مسعود منجزی

The occurrence of settlement induced by tunneling operations in urban environments is an inevitable phenomenon. The risk of damage to nearby infrastructures and surface structures can be greatly reduced by predicting and controlling this event. This paper uses multiple linear regression (MLR) model and random forest (RF) algorithm to predict the maximum surface settlement (Smax) due to shallow tunnel excavation. Nine input parameters, including the distance of the tunnel center from the ground surface (H), height of the underground water level above the tunnel (W.T), tunnel diameter (D), elastic modulus of soil (E), undrained shear strength of soil (Cu), earth pressure coefficient (K0), unit weight of soil (γ), gap parameter (g), and stability number (N) were selected from 24 data sets related to 14 tunneling projects. The MLR and RF techniques were then implemented for predicting Smax. Three performance indicators of coefficient of determination (R2), root mean square error (RMSE), and mean absolute error (MAE) were employed for the training and test phases to evaluate the efficiency of the suggested models. The coefficient of determination values for MLR and RF for training data were 0.814 and 0.957, respectively, while for test data were 0.793 and 0.96, indicating that the RF approach is more efficient than MLR. Moreover, the findings reveal that the RF algorithm exhibits lower RMSE and MAE values in both the training and testing phases compared to the MLR method. This suggests that the RF algorithm exhibits reduced error and higher reliability and accuracy when compared to the MLR model. Also, the performance study demonstrates that among the input parameters, the gap parameter (g) and the undrained shear strength of soil (Cu) have the greatest and least influence on Smax, respectively.

Determining the appropriate blasting pattern is important to prevent any damage to the tunnel perimeter in conventional tunneling by blasting operation in hard rocks. In this research work, the LS-DYNA software and numerical finite element method (FEM) are used for simulation of the blasting process in the Miyaneh-Ardabil railway tunnel. For this aim, the strong explosive model and nonlinear kinematic plastic material model are considered. Furthermore, the parameters required for the Johnson-Holmquist behavioral model are based on the Johnson-Holmquist-Ceramic material model relationships and are determined for the andesitic rock mass around studied tunnel. The model geometry is designed using AUTOCAD software and Hyper-mesh software is applied for meshing simulation. After introducing elements properties and material behavioral models and applying control and output parameters in LS-PrePost software, the modeling process is performed by LS-DYNA software. Different patterns of blastholes including 66, 23, and 19 holes, with diameters of 40 and 51 mm, and depths of 3 to 3.8 m are investigated by three-dimensional FEM. The borehole pressure caused by the ammonium nitrate-fuel oil (ANFO) detonation is considered based on the Jones-Wilkins-Lee (JWL) equation of state in the LS-DYNA software. The outer boundaries of the model are considered non-reflective to prevent the wave’s return. The results showed that LS-DYNA software can efficiently simulate the blasting process. Moreover, the post-failure rate of the blasting is reduced by more than 30% using the main charge with less explosive power and reducing the distance and diameter of contour holes.

Loading and haulage operation in open pit mines is the last stage of the mining process. truck- shovel system, due to its many advantages  including high flexibility, is preferred for this operation. Due to high operating costs, proper fleet management and optimization can significantly affect the project economics. Truck allocation and dispatching issue is a very complex problem, especially in large mines with numerous loading and dumping points. Because of the problem size and complexity, employing mathematical methods is not justified due to very high solution time which leads to employing super computers. To overcome the aforesaid shortcoming, heuristic algorithms can be applied. In this paper, in MATLAB environment, a heuristic algorithm was developed to solve allocation and dispatching problem of transportation fleet of a real mine. According to the obtained results, a running time of 39 seconds was computed for the heuristic algorithm. Finally, the same problem was solved with an available mathematical model with a running time of 24 hours which shows the superiority of the proposed algorithm over the mathematical modeling.

