International Journal of Mining & Geo-Engineering
Volume:55 Issue: 1, Winter-Spring 2021

One of the significant tasks in undercut slopes is determining the maximum stable undercut span. According to the arching effect theory, undercut excavations cause the weight of the slope to be transmitted to the adjacent stable regions of the slope, which will increase the stability of the slope. In this research, determining the maximum width of undercut slopes was examined through numerical modeling in the FLAC3D software. For this purpose, a series of undercut slope numerical models, with various slope angles, horizontal acceleration coefficients, and counterweight balance widths was conducted, and the results were validated using the corresponding experimental test results. The effect of each parameter on the maximum stable undercut span was investigated with an artificial neural network, where a multi-layer perceptron (MLP) model was performed. The results showed good accuracy of the proposed MLP model in the prediction of the maximum stable undercut span. In addition, a sensitivity analysis demonstrated that the dip angle and horizontal acceleration coefficient were the most and least effective input variables on the maximum stable undercut span, respectively.

Sandy alluvial soils contaminated with crude oil were investigated with a view to understanding the effects of crude oil contamination on their engineering properties. Bulk samples of alluvial soils compacted in layers were admixed thoroughly with 10% by volume of the contaminant and were cured for 63 days under room temperature in the laboratory and outside in the open to simulate field conditions. Mineralogical and chemical compositions of soils were obtained using X-ray diffraction and X-ray fluorescence analyses, and specific gravity, hydraulic conductivity, and compaction tests were conducted on the soils before and after contamination. Results show that the soil is silica-rich with SiO2 content of 96.24g/g. This is corroborated by the high quartz content (96.62%) observed from the mineralogical composition with minor amounts of kaolinite (6.04%), and trace amounts of haematite (0.02%).  The addition of crude oil resulted in an increase in maximum dry density (MDD) with a corresponding decrease in hydraulic conductivity, optimum moisture content (OMC), and specific gravity for both laboratory and outside cured samples. Hence, crude oil contamination can be said to modify the engineering properties of sandy soils, and the environment of samples’ emplacement also contributed to the alteration pattern observed.

A novel index is presented in this paper to evaluate sustainable development in underground coal mining. Eleven parameters were chosen as impacting factors that define three aspects of sustainable development, including environmental, economic, and social. Fuzzy Delphi Analytical Hierarchy Process (FDAHP) was used to develop a new rating system in the form of a classification system. Subsequently, a sustainable development index (SDi) was defined as a simple summation of ratings for all parameters to classify the sustainability level of underground coal mining qualitatively. Applicability of the new index was examined through applying it to a case study, and the results were compared with a benchmark model. The results indicate that SDi possesses a higher performance in sustainable development evaluation in the actual case when compared to common models. This performance is because it is developed for underground coal mining, especially in a scientific manner that considers three aspects of sustainable development together.

This work presents a hybrid-based clustering approach for mineral potential mapping (MPM) of porphyry-type Cu mineralization at Kerman province in the SE of Iran. Whereby a multidisciplinary geospatial data set was processed and integrated in the Chahargonbad district. Data-driven prediction-area (P-A) plots were drawn for each evidence layer derived from geological, geochemical, geophysical and satellite imagery data. The P-A plots provide insight into the weight of evidence for synthesizing all geospatial layers. Out of many knowledge-driven methods which biasing from experts' opinions, index overlay and fuzzy operators were employed to find out an optimum Cu favorability map through calculating an efficiency index representing the performance of each MPM. A concentration-area (C-A) fractal model was implemented to separate the mineral favorability map into some populations to ensure correct determining the cluster numbers. Clusters number is a prerequisite which must be defined correctly to increase the performance of clustering analysis for generating reliable results in MPM. Such an appropriate number of clusters can be incorporated in running three prevalent groups of clustering methodologies as data-driven approaches in MPM. They are self-organizing map, fuzzy c-means, and k-means algorithms. One of the reasons for this tendency to consider a hybrid-based method is that it overcomes the shortcomings of the both methods (bias of experts’ opinions and unknown clusters number) in mineral favorability mapping. The unknown number of clusters was determined through a knowledge-driven method, and then it was passed to an unsupervised data-driven method, i.e. clustering algorithm. This hybrid method produces synthesized maps in close association with known porphyry-Cu mineralization in the Chahargonbad area.

