masoud zare naghadehi

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.

More than 21 Mt of coal wastes is being produced by Anjir Tangeh coal washing plant during 22 years. Wastes have been deposited in the area without considering of environmental regulations in such an unspoiled area. Moreover, the pervious researches revealed that the pile has a potential of acid mine drainage generation. Therefore, it is essential to present a model for investigating risk of acid mine drainage generation in this pile. This paper presents a probabilistic method to prediction of acid mine drainage is being generated within Anjir Tangeh coal wastes pile. After building a database, pyrite remaining fraction and pH were considered as output data while depth of the wastes, bicarbonate concentration and oxygen fraction were input parameters. Then, the best probability distribution functions were presented on each of input parameters applying Monte Carlo simulation. Also, the best linear statistical relationships between input data and output data were presented. Afterward, the best probability distribution functions of input parameters were inserted to the relationships to find the probability distribution functions of output data. Eventually, the results revealed that the remaining pyrite fraction and pH values were lower than 0.894 and 4.6 at a 5% probability level, respectively.

From 1960s several attempts have been made to measuring the rock brittleness index BI. Schwartz (1964) using results of a series of triaxial tests on rock samples, stated that the rock’s behavior from frangibility to ductility happens in 4.3 ratios of principal stresses. Altindag (2002; 2003) introduced a new method for prediction of the BI by the division of the uniaxial compressive strength (UCS) of the rock to Brazilian tensile strength (BTS). In the late 1960s punch penetration test (PPT) introduced by Handewith (1971) to measure some physical properties of rock sample related to hardness and toughness of rock. Yagiz (2006) stated that the PPT’s results for measuring the BI have a very high correlation with TBM penetration rate. Although the PPT has very delightful results, application of this test is very expensive and needs much time as well.
Since 2002, researchers have made some efforts to predict the BI results acquired by PPT. Yagiz (2009) using 48 sets of 30 different tunnel rock’s PPT test data, introduced a new linear multivariable equation for prediction of the BI. In that equation, 3 major rock’s physical properties as UCS, BTS and unit weight were used. Also, Yagiz (2010) introduced a new nonlinear multivariable equation and improved the accuracy of prediction from R2=0.88 to R2=0.89. As well Khandewal et al (2016) developed a new equation using Genetic Programming (GP) based on Yagiz (2009) data with R2=0.90, but none of these equations include the type of rock as a major factor that influences the BI. In the present study, the BI will be investigated by considering the rock type (or texture) as an important parameter, and a new nonlinear multivariable equation will be introduced. As well using a classification and regression tree (CART) a new attempt will be made to predict the PPT’s result for the BI.

The selection of the best subsurface exploration methods corresponding to geotechnical, topographical and economic circumstances of the project is one of the most effective factors in the success of a tunneling project. On the other hand, the development of a decision model and consequently choosing the most suitable alternatives are complex tasks. Therefore, prioritizing and selecting the best subsurface exploration methods, as the main aim of this study, can reduce the economic and social costs associated with the execution of a tunneling project. For this purpose, ten experts from tunneling community have been asked to weigh the chosen criteria of the problem in this research. A methodology utilized in this study is the Analytical Hierarchy Process (AHP) which proved useful in engineering decision-making problems. The other method is TOPSIS, one that has continuously been used in decision making in the recent decades. These two techniques have been combined and utilized in this work to rank the aforementioned exploration methods.
Material and The study area is located about 109 km far from Shahrekord city amid the Zagros mountains. The Sabzkooh tunnel development plan has been under evaluation in the time that this research was being done. The geology of the area majorly encompasses sedimentary rocks which have been outcropped as folds, faults, and fractured and altered zones. However, the variety of the lithological units in the tunnel route is high, and units of limestone and shales also exist over the path.
In the first step, geological and topographic maps were produced for the study area, and general information from the tunnel path was collected and examined. Suitable exploration methods were evaluated, and six main methods were chosen to be considered as the alternatives of the study including (a) discontinuities study, (b) Lugeon tests, (c) RQD, (d) Geo-electric, (e) Gravimetry, and (f) Seismic methods. Moreover, nine criteria ranging from “volume of the available information” and “environmental impact” to “cost” and “accuracy” of the employed methods were taken into account. A pairwise comparison matrix was then developed, and the experts were asked to fill it out. The importance of each criterion was then simply calculated through this matrix. Alternatives pairwise comparison matrices were also filled out and, in this manner, the alternatives could be ranked using the AHP technique. Next, the TOPSIS technique was employed for the same purpose using a rather different process.
Both AHP and TOPSIS techniques show very close results for ranking of the alternatives in this study. They rank the three Seismic, Geo-electric and Discontinuities studies methods as the best ones for the considered tunneling project. The only difference between these two techniques is how they determine the worst method. The AHP ranks the Lugeon as the last rank among the six methods whereas the TOPSIS determines the RQD as the least reliable method of exploration for the Sabzkooh tunnel project.
The prioritization and the subsequent selection of the most reliable exploration methods for an underground excavation project is a crucial task amid technical decisions. In this research, two major multi-criteria decision-making methods including AHP and TOPSIS were considered and applied for the Sabzkooh tunnel in Iran. The results indicate a high agreement between the two methodologies even though these two approaches decide differently on the least reliable methods to be applied.