Assessing the impact of information and communication technology on the family system (structural and emotional relationships) in order to predict the structure of families in the future can be the subject of an applied research. Iranian families in metropolitan areas face structural problems and a lack of emotional relationships due to the use of ICT, as almost all family members, regardless of gender, age and status, are considered users. This study seeks to investigate the effects of information and communication technology on the structural relationships and emotional relationships of the family members according to the social theory of communication. So the research question is how the structure of the family (structural relationships and emotional relationships) will be constructed by the influence of information and communication technologies? According to the social theory of communication, the change in structural and emotional relationships is due to the influence of technological relationships in the family. The difference in the forms of the family in the future is due to the influence of communication technology, due to the difference in the amount and manner of using technology between family members and at different stages of life. The main conclusion and idea of this paper is that the structure of the contemporary family will change in new ways due to the influence of ICT according to the social theory of communication in near future.

Virtual social networks play a very important role in social change, especially changes in the structural and emotional relationships of the family. But predicting these changes is very important today. But what method can be used to predict structural changes in the family? This study intends to introduce one of these methods, which is a subset of artificial intelligence, called artificial neural network and as an example to show its effectiveness in predicting the relationships of families affected by virtual social networks. Therefore, the research question is formulated in such a way that by what method or methods can the consequences of the impact of virtual social networks on family relationships be predicted? Since this research is a quantitative research, data collection was done by a questionnaire and the research model was operational. The research model is a fuzzy field statistical model. The result of the research is the prediction of four types of committed, unsuccessful, incompatible and broken families, which were shown on the fuzzy spectrum as follows: Committed family: 75 to 100%; Unsuccessful family: 50 to 75%; Incompatible family 25 to 50 percent and broken family 0 to 25 percent.

The production cycle in open-pit mines includes the drilling, blasting, loading, and haulage. Since loading and haulage account for a large part of the mining costs, it is very important to optimize the transport fleet from the economic viewpoint. Simulation is one of the most widely used methods in the field of fleet design. However, it is unable to propose an optimized scenario for which the appropriate metaheuristic method should be employed. This paper considers the Sungun copper mine as the case study, and attempts to find the most feasible transportation arrangement. In the first step, in this work, we compare the flexible dispatching with the fixed allocation methods using the Arena software. Accordingly, the use of flexible dispatching reveals the increase in the production rate (20%) and productivity (25%), and the decrease (20%) in the idle time. The firefly metaheuristic algorithm used in the second step shows that the combined scenario of the 35-ton and 100-ton trucks is the most suitable option in terms of productivity and cost. In another attempt, comparing different heterogeneous truck fleets, we have found that the scenarios 35-100 and 35-60-100-144 increase the production rate by 39% and 49%, respectively. Also, in both scenarios, the production cost decreases by 11% and 21%, respectively.

Summary:

In this paper, the blasting data and rock mass characteristics of Chogart, Chadormalu, and Sechahum mines were used to predict the size distribution of rock fragmentation (D80). Rock fragmentation is affected by various parameters such as rock mass properties, in-situ blocks shape, blasting geometry, etc. To quantify the shape of in-situ blocks, fractal geometry is a suitable method. To predict the rock fragmentation (D80) based on independent variables (rock mass characteristics, in-situ block shape, and blasting geometry); linear/nonlinear regression and neural networks were used. The results showed that the nonlinear regression and neural network were the ability to predict the size distribution of rock fragmentation. 

Due to economic reasons, drilling and blasting methods have been used in mining, quarrying industries, and civil projects. The results of blasting can be categorized into two i.e. favorable results (rock fragmentation, heave and move material) and unfavorable results (air overpressure, back break, ground vibration, and fly rock). Rock fragmentation due to blasting is influenced by several factors that are classified into three namely; explosive parameters, rock mass characteristics, and blast geometry. In recent decades, several empirical models have been proposed to predict rock fragmentation due to blasting. Nowadays, based on computer science advances,    regression analysis and artificial intelligence (AI) have been employed for rock fragmentation prediction.