Our recent studies revealed that the ground sulphide minerals in contact with water generate H2O2 but its effect on the oxidation of pulp components and hence in deteriorating the concentrate grade and recovery in flotation has not been explored yet. The use of Na2S reductant at the grinding stage is thought to control the deleterious effects of H2O2 in the pulp liquid. Therefore, the effect of Na2S addition during grinding stage on the formation of H2O2 and its influence on sulphide complex ore flotation was investigated. The results showed that the presence of Na2S increases the formation of H2O2 but decreases the dissolved oxygen. An increase in Na2S dosage in grinding, the Pb grade and recovery in Cu-Pb concentrate is decreased while pyrite is depressed marginally better. These changes in flotation response of sulphides have been discussed and explained with the formation of H2O2 quantitatively.

The main objective of this study is to estimate the amount of groundwater inflow into Dorud-Khorramabad railway tunnel. To this end, in the first place, existing approaches of predicting groundwater inflow into tunnel was reviewed. According to the literature, up to now, a wide range of approaches have been proposed in order to predict the groundwater inflow into tunnel which can be classified into three distinct groups including, analytical solutions, empirical equations, and numerical modeling. Analytical solutions and empirical equations are mainly developed based on the given hypotheses and specific data sets, respectively, and should be applied in similar conditions. On the other hand, results obtained from numerical modeling are generally dependent on a wide range of parameters. Literature review revealed that one of the most effective parameters on the numerical modeling results is model extent, which controls not only final results but also numerical runtime. Hence, a sensitivity analysis is performed in order to investigate the effect of model extent on numerical results. The results demonstrated that increasing model extent decreases the groundwater inflow rate, and for a large model extent (greater than 1000), the amount of groundwater inflow tends to a constant value. In the second part, analytical solutions and finite element numerical modeling are applied for estimating the amount of groundwater inflow into Dorud–Khorramabad railway tunnel. The results indicate that the groundwater inflow into the tunnel, based on analytical methods, gives higher values than the numerical modeling. Assumptions and simplifications may justify this difference in analytical methods, accordingly, it can be inferred that if an appropriate model extent selected, the results of the numerical model based on the fact in the project can be more reliable.

In this paper, the flashlight (FL) algorithm, which is categorized as a heuristic method, has been suggested to determine the ultimate pit limit (UPL). In order to apply the suggested algorithm and other common algorithms, such as the dynamic programming, the Korobov, and the floating cone, and to validate the capability of the proposed method, the ultimate pit limit was determined in a cross-section of the Korkora reserve, which is located in Kurdistan province, northwestern of Iran and consists of 3080 blocks. The comparison of the FL algorithm and other methods revealed that same as high accuracy dynamic programming methods, the proposed algorithm could find the optimum value, while the Korobov and the floating cone algorithms failed to determine the optimum limit.

Identification and delineation of different zones in oil fields are among the fundamental tasks in petroleum explorations. Fractal methods are useful tools for such purposes. The aim of this paper is to conduct a comparative study of Concentration-Area (C-A) and Number-Size (N-S) fractal models to separate effective porous and permeable zones based on core logging samples collected from one of the oilfields in southern Iran. However, permeability and porosity threshold values were calculated based on the C-A and N-S log-log plots. A comparison between the C-A and N-S fractal results showed that the C-A method is more compatible with reality, and it is capable of separating permeable and porous zones in this oilfield.

