Environment
Ali Rasouli; Akbar Esmaeilzadeh; Reza Mikaeil; Solat Atalou
Abstract
Identifying joint sets is essential in engineering geology for rock mass classification and slope stability analysis in mining. Accurate clustering of joint sets based on dip and dip direction enhances the understanding of rock behavior and ensures stability in mine walls. This study presents a novel ...
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Identifying joint sets is essential in engineering geology for rock mass classification and slope stability analysis in mining. Accurate clustering of joint sets based on dip and dip direction enhances the understanding of rock behavior and ensures stability in mine walls. This study presents a novel clustering approach integrating the Harmony Search (HS) and Particle Swarm Optimization (PSO) algorithms to classify joint sets in the Sungun copper mine. Initially, joint characteristics were classified using the Fuzzy C-Means (FCM) method, with the elbow method selecting a four-class clustering solution. To optimize clustering, FCM was combined with HS and PSO, and joint data were assessed using Davies-Bouldin, Calinski–Harabasz, and Silhouette indices. The results demonstrated that the hybrid FCM-PSO method outperformed alternatives, achieving scores of 0.80, 347.48, and 0.57, respectively, indicating superior clustering performance and stability. In contrast, the FCM-HS method performed worse than FCM alone, ranking third overall. The findings confirm that FCM-PSO effectively classifies joint sets, providing reliable insights into rock mass behavior in the Sungun mine. Considering the features and advantages of the FCM-PSO method, it is concluded that the proposed approach has significant potential for effective joint classification in mining engineering. This improved clustering approach enhances geological analysis, supporting safer and more efficient mining operations.
Environment
Morteza Niromand; Reza Mikaeil; Mehran Advay; Masoud Zare Naghadehi
Abstract
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. ...
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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.
Exploitation
Shahrokh Khosravimanesh; Masoud Cheraghi Seifabad; Reza Mikaeil; Raheb Bagherpour
Abstract
Specific energy is a key indicator of drilling performance to consider in the feasibility and economic analyses of drilling projects. Any improvement in the specific energy of a drilling operation may reflect an improvement in the overall efficiency of drilling operations. This improvement can be achieved ...
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Specific energy is a key indicator of drilling performance to consider in the feasibility and economic analyses of drilling projects. Any improvement in the specific energy of a drilling operation may reflect an improvement in the overall efficiency of drilling operations. This improvement can be achieved by delivering a suitable cooling lubricant into the drilling environment. The present study examines the mechanical characteristics of the drilled rock, the physical qualities of the cooling lubricant employed, and the drilling rig operational parameters related to the drilling-specific energy (DSE). To this end, seven rock samples (granite, marble, and travertine) were drilled using water and five other fluids as the cooling lubricants. A total of 492 drilling experiments were conducted with a custom-designed and built laboratory-scale drilling rig on cuboid rock specimens. The univariate linear regression analysis of experimental results revealed a significant drop in DSE after using cooling lubricants instead of conventional cooling fluid (i.e. water). Under constant conditions in terms of mechanical properties of the rock, using Syncool with a concentration of 1:100 and soap water with a concentration of 1:120 instead of water led to 34% and 43% DSE reductions in the granite samples, 48% and 54% in the marble samples, and 41% and 50% in the travertine samples, respectively. These variations in specific energy suggest that the drilling efficiency and performance can be augmented using properly selected cooling lubricants.
Environment
Akbar Esmaeilzadeh; Korosh Shahriar; Reza Mikaeil
Abstract
The hydraulic properties of the rock masses are of great importance in analyzing the behavior and stability of the structures constructed on or in rock mass. Permeability is key parameter among other rock mass features due to its important role in rock mass overall behavior. According to aforementioned ...
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The hydraulic properties of the rock masses are of great importance in analyzing the behavior and stability of the structures constructed on or in rock mass. Permeability is key parameter among other rock mass features due to its important role in rock mass overall behavior. According to aforementioned reason, numerous efforts have been made by researchers in the field of rock mechanics for its obtaining. To access the rock masses’ permeability, in-situ test methods and simulation techniques could be used. In-situ tests like Lugeon Test are time-consuming and costly and they provide local results. Simulation base methods calculate the permeability of the model that is generated similar to the real region indeed and the developing the results to the real condition always raises substantial challenges. according to the aforementioned reason, direct acquiring of permeability with optimum cost and time which is easily generalizable to the overall of a region would be very important. In this work using crack tensor concept, permeability tensor of Lorestan’s Rudbar dam cavern is calculated efficiently by considering rock mass structural features. Resulted permeability of the power plant’s cavern was obtained equal to that seems to be acceptable compared to the measured values which is obtained 9/87×10-7 m/s.
