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In this paper, a coupled thermo-hydro-mechanical (THM) simulation in a faulted deformable porous medium is presented. This model involves solving the mass conservation, linear momentum balance, and energy balance equations which are derived from the Biot's consolidation theory. Fluid pore pressure, solid displacement, and temperature are chosen as initial variables in these equations, and the finite element method in combination with the interface element is used for spatial discretization of continuous and discontinuities (fault) parts of the medium to solve the equations. The main purpose of this study is providing precise formulations, applicability, and ability of the triple-node zero-thickness interface element in THM modeling of faults. It should be noted that the system of equations is solved using a computer code written in Matlab program. In order to verify the developed method, simulations of index problems such as Mandel's problem, and coupled modeling of a faulted porous medium and a faulted aquifer are presented. The modeling results obtained from the developed method show a very good agreement with those by other modeling methods, which indicates its accuracy. Keywords: Thermo-hydro-mechanical (THM) simulation, Geomechanical coupling, Zero-thickness element, Joint element, Finite element

Solving the stope boundary optimization problem is the primary step that should be taken after selecting an underground mining method. Obviously, this solution should be optimal because suboptimal boundaries may lead to wasting both mining capital and mineral resources. Four decades have passed since the first algorithm was developed to solve this problem, and no comprehensive method has yet been reported. The current study presents an integer programming (IP) model to solve the stope boundary optimization (SBO) problem. The application domain of this model is limited to non-complex problems, and thus, to cover all the stope boundary optimization problems, a greedy algorithm is also developed. The greedy algorithm was implemented on three real cases and its optimality gap on these cases is 1.85%, 0.47%, and 1.42%, respectively. However, to obtain better results and decrease the optimality gap, this paper introduces a new iterative enumeration algorithm. The proposed algorithm uses two inner algorithms: the Improved Greedy and Approximate Dynamic Programming algorithms. The optimality gap of the Iterative Enumeration algorithm in all the mentioned cases was less than 0.6%.

Rock burst phenomenon prediction is one of the important issues in the mining, geo-mechanics and strata control fields. Predictive classification consists of constructing models designed with the goal of exact and reliable forecasting the values of nominal target variables using information on a set of predictor variables. In this research, after regarding the particular considerations of the classification problem and identifying the key and effective predictor variables by summing up the results of the data preprocessing procedure on a rock burst database, some of the most effective classification methods currently known with totally different attitudes for separating the decision space, including five widespread families of the naive Bayesian, decision tree, nearest neighbor, support vector machine and artificial neural network techniques, were employed to predict the occurrence and intensity of the phenomenon in the form of distinct problems. The models with the highest classification accuracies were recognized based on the considered error estimators adopting different estimation strategies. These paramount classification models had satisfactory outcomes and also significantly higher performances than the simultaneous application of the empirical criteria presented based on the in-review predictor variables.

Rock bursts constitute serious hazards in underground mining and excavating. Up to now, numerous researches in the form of empirical, experimental, analytical, intelligent and numerical methods with their own specific scope, characteristics, strengths and weaknesses, have been conducted for rock burst prediction. The weaknesses and limitations of the mentioned prediction methods, especially the intelligent studies, indicate the need for continuing the researches in this field. In this research, a rock burst database, consisting of 188 datasets, was considered. Each dataset corresponds to a series of predictor variables and one of defined classes for the dependent variable “rock burst intensity”. To design classification models, describing important characteristics of datasets and predicting future trends, a data preprocessing procedure was conducted. The procedure consisted of a statistical analysis strategy, a metaheuristic technique for feature (variable) subset selection and some feature extraction techniques. The statistical analysis led to conclude that by considering the available datasets, some predictor variables have statistically insignificant contributions for rock burst prediction. By contrast, the other predictor variables have considerable ordinal contributions. These statistical inferences were completely in accordance with the results of the feature subset selection technique. Besides, the application of this technique revealed specific combinations of significant predictor variables having the highest priorities for modelling the dependent variable. The application of feature extraction techniques to construct derived components from initial datasets did not lead to representative results. Therefore, a high rank combination of significant predictor variables can be adopted to design and develop new classification models based on the considered datasets.