Methodology and Approaches:

In this research, fractal geometry was used to describe the rock mass shape. The fractal dimension of in-situ blocks was determined by the box-counting method. On the other hand, the uniaxial compressive strength (UCS) and longitude wave velocity (laboratory and in-situ) were considered as rock mass characteristics. Also, the powder factor (PF) was representative of blasting geometry and explosive parameters. The linear/nonlinear regression and neural network were used to investigate the relationship between the rock fragmentation and independence variables (rock mass characteristics, blasting geometry, rock mass shape).

In this research, an attempt was made to predict the size distribution of rock fragmentation (D80)at the Central iron ore mines (Chogart, Chadormalu, and Sechahun) by linear/nonlinear regression and neural network. Linear regression results revealed that the independent variables have a significant effect on the dependent variable (D80). The results were shown the neural network has the superiority to the prediction of rock fragmentation.

In each project, there is always a possibility of occurrence of hazards and risks. Accidents cause many damages such as financial and psychological problems, that may have a negative effect on the workers life. To prevent or reduce the occurrence of incidents, it is necessary to identify and manage the relevant affecting factors. Blasting is one of the events that has frequently led to accidents. In this paper, 13 factors affecting the occurrence of blasting related accidents in the mining and construction projects, have been selected according to the opinion of experts and ranked to identify the most important one. For this purpose, Monte Carlo simulation method and analytical hierarchy process method were implemented. The factors were ranked based on the opinion of 15 experts in this field. Finally, based on the obtained results, inappropriate blasting time, unprofessional personnel were selected as the most important factors. Also, blasting pattern specifications, type of explosive and rock mass characteristics have the least effect in this regard. Validity of the paper outcomes was checked with comparing the real recorded events.

Waste rock dumping is very important in the production planning of open-pit mines. This subject is more crucial when there is a potential of acid-forming (PAF) by waste rocks. In such a type of mines, to protect the environment, the PAF materials should be encapsulated by non-harmful rocks. Therefore, block sequencing of the mined materials should be in such a way that both the environmental and economic considerations are considered. If non-acid forming (NAF) rocks are not mined in a proper time, then a stockpile is required for the NAF materials, which later on would be re-handled for encapsulation of PAF rocks. In the available models, the focus is on either block sequencing or waste dumping strategy. In this work, an attempt has been made to develop an integrated mathematical model for simultaneous optimization of block sequencing and waste rock dumping. The developed model not only maximizes the net present value (NPV) but also decreases the destructive environmental effects of inappropriate waste dumping. The proposed model, which is solved by a CPLEX engine, is applied to two different iron deposits. Also the performance of the proposed model is cross-checked by applying the available (traditional) models in a two-step manner. According to the results obtained, it can be considered that utilizing the developed model, because of extensive re-handling cost reduction, the NPV improvement is significant, especially when the overall stripping ratio is higher (deposit case A).

Truck-Shovel fleet, as the most common transportation system in open-pit mines, has a significant part of mining costs, for which optimal management can lead to substantial cost reductions. Among the available dispatch mathematical models, the multi-stage approach is well suited for allocating trucks to respected shovels in a dynamic dispatching program. However, with this kind of modeling sequencing of the allocated trucks is not possible though it is important to find out the best solution so that getting the minimum accrued cost. To comply with the shortcoming of the traditional model, in this paper, a new hybrid model is developed and applied in Copper Mine of Iran, in which for each truck an allocation matrix is considered as input to the genetic algorithm implemented to determine the best solution. According to the obtained results, the optimal sequencing of the trucks can result in a significant (31%) cost reduction in a shift.

Uncertainties of economic parameters have an indispensable role in mining evaluation projects. Therefore, evaluation of a mining project without considering the available uncertainties is incorrect and unreliable. Metal price uncertainty is the most important parameter in a mine economic uncertainty. Many researchers have studied the role of economic uncertainties in the production planning. But majority of these studies are conducted in one-element deposits and the two-element deposits are rarely investigated. In this research the binomial tree technique valuation is used to compute the production planning evaluation in two element deposits for price uncertainty. It is concluded that the mine evaluation suggests greater net present value when price uncertainty is considered for both element. 

