نشریه روش های تحلیلی و عددی مهندسی معدن
پیاپی 31 (Summer 2022)

Central Iran is one of the active mining zones of Iran and has great mining potential. Large iron mines such as Choghart, Chadormalu, Sechahoon, Chahgaz, Lake Siah, Mishdavan, etc. are located in this zone. Other metals also exist in this zone Like lead and zinc in Koushk, Chahmir, and Taj-Kooh mines. Also, non-metallic deposits such as Fahraj limestone mines and building stone mines such as Bishedar marble, Taft travertine, Shirkooh granite, etc. are being extracted in this zone. Considering mineral resources and current explorations, the mines continue to develop and one of the important topics in the exploration and exploitation phase is the study of geomechanical conditions in the zone under study.
The relationship between the physical and mechanical properties of rocks makes it possible to predict the strength of the intact rock which can be used in preliminary designing of the mine at less cost and less time and just with some simple tests on exploratory boreholes and surface samples. It can also be used in mines under extraction to gain more comprehensive knowledge of the mechanical properties of mine rocks. In this study, mechanical properties such as uniaxial compressive strength, point load, indirect tensile strength (Brazilian) as well as physical properties of rock such as density, porosity, compressive wave velocity (P-wave), and electrical resistivity were measured on selected samples taken from Choghart, Sechahoon, Lakeh Siah, Koushk, Bishehdar marble, Taft travertine, Ravar sandstone and the cores of 5 geotechnical boreholes from the Anomaly VI of Central Iran Iron Ore and 4 geotechnical boreholes of Chahgaz iron ore mine. The purpose of these measurements is to investigate the relationship between mechanical and physical properties of the samples, especially electrical resistivity. In the first step, 300 surface and depth samples were collected from the mines mentioned above. After preparing the cores, effective porosity and density were recorded according to the standards (weighing the saturated and dry sample method). Also, the electrical resistivity was calculated by measuring the voltage and electrical current in the samples. The results demonstrated that there is a high correlation between P-wave velocity and electrical resistivity in all the samples. Furthermore, both parameters of P-wave velocity and electrical resistivity are dependent on porosity, and electrical resistivity like P-wave velocity has a good relationship with the mechanical properties of sedimentary rocks and volcano-sediments. Hence, the special electrical resistivity can be used as a non-destructive test to estimate the mechanical properties of rocks. Additionally, the presence of metal ores in the samples in low percentages does not cause errors in estimating physical and mechanical parameters as long as density is less than 2.8 gr/cm3. For samples with high metal content, induced polarization measurements can reduce the uncertainty of the electrical resistivity.

Structure processes have a significant role in forming sediment-hosted lead and zinc deposits. In other words, they are formed in an individual pattern located in the related rift basins. So, the first-order, the second-order, and the third-order basins have been created. Faults operate as the conduct of ore-bearing fluid and the bounding-the second-order basins. By starting rifting, hydrothermal fluid enters a sedimentary basin and as a result of the extending faults, it creates convection cells confined by faults. Therefore, structural processes are the main control factor for these deposits. Also, factors controlling the mineralization are recognized by spatial analysis methods. In this study, to identify controlling factors and present an exploratory pattern for sediment-hosted Pb-Zn deposits in the southern part of the Yazd Block, the fractal (Box- counting) method has been used. This method was utilized to estimate the distance of sub-basins which are the host of sediment-hosted Pb and Zn mineralization for the first time.  The results of this method depicted three different populations representing three factors controlling the mineralization in the studied area. These results were consistent with the basin structure which has been formed from three sub-basins. Therefore, three populations obtained from the fractal analysis showed the dimensions of three sub-basins in the studied area. The distance between mineral deposits in the third-order basin obtained about 8 Km while it is about 31 Km for the second-order basins. So, three second-order basins were recognized in which the distance between sub-basin numbers 1 and 2 is about 30 Km while it is about 60 Km between sub-basin numbers 2 and 3. Based on this exploratory model, there could be another second-order basin between sub-basin numbers 2 and 3. According to reports and literature, no other deposit has been discovered between them until now. So, based on the suggested model, there is a possibility of other deposits occurring between sub-basins numbers 2 and 3.