Coal bump is one of the most important hazards for underground miners due to occurrence without warning. This hazard leads to the problems in the form of injury, disability or fatality as well as economic loss for the mining companies. As a result, development of a model for risk analysis of coal bump is an important issue in safety of underground coal mining. Although the mechanism of the bump is not well understood, it is found that stress condition is strongly associated with the coal bump. In this research, a numerical model is developed for analyzing of coal bump risk to map the zones with a high risk in Tabas Coal Mine, Iran. Results show that the concentration of the abutment stress that could lead to a coal bump starts at more than 25 m in front of the mining face and increases nearer the face. Moreover, stress concentration starts to accelerate at a distance of 10 m ahead of the mining face. Large and rapid stress concentration is a distinct precursor of coal bump occurrence during longwall mining. Therefore, zones at a distance less than 10 m in front of the face are predicted as the bump-prone zones. The mentioned zones that moved ahead of the mining face must be considered in the mine risk analysis plan. The numerical results were validated against the monitoring data collected to measure the roof displacement in the mine roadways and confirm that prediction of the coal bump by this approach is accurate and satisfied.

Rock failure mechanism is one of the most important issues in rock mechanics engineering which plays a key role in the stability analysis of various structures. Therefore, different failure criteria have been proposed to understand the failure mechanism of rocks. One of the most commonly used rock failure criteria is the Hoek-Brown criterion, in which there is a parameter called mi, which is very important to the response provided by this criterion. Due to the importance of conducting extensive studies on this parameter, in this current research, by performing a series of experimental triaxial compressive strength test and numerical simulating in PFC-2D code, the effect of the Hoek-Brown constant mi on the failure mechanism and crack growth of different rocks has been studied. Based on the results of this study, it was found that the effect of parameter mi on the failure mechanism of different rocks varied according to the type of rocks, and the greatest effect of this parameter was on the peak strength of rocks. In addition, it was found that under higher lateral pressures, there are less destructive cracks in rocks, and as a result, they show more ductile behaviour., Mehanizam loma stijene jedno je od najvažnijih pitanja u mehanici i inženjerstvu stijena te je ključno u analizi stabilnosti raznih struktura. Za različite kriterije loma predloženi su različiti mehanizmi odgovorni za takav proces. Jedan od najčešće korištenih kriterija kojim se opisuje lom stijena jest Hoek-Brownov kriterij, u kojemu postoji varijabla zvana mi, koja je vrlo važna kod ocjene tim kriterijem. Zbog važnosti provođenja opsežnih studija o prilagođavanju te varijable u prikazanim istraživanjima ona je određena izvođenjem serije eksperimentalnih ispitivanja troosne tlačne čvrstoće i numeričke simulacije u PFC-2D kodu. Time je istražen učinak vrijednosti mi na lom stijene i rast pukotina različitih stijena. Na temelju rezultata utvrđeno je kako je utjecaj varijable mi na lom različitih vrsta stijena varijabilan, a najveći je učinak na vršnu čvrstoću stijena. Također, utvrđeno je da su pod većim bočnim pritiscima razarajuće pukotine u stijenama manje (uspoređeno s prethodnim testom), a kao rezultat pokazuju više duktilno ponašanja.