The main sources of uncertainty arising at the beginning of a mine project can be categorized into three groups: geology uncertainties, engineering uncertainties and economic uncertainties. Many researchers studied these types of uncertainties. In majority of the aforementioned researches, price uncertainty effect was investigated only for one- element deposits, which can result in an inaccurate valuation. Therefore, in this paper, the binomial tree method was used for determining the effect of price uncertainty in valuation of two-element deposits. 

In this research the binomial tree technique valuation is used to compute the production planning evaluation in two element deposits for price uncertainty in four scenarios: Assuming price certainty for both elements; assuming price uncertainty for first element and price certainty for second element; assuming price certainty for first element and price uncertainty for second element; assuming price uncertainty for both elements. 

It is concluded that the mine evaluation suggests least NPV of 245.05 thousand dollars when price certainty is considered for both elements and greater NPV of 349.6 thousand dollars when price uncertainty is considered for both elements. The sensitivity analysis showed that even by changing input economic parameters, the NPV, which was computed in price uncertain for both elements, was the greatest.

Stope layout and production scheduling are two main areas of underground mine planning that have a close relationship with each other such a way that optimization results of each of them have important effects on the other one. Hence, separately optimization of these two areas cannot guarantee the true optimum. Because, in isolated optimization approach often ignores the effects of different planning areas on each other, this is not able to manage issues among different areas. To tackle this problem, simultaneous optimization approaches are developed in recent years which simultaneously optimize the different areas of the mine planning process and lead to more profitable mine plans. In the present research, a mathematical IP model has been developed to simultaneously optimize stope layout and production schedules for sublevel stoping (SLS). Then, the developed model is applied on iron ore operations and obtained results from simultaneous optimization were compared with those of isolated approach. It has been concluded that the simultaneous optimization approach is able to produce the globally optimal scheduling result for the stope layout optimization and scheduling by taking into account all constraints related to both areas. Application of the simultanous optimization method in iron ore operation resulted in a 16 per cent increase in final NPV over the isolated approach.

In open pit mines the loaders and hauls choice , allocation and dispatching of trucks is one of the most important and complicated operational processes. That’s optimization results in great thrifts. For the analysis of this type of examples, simulation is a powerful tool.One of the basic points in the topic of simulation models is the presentment of optimization. Simulation based optimization methods are often one objective. In this Thesis it has been objected to use the multi objective forms of these methods. The considered goals are minimizing of operational cost and maximizing of production. Meta heuristic algorithms which is chosen for this issue Multi-Objective-Particle-Swarm-Optimization (MOPSO). For simulation we have used Arena’s software and for optimization used MATLAB software. The results of proposal methods implementation on Sungun mine’s loading and hauling fleet, shows that with the administration of obtained scenarios it is possible to maximize the current production of the mine 2.5 times and to reduce the operational costs of production up to 23%

Summary :To date, given the importance of drilling operations in the mining process and its impact on other phases of extraction, a number of studies have been carried out to investigate the influence of predicted bit rate and to determine the parameters influencing it. The estimation of rate of penetration (ROP) is an integral part of the design and planning stages of a mining project. To achieve a reasonable estimation of the ROP in the rock, it is essential to identify the influential factors, measure the effectiveness, and calculate the uncertainty of each set. The factors examined in current study include thrust, rock mass characteristics including rock mass indices, uniaxial compressive strength of intact rock, Schmidt hardness. Conducting a field study and laboratory testing, a database was compiled from the above parameters in Irankuh mine. Among these parameters, Schmidt hardness has the lowest standard deviation and highest reliability index and thrust has the highest standard deviation and lowest reliability index. This result indicated that in the independent parameters, Schmidt hardness has the lowest and thrust has the highest level of uncertainty. The standard deviation and reliability index of experimental models presented for the (ROP) are also at 53.02 and 0.729 percent respectively. So this relationship to predict the (ROP) in the lead and zinc Irankuh mine is confidence.
In this research, the necessary parameters to predict the drilling speed were selected in order to examine the effect of different geological, geo-mechanical and operational parameters of the drilling machine on ROP and prediction of the performance of the drilling machine. The model was obtained from effective parameters for the penetration rate. In the following, the reliability rate of the obtained model was calculated for the rate of permeability of drilling-bit in Irankouh mine to examine the uncertainty rate.
Methodology and Approaches : The first-order second-moment method has the calculations that are relatively simple, and only the information needed to calculate it, is information related to the moments of the input variables, so this method has been used in this research.
Results and In this research, there is an uncertainty in registered information due to the environment to conduct the field observations and surveys and laboratory tests. Therefore, there is uncertainty and ambiguity in the developed equation to calculate the penetration rate in the Irankouh mine. So it has been tried to calculate the amount of ambiguity and, in fact, the number of variations that indicates the dispersion around the actual value of the penetration rate, using reliability analysis to FOSM first-order-second-moment method.