Summary In this research, the Block Economic Value (BEV) and project NPV in an open-pit mine has been analyzed probabilistically by using the Monte Carlo Simulation (MCS) method to deal with the uncertainties in the nature of the most important economic parameters including metal price and operational costs. The results showed that the outcomes are closer to the reality by this method compared to the results of the classic methods.
Introduction The basic input in the process of open-pit limit optimization is a set of block values representing the net economic worth of each block. Based on the estimated block economic values (BEV’s), the optimizer selects the optimum destination of each block in order to maximize the overall pit value under some given technical constraints. A dollar value is usually assigned to each block by estimating the revenue of recoverable metal at a given fixed metal price and subtracting applicable mining, processing and other costs. The variation trend of metals price and mining costs over the years shows that deterministic assumptions for values of such parameters will result in errors in the process of BEV calculation. The effective parameters such as metal price, operating costs, grade etc. are always assumed deterministic in the conventional methods of BEV calculation. While, these parameters have, obviously, uncertain nature.
Methodology and Approaches In this paper, the BEV and project NPV were initially determined using Whittle’s formula based on deterministic economic parameters. Then the Monte Carlo Simulation (MCS) method was employed and the economic uncertainties such as the metal price and cost uncertainties were taken into account. The economic data of Grassberg Copper Mine were utilized to achieve this goal.
Results and Conclusions The results showed that the Monte Carlo simulation method is highly capable of estimation of economic uncertainties. The estimated block economic values using this method are closer to the actual values compared to other methods and the error percentage is lower. As well, it was observed that the net present values obtained by the Monte Carlo simulation are closer to the reality compared to the other calculations.

This paper tries to determine an optimum condition for the flotation operation of the Alborz-Sharghi coal washing plant. For this purpose, a series of comprehensive experiments have been conducted on representative samples from feed of the flotation system of the plant. Four operational variables such as the collector dosage (Fuel oil), the frother dosages (MIBC), the pulp density percent and the impeller speed were taken into account. After obtaining representative samples, 81 required experiments were designed using the orthogonal array (34) of Taguchi method. Three levels of the variables amount including low, base and high were considered for the experiments. The most obvious finding to emerge from this study was that the optimum flotation recovery (61.09 %) is obtained in the base level (L-2) of the collector dosage, the lowest level (L-1) of MIBC and the highest levels (L-3) of the pulp density and the impeller speed. The sensitivity analysis of the variables also indicated that the increase in the collector dosage causes to increase in the total recovery of the flotation and the coal quality. Besides, the largest effect on total recovery was clearly related to the pulp density levels. The increase in values of the pulp density causes to decrease in the recovery values.