Rocks are formed from particles and the interaction between those particles controls the behaviour of a rock’s mechanical properties. Since it is very important to conduct extensive studies about the relationship between the micro-parameters and macro-parameters of rock, this paper investigates the effects of some micro-parameters on strength properties and the behaviour of cracks in rock. This is carried out by using numerical simulation of an extensive series of Uniaxial Compressive Strength (UCS) and Brazilian Tensile Strength (BTS) tests. The micro-parameters included the particles’ contact modulus, the contact stiff ness ratio, bond cohesion, bond tensile strength, the friction coefficient and the friction angle, and the mechanical properties of chromite rock have been considered as base values of the investigation. Based on the obtained results, it was found that the most important micro-parameters on the behaviour of rock in the compressive state are bond cohesion, bond tensile strength, and the friction coefficient. Also, the bond tensile strength showed the largest effect under tensile conditions. The micro-parameter of bond tensile strength increased the rock tensile strength (up to 5 times), minimized destructive cracks and increased the corresponding strain (almost 2.5 times) during critical stress., Svaka je stijena stvorena od čestica čijim se međudjelovanjem određuju njezina mehanička svojstva. Budući da je vrlo važno provesti opsežne studije o povezanosti mikroparametara i makroparametara stijene, u ovome su radu istraživani učinci nekih mikroparametara na svojstva čvrstoće i ponašanje pukotina stijene numeričkim simulacijama jednoosne tlačne čvrstoće (UCS) i brazilske vlačne čvrstoće (BTS). Ispitivani mikroparametri uključivali su kontakt čestica, omjer kontaktne krutosti, koheziju veze, vlačnu čvrstoću, koeficijent trenja i kut trenja, a mehanička svojstva stijene bogate kromitom bile su osnovne vrijednosti ispitivanih mikroparametara. Utvrđeno je kako su najvažniji mikroparametri na ponašanje stijene kod tlačenja kohezija veze, vlačna čvrstoća veze i koeficijent trenja. Također, vlačna čvrstoća ima najveći učinak u vlačnim uvjetima. Ovaj mikroparametar povećava vlačnu čvrstoću (do 5 puta), minimalizira destruktivne pukotine i povećava odgovarajuće naprezanje (gotovo 2,5 puta) pri kritičnome naprezanju.

Accurate prediction of surface subsidence becomes a significant challenge for active industrial companies in coal mining fields due to the importance of the economic impacts of longwall mining-induced subsidence. This article explores a new variant of genetic programming, namely gene expression programming (GEP). The GEP-based method is utilized to present a new mathematical formula for subsidence prediction in longwall coal mining. The derived model includes both geometrical and geological variables. The data set consists of field measurements obtained through 37 longwall panels of Ulan coal mine, NSW, Australia. The GEP-based model concluded satisfactory subsidence prediction outcomes compared to other empirical methods such as NCB, DMR, ACARP, and IPM. The predictive ability of the GEP-based models, which captures the complex nonlinear effects of the critical factors on the magnitude of subsidence, resulted in a statistically significant improvement in predictive capacity compared to the aforementioned empirical methods. The sensitivity analysis results indicated that Panel width and cover thickness with 31% and 23% were the most influential parameters in the proposed model. Also, the extracted seam thickness, thick layer location, and thick layer thickness had 19%, 16%, and 11% impact on the GEP proposed model, respectively.

Rock burst phenomenon is one of the major concerns in underground mining and excavating. Despite the significant progress in the field of phenomenon prediction, including data-driven researches, the need for developing reliable models is still felt. In this research, a database consisting of 188 distinct case histories was considered. Each case history contains some of the predictor variables “overburden thickness, maximum tangential stress in the boundary of opening, uniaxial compressive strength of rock, tensile strength of rock, stress ratio, brittleness ratio and elastic strain energy index” and one of the four defined classes (none or not-occurred, weak, moderate and strong) for the qualitative dependent variable “rock burst intensity”. After data preprocessing procedure (including outlier detection and substitution, factor analysis and nonlinear principal component analysis in which the two later did not lead to extract reasonable predictor components), 48 regression models were fitted for different original predictor variable arrangements, fitting datasets and link functions to obtain proper statistical equations for rock burst occurrence and intensity prediction. The best models, recognized based on fulfilling certain criteria and maximizing specific objectives, have proper classification accuracies, especially in comparison to those of the empirical criteria developed for rock burst prediction. Due to the presence pattern of the predictor variables in regression equations, a stepwise statistical strategy, consisting of normality condition evaluation, evaluation of mean equality and distribution function equality hypotheses and correlation analysis for predictor variables, was considered. The strategy led to conclude that the predictor variables “overburden thickness, tensile strength of rock and brittleness ratio” with the available case histories have insignificant contributions to predict rock burst phenomenon. By contrast, the predictor variables “maximum tangential stress, stress ratio, elastic strain energy index and uniaxial compressive strength of rock” have considerable contributions, decreasing in an ascending order. The application of non-dominated sorting genetic algorithm and multi-layer perceptron neural networks for solving the feature (i.e. variable) selection problem had a perfect adaption with the inferences.