Sh. Khosravimanesh; M. Cheraghi Seifabad; R. Mikaeil; R. Bagherpour
Abstract
In most rock drilling operations, the low rate of penetration (ROP) can be primarily attributed to the presence of the cuttings produced during drilling and the thermal stresses caused by friction at the bit-rock interface, which can be exacerbated with the increasing strength, hardness, and abrasivity ...
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In most rock drilling operations, the low rate of penetration (ROP) can be primarily attributed to the presence of the cuttings produced during drilling and the thermal stresses caused by friction at the bit-rock interface, which can be exacerbated with the increasing strength, hardness, and abrasivity of the drilled rock. In order to improve ROP, drill bit lifetime, and cutting power, it is necessary to minimize the process forces due to the mechanical bit-rock interaction and the thermal stresses generated in the drill hole. Any improvement in these areas is extremely important from both the technical and the economic perspectives. This improvement can be achieved by the use of appropriate cooling/lubricating fluids in the drilling process in order to increase ROP, reduce the temperature of the drilling environment, and create a clean drill hole free of cuttings. In this work, a series of laboratory drilling tests are performed to investigate and compare ROP in the drilling of seven samples of hard and soft rock in the presence of six different cooling-lubricating fluids. The drilling tests are performed on the cubic specimens with a laboratory-scale drilling rig at several different rotation speeds and thrust forces. The statistical analyses are performed in order to investigate the relationship between ROP and the mechanical properties of the rock, properties of the fluid, and machining parameters of the drilling rig. These analyses show that under similar conditions in terms of mechanical properties of the rock using Syncool with a concentration of 1:100 and soap water with a concentration of 1:120 instead of pure water leads to the average 31% and 37% increased ROP in granite, 36% and 43% increased ROP in marble, and 47% and 61% increased ROP in travertine, respectively. These results demonstrate the good performance of these cooling/lubricating fluids in increasing ROP.
D. Mohammadi; R. Mikaeil; J. Abdollahei Sharif
Abstract
The blasting method is one of the most important operations in most open-pit mines that has a priority over the other mechanical excavation methods due to its cost-effectiveness and flexibility in operation. However, the blasting operation, especially in surface mines, imposes some environmental problems ...
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The blasting method is one of the most important operations in most open-pit mines that has a priority over the other mechanical excavation methods due to its cost-effectiveness and flexibility in operation. However, the blasting operation, especially in surface mines, imposes some environmental problems including the ground vibration as one of the most important ones. In this work, an evaluation system is provided to study and select the best blasting pattern in order to reduce the ground vibration as one of the hazards in using the blasting method. In this work, 45 blasting patterns used for the Sungun copper mine are studied and evaluated to help determine the most suitable and optimum blasting pattern for reducing the ground vibration. Additionally, due to the lack of certainty in the nature of ground and the analyses relating to this drilling system, in the first step, a combination of the imperialist competitive algorithm and k-means algorithm is used for clustering the measured data. In the second step, one of the multi-criteria decision-making methods, namely TOPSIS (Technique for Order Performance by Similarity to Ideal Solution), is used for the final ranking. Finally, after evaluating and ranking the studied patterns, the blasting pattern No. 27 is selected. This pattern is used with the properties including a hole diameter of 16.5 cm, number of holes of 13, spacing of 4 m, burden of 3 m, and ammonium nitrate fuel oil of 1100 Kg as the most appropriate blasting pattern leading to the minimum ground vibration and reduction of damages to the environment and structures constructed around the mine.
J. Ziaei; S. Ghadernejad; A. Jafarpour; R. Mikaeil
Abstract
One of the most crucial factors involved in the optimum design and cost estimation of rock sawing process is the rock abrasivity that could result in a significant cost increase. Various methods including direct and indirect tests have been introduced in order to measure rock abrasivity. The Schimazek’s ...