To increase production in coal mining panels along with the use of other equipment, the use of coal machines (shearers) is very beneficial. Predicting the shearer rate and determining the effective parameters in it plays an essential role in estimating costs. Full knowledge of the Strength and properties of coal gas and evaluation of the performance of shearer loader devices causes an increase in the speed of the loader and coal-rock production. Therefore, to achieve high production efficiency in the extraction of coal seams, it is necessary to predict the shearer rate and determine the effective parameters in it. In this paper, the shear rate prediction in relation to the Strength and gas bitumen properties of coal is investigated with the help of statistical analysis.For this purpose, 1260 types of coal cutting were done by coal machine (shearer) in E3 Tabas extraction panel No. 1 of Parvadeh coal mine. In the first stage, after harvesting and recording the shearer rate of each cut, information about degassing was done at three points along the entire length of the panel. These three points include the percentage of methane gases emitted in sensor number 88 and the input sensor (TG) and the sensor installed on the armored face conveyor (AFC). Then, using the strength properties such as coal hardness and methane degassing system, the shearer rate prediction was investigated. Using statistical studies, Shearer rate prediction was performed with three models of linear and nonlinear multivariate regression (exponential and logarithmic). To develop the predicted models, 70% of the data (882 data) were used as educational data and 30% of the data (378 data) as test data. Among the three regression models performed, the results show that the linear multivariate regression model has a more accurate prediction than the other two methods. Therefore, using the linear multivariate regression model, the amount of shearer rate in the coal mine number one of parvadeh Tabas can be predicted with good accuracy.

A pillar dimension in room and pillar mining method has been always a technical and economical issue for mining and rock mechanic engineers. The strength of the pillars is usually determined by empirical relationships, which have been determined by experience and the data collected from the coal mines of the United States, South Africa, and China, and which, except in one case, have never been considered seismic loads. This study aims to define the optimum pillar dimension based on the pillar strength derived from a new approach implemented in the numerical modeling by gradually applying an increasing load on the pillar and monitoring its displacement, using the Central section of Tabas coal mine data. The results are compared with the method of Salamon-Munro (1967) which is one of the most commonly used empirical methods. This comparison shows that the strength obtained from the numerical method for pillar widths of less than 15 m is well consistent with the empirical Salamon-Monroe method, whereas the difference between the results of the two methods increases progressively with a pillar width increment. The safety factor of the pillar is then defined by dividing the obtained pillar strength and monitoring critical stresses, under static and dynamic conditions. Tabas (1978) earthquake with 7.4 magnitudes is used for dynamic analyses. The results show that the optimal width of the pillar in the static and dynamic states is 12 and 15 meters, respectively. Moreover, curve fitting with high regression to the results obtained in both static and dynamic states, relations in terms of width to height ratio are presented for use in other areas of the mine with similar geomechanical conditions.

Modeling of mineral potentials to identify promising districts in large exploration regions for detailed exploration operations is one of the main stages of exploration. In this research, a new approach based on a convolutional neural network is proposed for geochemical numerical modeling and mineral potential exploration. In the first step, in order to create the intelligent geochemical exploration modeling, the codes of the convolutional neural network algorithm and its evaluation indicators are programmed in MATLAB environment. After preprocessing of stream sediment geochemical data, including identification of outliers, estimation of censored data, and data normalization and standardization, factor analysis is performed in order to reduce the dimension of the study space, identify the main variables that control the concentration of deposit elements, and define factors. The variables used in modeling are the result of factor analysis of stream sediment data. The average accuracy of the mentioned modeling is obtained as 96%. In the second step, using the geostatistical method (universal kriging), the average accuracy of estimation points via ArcGIS software is calculated to be 75%. At the end of this study, the performances of numerical modeling using convolutional neural network and universal kriging as well as the support vector machine and its integration with the continuous genetic algorithm, which was studied in the previous article, are compared. The evaluation results show that machine learning algorithms are more accurate in identifying promising mineral districts compared to traditional methods. It is important to note that the results of this study are in good agreement with the results of field studies and mineralized sampling.

This article is focused on the compressive strength of the concrete using silica oxide (SiO2) and carbon nanotube (CNT) nanoparticles. The concrete samples are mixed with a combination of nanoparticles with different percentages. Since the nanoparticles are not solved in water without any specific process, before producing concrete samples, nanoparticles are dispersed using a shaker, magnetic stirrer, ultrasonic devices, and finally mechanical mixer. The 15*15*15 cm cubic concrete samples for determining the compressive strength are built in three cases high SiO2, and low CNT volume percent, low SiO2 and high CNT volume percent, and pure CNT without SiO2. The 28 days cubic concrete samples are tested by ELE ADR 2000 machine. The results show that in a constant SiO2 volume percent, the compressive strength is improved by increasing the CNT volume percent, while in a constant CNT volume percent, enhancing the SiO2 volume percent has no good effect on the compressive strength. In addition, the combination of two nanoparticles cannot increase the compressive strength with respect to the sample without nanoparticles. Hence, the best result is related to the concrete containing pure CNT nanoparticles with 0.4%, which increases the compressive strength of the concrete by about 75%.