Concrete-lined pressure tunnels under high internal water pressures require to be reinforced to withstand failure. Since these tunnels exert the sources of water flow to the rock formations, a suitable arrangement of lining reinforcement is required to minimize water losses from the tunnel. A simulation of this hydro-mechanical interaction can be implemented based on a direct-coupled method using the ABAQUS finite element program. In addition, a subroutine code has been developed to evaluate the influence of internal water pressure on the permeability coefficient variation of porous media. In the present study, the reinforcement distribution in the lining is investigated to limit water losses from the tunnel. To verify the proposed model, the numerical and analytical solutions are compared based on the elastic behavior of the media and a reasonable agreement is obtained. The results of the structural nonlinear analysis indicate that water losses from the tunnel remain at a lower amount with small diameters and spacing in the concrete lining for at the same percentage of reinforcement.

Rock bursts are sudden and violent failures of surrounding rockmasses in underground mines and excavations. In this paper, a database consisting of 188 case histories was collected. Each case histo...

When particle's wave velocity resulting from mining blasts exceeds a certain level, then the intensity of produced vibrations incur damages to the structures around the blasting regions. Development of mathematical models for predicting the peak particle velocity (PPV) based on the properties of the wave emission environment is an appropriate method for better designing of blasting parameters, since the probability of incurred damages can considerably be mitigated by controlling the intensity of vibrations at the building sites. In this research, first out of 11 blasting and geo-mechanical parameters of rock masses, four parameters which had the greatest influence on the vibrational wave velocities were specified using regression analysis. Thereafter, some models were developed for predicting the PPV by nonlinear regression analysis (NLRA) and artificial neural network (ANN) with correlation coefficients of 0.854 and 0.662, respectively. Afterward, the coefficients associated with the parameters in the NLRA model were optimized using optimization particle swarm-genetic algorithm. The values of PPV were estimated for 18 testing dataset in order to evaluate the accuracy of the prediction and performance of the developed models. By calculating statistical indices for the test recorded maps, it was found that the optimized model can predict the PPV with a lower error than the other two models. Furthermore, considering the correlation coefficient (0.75) between the values of the PPV measured and predicted by the optimized nonlinear model, it was found that this model possesses a more desirable performance for predicting the PPV than the other two models.

Underground coal mining is one of the most hazardous activities in all around the word. Therefore, risk analysis has a remarkable role in the coal mining works. In this study, a new probabilistic approach is developed to evaluate the most important hazard of coal mining. For this aim, at first a fuzzy TOPSIS model is applied to rank the risks of the mining. By this way, it is possible to overcome the existing uncertainty of the risk ranking process. Application of the proposed procedure shows that the roof fall is the most important hazard in the Tabas Coal Mine in Iran. Afterward, this study tried to quantify the roof fall risk as the most important hazard in underground coal mining. Due to the related uncertainties associated with every mine, it is very difficult to predict the roof fall. As a result, development of a methodology for evaluation of roof fall risk under uncertainty condition has a key role in safety of underground coal mines. In this paper, a new approach for analyzing the risk of roof fall is presented. For this aim, the major factors influencing the stability of the roof are utilized in a Bayesian network-based model. The proposed method is illustrated with an application in Tabas Coal Mine. The results show that that BN-based model is a capable method for adjusting to uncertainties in the roof fall risk evaluation.