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One of the most crucial factors involved in the optimum design and cost estimation of rock sawing process is the rock abrasivity that could result in a significant cost increase. Various methods including direct and indirect tests have been introduced in order to measure rock abrasivity. The Schimazek’s F-abrasiveness factor ( ) is one of the most common indices to assess rock abrasivity. is the function of three rock parameters including the Brazilian tensile strength ( ), median grain size ( ), and equivalent quartz content ( ). By considering its formulation, it has been revealed that the coefficient of each parameter is equal, which is not correct because each parameter plays a different role in the rock abrasion process. This work aims to modify the original form of by introducing three correction factors. To calculate these correction factors, an integrated method based on a combination of the statistical analysis and probabilistic simulation is applied to a dataset of 15 different andesite rocks. Based on the results obtained, the values of -0.36, 0.3, and -0.89 are suggested as the correction factors of , and , respectively. The performance of the modified Schimazek’s F-abrasiveness factor ( ) is checked not only by the wear rate of diamond wire but also by the cutting rate of the wire sawing process of Andesite rocks. The results obtained indicate that the wear rate and cutting rate of andesite rocks can be reliably predicted using . However, it should be noted that this work is a preliminary one on the limited rock types and further studies are required by incorporating different rock types.
Rock Mechanics
J. Mohammadi; M. Ataei; R. Kakaie; R. Mikaeil; S. Shaffiee Haghshenas
Abstract
Prediction of the production rate of the cutting dimensional stone process is crucial, especially when chain saw machines are used. The cutting dimensional rock process is generally a complex issue with numerous effective factors including variable and unreliable conditions of the rocks and cutting machines. ...
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Prediction of the production rate of the cutting dimensional stone process is crucial, especially when chain saw machines are used. The cutting dimensional rock process is generally a complex issue with numerous effective factors including variable and unreliable conditions of the rocks and cutting machines. The Group Method of Data Handling (GMDH) type of neural network and Radial Basis Function (RBF) neural network, as two kinds of the soft computing method, are powerful tools for identifying and assessing the unpredicted and uncertain conditions. Hence, this work aims to develop prediction models for estimating the production rate of chain saw machines using the RBF neural network and GMDH type of neural network, and then to compare the results obtained from the developed models based on the performance indices including value account for, root mean square error, and coefficient of determination. For this purpose, the parameters of 98 laboratory tests on 7 carbonate rocks are accurately investigated, and the production rate of each test is measured. Some operational characteristics of the machines, i.e. arm angle, chain speed, and machine speed, and also the three important physical and mechanical characteristics including uniaxial compressive strength, Los Angeles abrasion test, and Schmidt hammer (Sch) are considered as the input data, and another operational characteristic of the machines, i.e. production rate, is considered as the output dataset. The results obtained prove that the developed GMDH model is able to provide highly promising results in order to predict the production rate of chain saw machines based on the performance indices.
A. Aryafar; R. Mikaeil; F. Doulati Ardejani; S. Shaffiee Haghshenas; A. Jafarpour
Abstract
The process of pollutant adsorption from industrial wastewaters is a multivariate problem. This process is affected by many factors including the contact time (T), pH, adsorbent weight (m), and solution concentration (ppm). The main target of this work is to model and evaluate the process of pollutant ...
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The process of pollutant adsorption from industrial wastewaters is a multivariate problem. This process is affected by many factors including the contact time (T), pH, adsorbent weight (m), and solution concentration (ppm). The main target of this work is to model and evaluate the process of pollutant adsorption from industrial wastewaters using the non-linear multivariate regression and intelligent computation techniques. In order to achieve this goal, 54 industrial wastewater samples gathered by Institute of Color Science & Technology of Iran (ICSTI) were studied. Based on the laboratory conditions, the data was divided into 4 groups (A-1, A-2, A-3, and A-4). For each group, a non-linear regression model was made. The statistical results obtained showed that two developed equations from the A-3 and A-4 groups were the best models with R2 being 0.84 and 0.74. In these models, the contact time and solution concentration were the main effective factors influencing the adsorption process. The extracted models were validated using the t-test and F-value test. The hybrid PSO-based ANN model (particle swarm optimization and artificial neural network algorithms) was constructed for modelling the pollutant adsorption process under different laboratory conditions. Based on this hybrid modeling, the performance indices were estimated. The hybrid model results showed that the best value belonged to the data group A-4 with R2 of 0.91. Both the non-linear regression and hybrid PSO-ANN models were found to be helpful tools for modeling the process of pollutant adsorption from industrial wastewaters.
