificial Intelligence (AI) into heritage tourism has opened new avenues for transforming visitors’ engagement with historical sites. This research paper delves into a novel paradigm, focusing on AI integration in inter- and intra-regional mining heritage site planning and design. ...
Read More
Integrating Artificial Intelligence (AI) into heritage tourism has opened new avenues for transforming visitors’ engagement with historical sites. This research paper delves into a novel paradigm, focusing on AI integration in inter- and intra-regional mining heritage site planning and design. Recognizing this context's unique challenges and opportunities, the study aims to uncover critical ideas and theories on how AI enhances visitor experience, promotes cultural preservation, sustainability, and stakeholder collaboration. Acknowledging the distinctive challenges and opportunities presented by inter- and intra-regional mining heritage contexts, this research work underscores the critical importance of striking a harmonious equilibrium between technological advancements and preserving historical and cultural legacies. Drawing from a cross-disciplinary approach, the study examines the profound implications of integrating AI into mining heritage sites' planning and design strategies. The study reviews 199 articles on AI-driven planning and design benefits, examining potential advantages. Ethical considerations, algorithmic biases, and the role of interdisciplinary research are also explored. The study highlights the intricate interplay between AI-enhanced engagement, responsible tourism practices, and the meaningful representation of local cultures. By shedding light on this uncharted territory, the research contributes to developing informed strategies that harness AI's potential to shape inter- and intra-regional mining heritage site planning and design, fostering responsible and impactful tourism experiences. By delving into this paradigm, it hopes to arm the researchers, policy-makers, practitioners, and other stakeholders with information and understanding that will help them forge a progressive and morally upright future, in which technology co-exists peacefully with practices for cultural preservation and sustainable tourism.
M Ebadi; Saeed Karimi Nasab; H Jalalifar
Abstract
rnDetermination of rock mass deformation modulus is very important in different projects, especially in civil and mining engineering works. In-situ measurements such as dilatometer, plate load and flat jack methods may be applied to determine the deformation modulus. However, these methods are very expensive ...
Read More
rnDetermination of rock mass deformation modulus is very important in different projects, especially in civil and mining engineering works. In-situ measurements such as dilatometer, plate load and flat jack methods may be applied to determine the deformation modulus. However, these methods are very expensive and time- consuming. Analytical methods are very useful approaches which can also be used to estimate rock mass deformation modulus. Using these methods, the parameters influencing the rock mass modulus can also be evaluated. Analytical methods are based on the resultant displacement of rock mass and joints which are finally used to predict the rock modulus. It should be mentioned that none of the available analytical models could present a model to consider the effect of lateral stresses on rock mass modulus calculations. Therefore, this paper tries to investigate the effect of intermediate principal stress (σ2) and minimum principal stress (σ3) on the deformation modulus of jointed rock mass.rn
H. Khoshdast; M. Mahmoodabadi
Abstract
A new method is developed for a fast identification of the stability situation of industrial processes. The proposed method includes two factor ratios of the control constants for the upper and lower control limits to process these constants. An indication ratio is then defined as the ratio of the maximum ...
Read More
A new method is developed for a fast identification of the stability situation of industrial processes. The proposed method includes two factor ratios of the control constants for the upper and lower control limits to process these constants. An indication ratio is then defined as the ratio of the maximum data range value to the difference between the maximum and average values for individual data points. It is shown that if the indication ratio comes into values between the corresponding control factor ratios, the process will be under control, and otherwise, if the indication ratio decreases to smaller than the lower control factor ratio or gets more than the upper control factor ratio, the process will be expected to be out-of-control. Validation of the method was successfully resulted using two series of quality control datasets obtained from Zarand Iron Ore Complex (Zarand, Iran) and Miduk Copper Complex (Shahr Babak, Iran). The results obtained show that the process responses predicted by the proposed method are in agreement with those indicated by the conventional chart-based method. The developed method eliminates the need for drawing the process control charts used to assess the process control level. The superiority of the proposed method over the chart-based method becomes apparent especially when a large number of control charts are necessary to be drawn and interpreted for engineering decision-making purposes.