This paper deals with the control of ground surface settlement due to excavation of shallow tunnels. In order to control the settlement, one should be able to predict it, based on the prediction one may consider required preventions and protections. Prediction of surface settlement depends on several parameters and each parameter has an effect on the other. Application of the traditional methods could become impractical as the proposed equations might have low accuracy. To overcome these limitations, intelligent methods could be implemented. The present study aims to develop an intelligent model for prediction of the surface settlement in Shanghai subway line 2 project using adaptive neuro-fuzzy inference system (ANFIS). The results indicated that the proposed model had an appropriate performance. In order to perform sensitivity analysis of the ANFIS model, cosine amplitude method (CAM) was used and according to the results it was found that the operational, geometric and strength parameters had the highest impacts, respectively. Furthermore, amongst the input parameters, the two parameters of grout filling percentage (n) and grouting pressure (P) were identified as the most effective ones. The values of critical settlement were determined based on Rankin’s criteria of damage risk assessment to control the ground settlement. Then, the corresponding surface settlement was minimized by changing values of the input parameters. According to the results, control of machine operational factors particularly the n and P parameters had a crucial role in reducing surface settlement and preventing pertinent damages.

Laser perforating is a new method in oil and gas wells where researchers look for an alternative to explosive methods. One of the important problems with this method is the generation of uniform and cylindrical holes at a selected pitch for enhancing the permeability of rocks. In non-moving laser perforation, the nozzle of the laser and the rock do not approach each other and due to laser convergence in a point, uniform and cylindrical holes are not created. For this reason, moving laser perforation is suggested in this research. One of the important parameters in moving laser perforation is the power of the laser that can be perforated at a specific rate. In this article we predicted the laser power for a definite rate of perforation (ROP) and then the accuracy of this prediction was evaluated to support the experiments. A pulsed Nd: YAG laser, with a pulse energy around 5.5 J, pulse repetition rate of 30 Hz and pulse duration of 2 ms were used for rock perforation in this study. The results shows that the presented relation for perforation could reliably be used in practice. Furthermore, by knowing the rate of perforation, the required laser power for consistent drilling could be calculated.

Retreat mining is always accompanied by a great amount of accidents and most of them are due to roof fall. Therefore, development of methodologies to evaluate the roof fall susceptibility (RFS) seems essential. Ghasemi et al. (2012) proposed a systematic methodology to assess the roof fall risk during retreat mining based on risk assessment classic approach. The main defect of this method is ignorance of subjective uncertainties due to linguistic input value of some factors, low resolution, fixed weighting, sharp class boundaries, etc. To remove this defection and improve the mentioned method, in this paper, a novel methodology is presented to assess the RFS using fuzzy approach. The application of fuzzy approach provides an effective tool to handle the subjective uncertainties. Furthermore, fuzzy analytical hierarchy process (AHP) is used to structure and prioritize various risk factors and sub-factors during development of this method. This methodology is applied to identify the susceptibility of roof fall occurrence in main panel of Tabas Central Mine (TCM), Iran. The results indicate that this methodology is effective and efficient in assessing RFS.

The present study aims to employ modern intelligent method to predict intact rock strength parameters. This method can be used for intact rock strength parameters prediction of different extraction projects. Mechanical rock excavation projects need uniaxial compressive strength ( UCS ) and static modulus of elasticity ( E ) of the intact rock material. Many parameters affect to strength parameters, but some of them are applicable to empirical or analytical equations and the other difficulty, high-quality core samples of appropriate geometry are needed to find out these parameters. In this regard, models predicting UCS and E based on rock index tests and intact rock properties could be useful methods. This paper aims to employ Gene Expression Programming (GEP) to predict E and UCS . Out of the 44 sets of the data, 22 sets (50% of the data) were considered for training and the remaining 22 sets of the data (50%) were considered for testing. The intelligent method has been studied on the basis of data obtained from 44 different excavation projects all over the world. These parameters were collected from previous research data. The values of UCS and E are predicted by using quartz content ( Q ), dry density ( γ d ) and porosity ( n ) of the rocks. 22 datasets (50% of the data) were utilized for modeling and the remaining 22 sets of the data (50%) were considered for evaluating theirs performance. For this purpose, writing a code was necessary, as some of the proposed relations were complex. The obtained results of this study are presented within a computer-based format in order to be easily accessible too every experts. With respect to the accuracy of the GEP method, it may be recommended for predicting intact rock strength parameters for future excavation design purpose.