Rock Mechanics
A.R. Dormishi; M. Ataei; R. Khaloo Kakaie; R. Mikaeil; S. Shaffiee Haghshenas
Abstract
One of the most significant and effective criteria in the process of cutting dimensional rocks using the gang saw is the maximum energy consumption rate of the machine, and its accurate prediction and estimation can help designers and owners of this industry to achieve an optimal and economic process. ...
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One of the most significant and effective criteria in the process of cutting dimensional rocks using the gang saw is the maximum energy consumption rate of the machine, and its accurate prediction and estimation can help designers and owners of this industry to achieve an optimal and economic process. In the present research work, it is attempted to study and provide models for predicting the maximum energy consumption of the gang saw during the process of soft dimensional rocks with the help of an intelligent optimization model such as random non-linear techniques, i.e. the Hybrid ANFIS-DE and Hybrid ANFIS-PSO algorithms based upon 4 physical and mechanical parameters including uniaxial compressive strength, Mohs hardness, Schimazek’s F-abrasiveness factors, Young modulus, and an operational characteristic of the machine, i.e. production rate. During this research work, 120 samples are tested on 12 carbonate rocks. The maximum energy consumption of the cutting machine during this work is measured and used as a modeling output for evaluating the performance of cutting machine. Also meta-heuristic algorithms including DE and PSO algorithms are used for training the Adaptive Neural Fuzzy Inference System (ANFIS). In addition, the PSO algorithm has a higher ability in terms of model output and performance indices and has a superiority over the differential evolution algorithm. Furthermore, comparison between the measured datasets with the ANFIS-DE and ANFIS-PSO models indicate the accuracy and ability of the ANFIS-PSO model in predicting the performance of gang saw considering the machine’s properties and the cut rock.
Exploitation
B. Sohrabian; R. Mikaeil; R. Hasanpour; Y. Ozcelik
Abstract
The quality properties of andesite (Unit Volume Weight, Uniaxial Compression Strength, Los500, etc.) are required to determine the exploitable blocks and their sequence of extraction. However, the number of samples that can be taken and analyzed is restricted, and thus the quality properties should be ...
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The quality properties of andesite (Unit Volume Weight, Uniaxial Compression Strength, Los500, etc.) are required to determine the exploitable blocks and their sequence of extraction. However, the number of samples that can be taken and analyzed is restricted, and thus the quality properties should be estimated at unknown locations. Cokriging has been traditionally used in the estimation of spatially cross-correlated variables. However, it can face unsolvable matrices in its algorithm. An alternative to cokriging is to transform variables into spatially orthogonal factors, and then to apply kriging to them. Independent Component Analysis (ICA) is one of the methods that can be used to generate these factors. However, ICA is applicable to zero lag distance so that using methods with distance parameter in their algorithms would be advantageous. In this work, Minimum Spatial Cross-correlation (MSC) was applied to six mechanical properties of Cubuk andesite quarry located in Ankara, Turkey, in order to transform them into approximately orthogonal factors at several lag distances. The factors were estimated at 1544 (5 m × 5 m) regular grid points using the kriging method, and the results were back-transformed into the original data space. The efficiency of the MSC-kriging was compared with Independent Component kriging (IC-kriging) and cokriging through cross-validation test. All methods were unbiased but the MSC-kriging outperformed the IC-kriging and cokriging because of having the lowest mean errors and the highest correlation coefficients between the estimated and the observed values. The estimation results were used to determine the most profitable blocks and the optimum direction of extraction.
R. Mikaeil; M. Abdollahi Kamran; G. Sadegheslam; M. Ataei
Abstract
Predicting the sawability of the dimension stone is one of the most important factors involved in production planning. Moreover, this factor can be used as an important criterion in the cost estimation and planning of the stone plants. The main purpose for carrying out this work was to rank the sawability ...
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Predicting the sawability of the dimension stone is one of the most important factors involved in production planning. Moreover, this factor can be used as an important criterion in the cost estimation and planning of the stone plants. The main purpose for carrying out this work was to rank the sawability of the dimension stone using the PROMETHEE method. In this research work, four important physical and mechanical properties of rocks including the uniaxial compressive strength, Schmiazek F-abrasivity, mohs hardness, and Young's modulus were evaluated as the criteria. During the research process, two groups of dimension stones were selected and analyzed. The rock samples were collected from a number of Iranian factories for the laboratory tests. The production rate of each sawn stone was selected to verify the proposed sawability ranking method. The results obtained showed that the new ranking method can be reliably used for evaluating the sawability of the dimension stone at any stone factory with different rocks only by the physical and mechanical properties testing.