The main purpose of optimal blasting operation is suitable crushing and prevent undesirable and unwanted phenomena arising from the blasting (ground vibration, flyrock and backbreak). Generally, the parameters affecting the blasting operation divided into two main group controllable (blasting pattern) and uncontrollable (geomechanical properties of rocks) parameters. Controllable parameter can be determined using experimental models. Due to variety of obtained values for controllable parameters from experimental models, it is necessary to use methods with high efficiency. The main reason for not achieving good results in experimental models is engaging a large number of parameters, resulting from blasting. The combination of intelligent and mehta heuristic methods to solve suck problems can be useful. In this study, Delkan iron mine as a case study has a side effects of blasting like as flyrock and backbreak. The main purpose of this paper is making a artificial neural network model as a strong predictor and finding a combination of data using ant colony optimization to minimum the unwanted phenomena. After the Modeling the blast pattern Burden 2.8 m, spacing 3.3 m, hole length 10.2 m, stemming 1.5 m and powder factor is 201 gr/ton. Using this model can lead to a reduction of approximately 42 percent and 62 percent in flyrock and backbreak respectively.

Fluctuation of the grade of the ore feeding the processing plants is one of the most important factors in plant's recovery, so that plant efficiency increases as the fluctuations decrease. In order to reduce the grade variability, homogenization piles are largely used in mining industry. The mass and the number of layers in the pile have an important role in the rate of grade variability reduction. The grade variability decreases as the mass and the number of layers increase, although this increase can lead to an increase in operation complexity and costs. In this research, a method based on geostatistical simulations was used to predict grade variability in the Anguran mine. In this regard, 25 equi-probable grade realizations are first generated using the Sequential Gaussian Simulation method by SGeMS Software, then the block model for each realization is built in DATAMINE Software. In the next stage, the blocks that would be sent to the pile are specified taking into account the production planning (mine schedule) obtained NPV Software. Finally, grade variability was determined for various pile sizes and number of forming layers. Based on the obtained results, piles with a mass of 187 kt and 25 layers have the lowest variability.

Fleet selection in open-pit mines is of vital importance. Inappropriate selection of fleet can be influential on the project economics in two ways; failure in achieving production objectives and increase the attributed costs due to increase in the shipment time. Usually fleet design is fulfilled in the forms of either allocating or dispatching aiming to production maximization and cost minimization. Simulation technique, one of the available fleet selection methods, allows designers to study a system in a virtual environment without executing it in the real world, to technically and economically evaluate its efficiency. In this paper, at first using the simulation technique, transportation system of the Songun copper mine was simulated in the Arena software and then by defining new systems, efficiency of the available fleet was improved. On the basis of the obtained results it was observed that a fleet of 100 t dump trucks, 6.1 m3 front end loaders, 7.0 m3 shovel and 4.6 m3 excavator with weekly production of 550,000 tones and 0.83$ per/ m3 is the best possible alternative.

blasting operations with regard to influencing other parts of the mineral extractionprocess (loading, hauling and crushing) is of major importance. In this operation theassessment criteria in addition to achieving the desired fragmentation is reduction ofundesirable phenomena such as back-break and fly-rock. several parameters such as geometric properties,rock mass properties and characteristics of explosives affect blasting pattern.As the problem is multi criteria, utilization of useful methods that satisfyoperational objectives (desired fragmentation, reduction of back-break and fly-rock)is necessary.In this research in which Gol-E-Gohar Iron ore mine is considered as a case study,in the first with using multiple attribute decision making model (TOPSIS), efficient pattern from the patterns carried out in the mine based on the objectives is selected.in the second part of the research has been tried to improve the values of these objectives using goal programming(GP) model.