Wear of cutting tools and other components of the pressurized face tunnel boring machine which come in contact with the muck is an important parameter in soft ground tunneling. This is due to the need for cutterhead inspection and tool maintenance under pressurized conditions or “hyperbaric interventions” which is a time consuming, dangerous, risky, and costly activity. This study investigates the tool wear of an EPB-TBM used in Tehran metro line 7 over the initial 6500 m of tunnel. The ground along the tunnel alignment is mostly alluvial deposits, composed of gravely sand with clay/silt and sandy gravel with clay. In this project, the number of cutting tools replaced were 1169, including 654 rippers, 357 scrapers and 153 disc cutters. In this paper the influence of geological parameters and operational factors on tool wear is examined. The results show that by increasing TBM thrust, earth pressure, and torque, the cutter consumption generally increases. Also, soil conditioning plays an important role in controlling cutter wear, so that the tool wear can be reduced even in coarse-grained soils by improving the soil conditioning parameters. Quantitative analysis of these parameters for this project and a brief discussion of their implications is offered. Also, multiple linear regression analysis was used to seek the best correlation between the cutter wear and ground / opening variables, and correlation coefficient of up to 0.98 have been observed. This highlights the potential of developing models for tool wear prediction by multiple regression, if sufficient data is collected in soft ground tunneling projects.

This study aims to predict ground surface settlement due to shallow tunneling and introduce the most affecting parameters on this phenomenon. Based on data collected from Shanghai LRT Line 2 project undertaken by TBM-EPB method, this research has considered the tunnel’s geometric, strength, and operational factors as the dependent variables. At first, multiple regression (MR) method was used to propose equations based on various parameters. The results indicated the dependency of surface settlement on many parameters so that the interactions among different parameters make it impossible to use MR method as it leads to equations of poor accuracy. As such, adaptive neuro-fuzzy inference system (ANFIS), was used to evaluate its capabilities in terms of predicting surface settlement. Among generated ANFIS models, the model with all input parameters considered produced the best prediction, so as its associated R2 in the test phase was obtained to be 0.957. The equations and models in which operational factors were taken into consideration gave better prediction results indicating larger relative effect of such factors. For sensitivity analysis of ANFIS model, cosine amplitude method (CAM) was employed; among other dependent variables, fill factor of grouting (n) and grouting pressure (P) were identified as the most affecting parameters.

Detecting of joint sets (clusters) is one of the most important processes in determining properties of fractures. Joints clustering and consequently, determination of the mean value representing each cluster is applicable to most rock mass studies. It is clear that the accuracy of the clustering process plays a key role in analyzing stability of infrastructures such as dams and tunnels and so on. Hence, in this paper, by reviewing several methods proposed for clustering fractures and considering their advantages and disadvantages, a three-stage hybrid method is developed which contains Fuzzy c-means, Fuzzy covariance and Fuzzy maximum likelihood estimation that by utilizing the modified orientation matrix had been optimized. This method is optimized by the Differential Evolutionary algorithm using a new and strong cost function which is defined as the computation core. In addition, using three clustering quality comparing criteria, the new developed method of differential evolutionary optimized of fuzzy cmeans - fuzzy covariance - fuzzy maximum likelihood estimation clustering method (DEF3) is compared with other base and common methods using field data. After doing the calculations, the developed method by giving the best values for all the criteria provided the best results and good stability in meeting different criteria. The DEF3 method was validated using actual field data which mapped in Rudbar Lorestan dam site. The results revealed that DEF3 acquired the best rank among the other method by getting the value of 0.5721 of Davis-Bouldin criterion, 1403.1 of Calinski-Harabasz criterion, and 0.83482 of Silihotte as comparing criteria of clustering methods.