This study investigates the relationship between slake durability indices and geomechanical characteristics of five types of carbonate rocks situated in the west of Iran along the Doruod-Khorramabad highway. In this study, five types of limestone rocks were selected, including grey limestone (A), marly limestone (B, C, D), and sandy limestone (E). The geomechanical characteristics of the studied limestones were calculated based on the ISRM (1981) standard stimulations. Statistical approaches were executed to find the most influential geomechanical characteristics on slake durability indices and to find an appropriate slake durability cycle for interpreting rock behaviors. According to the simple regression analysis, the first and fourth cycles of slake durability can provide adequately good information for initial engineering/design works. Also, the correlation coefficients demonstrated nearly constant change after the fourth cycle. Geomechanical parameters, like Schmidt hammer and dry density, showed the highest correlation with the fourth slake durability cycle (R =0.98). On the other hand, uniaxial compressive strength revealed a poor correlation (R = 0.49) with this cycle. Apart from estimating the 4th durability cycle from geomechanical properties, it is possible to calculate the second to fourth cycles of slake durability using the results of the first durability cycle (R = 0.99–0.94). Consequently, a multivariate equation was developed based on water absorption, Schmidt hammer, effective porosity, and modulus of elasticity with R2=0.89 using the best subset regression method.

Thorough knowledge of physicomechanical properties of rocks is crucial during the primary and secondary stages of designing a rock engineering project. Laboratory examination of these properties requires high-quality rock specimens. However, preparing such high accuracy samples is a difficult, expensive, and time-consuming task, especially in weak and fractured rocks. Hence, indirect approaches seem an attractive research area for determining these properties. The main object of this study is to develop some empirical relations to determine different physical and mechanical properties of sedimentary and metamorphic rocks based on the shear wave velocity index. To do that, several schist, phyllite, and sandstone core samples were collected from the drilled boreholes in the Marivan Azad dam in western Iran. Then, the shear wave velocity and some physical and mechanical properties of rocks were measured in dry and saturated conditions. Subsequently, statistical analyses were conducted to develop shear wave velocity-based equations to determine different rock properties, including uniaxial compressive strength, modulus of elasticity, porosity, Poisson’s ratio, slake durability index, density, and water absorption. An equation with the maximum correlation coefficient was proposed as the optimum equation to determine each of the above rock properties. Finally, the results of the proposed empirical equations were compared with those of laboratory measurements. This comparison proved the proposed equations to have high accuracy for determining the physicomechanical properties of rocks and can be used in practical projects with similar geological conditions to save time and money.

In geophysical exploration, inversion is carried out on the observed data to generate a geophysical model, approximating the subsurface geological struc ture. In the interpretation of magnetic data, the subsurface model parameters are found by a proper inversion scheme. Hence, it will be possible to obtain the entire parameters of any features (e.g. Dike) including depth, width, and location. In this paper, theoretical and field studies were carried out to interpret the total components of magnetic anomalies of dikes at the finite depth. Moreover, a least-squares approach was used for depth determination using anomalous magnetic data. Potential field data inversion can be achieved through many optimization techniques. This study, however, it is attempted to develop an efficient two-dimensional (2D) inversion algorithm based on the Ridge Regression routine. The developed method was programmed using Matlab software and applied to three sets of synthetic magnetic data containing different percent of random noise to find out how good the results are. It was found that the proposed 2D inversion method can produce an accurate subsurface model that precisely explains the synthetic data in each case of data inversion. Finally, the method was applied to the real total magnetic field (TMF) data of Moghan Sedimentary basin. In that case, the estimated sedimentary basement depths were found to be in good agreement with that of the seismic data acquired before.

Detection of dispersed and blind mineral deposits is an important aim in the mineral exploration. Detailed exploratory operations such as drilled boreholes which are performed for exploration of mineral deposits in the depth caused high cost and risk. In this research, a new scenario based on spectral analysis of geochemical data has been utilized for prediction of mineralized zones in the depth without any additional cost. The variations of mineralized elements from the surface to the depth are predicted and delineated by using this approach based on surface geochemical data. This proposed approach is the state-of the-art application of two-dimensional Fourier transformation (2DFT) for geochemical image processing. This approach which is named frequency coefficient method (FCM) has been defined based on the behavior of elements in the frequency domain. In this study, the FCM shows two Pb and Zn mineralized zones at the surface and moderate depth and a non-mineralized zone at the profound depth in Chichakloo Pb–Zn mineralization. Finally, the results of FCM have been validated and confirmed by the results of drilled boreholes and geophysical surveys.