In longwall mining, the roof strata behind the stope is caved with face advancing. This is followed by a high load distribution around the panel and gob. Determination of the stress concentration, its distribution around the panel and the influencing parameters such as the stope length are of critical important to design of longwall mining. Both numerical and empirical methods can be used to investigate the effect of stope length on distribution and concentration of stress induced at the panel. In this paper, numerical modeling and analysis of a longwall panel were carried out by using a 3D numerical model for investigating the effect of stope length on stress distribution in Tabas coal mine, Parvadeh No. 1. A sensitivity analysis of the impact of six different values of stope length on abutement stress showed a nonlinear relation between the stope length and front abutment stress. The analyses results revealed that with a 30 percent increase in the stope length from 190 to 245 m, front abutment stress increases only to 10 percent (i.e. 5.55 to 6 MPa).With stopes longer than 245 meters up to 300 meters, an increase of 20 percent in stope length resulted in a 27 percent decrease of roof stress (i.e. 6 to 4.4 MPa). Also, results obtained from modeling studies revealed that the location of maximum front abutment stress increases from 5 to 15 meters in front of the face with change in stope length from 190 to 300 meters.

Mining due to its specific characteristic is considered as a hazardous industry. Risk assessment is one of the most important project management procedures. The aim of this research is to present an approach for identification and ranking risks in the open pit mines. Ranking is a multi-attribute decision making. In order to identify the risks، risk breakdown structure method and ranking risks by Fuzzy TOPSIS method is implemented. First a comprehensive structure of risks in open pit mines in the frame work of 20 main levels and 117 sublevels is designed. Then different indices such as “probability”، “impact on project objectives (time، cost، quality and performance) ’’، “manageability”، “continually repeating”، “exposure”، “proximity”، “confidence level” for ranking of risk issues are determined. This approach is applied for risk assessment of Zarshuran Gold Mine as one of the biggest goldmines in Iran. In this mine، marketing، geotechnical، economic and political risks have been identified as the most significant risks، respectively.

From economical point of view، identification and quantification of grade uncertainty have a great importance. For uncertainty quantification، conditional simulation techniques، appropriate to uncertainty quantification can be applied. These techniques are effectively implemented for single element deposits; but in the multi elements deposits in which spatial correlation is of great importance، can rarely be employed. For this purpose، other techniques such as conditional co-simulation (CCS)، stepwise conditional simulation (SCT)، principal component analysis (PCA(and min/max autocorrelation factors (MAF) can be utilized. In the recent years، MAF technique considering various merits to the aforesaid approaches has successfully been applied for simulating multi elements deposits. MAF can effectively be used in such deposits with more than one spatial structure. Also، using this technique، de-correlation can be done for all lag distances and accordingly can well reproduce the correlations. In this research، MAF was applied for simulation of a multi-element porphyry Copper deposit. On basis of the obtained results، it was considered that MAF can effectively be utilized for reproducing the correlations.

The main issue of tunnelling in urban environment is the prediction of the ground settlements induced by tunnel excavation, which may cause damage to surface structures. In this paper are used empirical, analytical and three-dimensional finite element methods (by ABAQUS program) for the prediction of ground settlements induced during tunnel construction using EPB (earth pressure balance) excavation machine in part of the line 3 of Tehran subway. The results indicate that the three dimensional FEM predicts a more realistic surface settlement value, and settlement profiles from empirical and analytical methods are narrower than numerical one. Moreover, the maximum predicted settlement by three methods is more than its suggested allowable value.