Fractures are amongst the most important factors determining the behavior of rock masses. The lack of direct access to the real fractures’ distribution leads to the use of indirect methods for their study. The generation of the discrete fracture network is one of the most common indirect methods. Fractures’ shape is one of the most important properties that the simulation of which is of great help in studying the fracture network. A wide range of shapes which include from infinite plates to elliptical shape have been suggested for the simulation of fractures. The present paper makes use of the data collected from the pumped-storage power plant and dam project in Roudbar, Lorestan Province, to provide circular and elliptical network of discrete fractures; moreover, the parameters as density, intensity and the real mean value of the length of the fracture traces have been used to evaluate the effect and accuracy of the selected shapes. Following the implementation of the proposed method, it was made clear that the elliptical discrete fracture network, with a mean error value of 13.5 %, outperforms the circular disk discrete fracture network, with a mean error value of 27.5 %; hence, it was found providing more accurate results for the description of the fractures network in the aforementioned region.

This paper presents a numerical method for the modeling of hydraulic confinement and hydrocarbon migration around the unlined rock caverns (URCs) by directly applying distinct fracture network (DFN) concept. A "migration tracing" algorithm for the assessment of hydrocarbon migration around URCs was developed based on the applying migration cessation criterion and pathway analysis in the DFN realizations. The veracity of the developed numerical method was explored by predicting the hydrocarbon migration in a uniform fracture network around an unlined cavern that consists with results obtained from the finite element continuum fluid flow analysis. Finally, the applicability of the proposed method was evaluated by simulation of hydrocarbon migration in the DFN realizations around a URC. The results demonstrate that the hydrocarbon migration is sensitive to the hydraulic boundary conditions, and the geometrical properties of fractures. Establishing sufficient water pressure in the fracture system controls hydrocarbon migration; however, the effect of external water pressure on the hydrocarbon migration is controlled by the fractures geometry. Consequently, local migration paths may develop around storage caverns through the intricately connected fracture network, despite the presence of high pressurized water curtain. The proposed method may prove useful for better design analysis of hydraulic confinement around URCs, or inclusions in simulators for computational demands. A numerical method was developed for Hydrocarbon migration around URCs.The concept of DFN was directly applied for hydrocarbon migration analysis.Migration analysis is based on the tracing algorithm and pathway analysis in DFN.The role of hydraulic confinement on the hydrocarbon migration was investigated.The suggested method can be incorporated in simulators for computational demands.

Roadways stability in longwall coal mining is critical to mine productivity and safety of the personnel. In this regard, a typical challenge in longwall mining is to predict roadways stability equipped with a reliable support system in order to ensure their serviceability during mining life. Artificial neural networks (ANNs) were employed to predict the stability conditions of longwall roadways based on roof displacements. In this respect, datasets of the roof displacements monitored in different sections of a 1.2-km-long roadway in Tabas coal mine, Iran, were set up to develop an ANN model. On the other hand, geomechanical parameters obtained through site investigations and laboratory tests were introduced to the ANN model as independent variables. In order to predict the roadway stability, these data were introduced to a multilayer perceptron (MLP) network to estimate the unknown nonlinear relationship between the rock parameters and roof displacements in the gate roadways. A four-layer feed-forward backpropagation neural network with topology 9-7-6-1 was found to be optimum. As a result, the MLP proposed model predicted values close enough to the measured ones with an acceptable range of correlation. A high conformity (R 2 = 0.911) was observed between predicted and measured roof displacement values. Concluding remark is the proposed model appears to be a suitable tool for prediction of gate roadways stability in longwall mining.

This paper presents a framework for the near-field stochastic discontinuum modeling and uncertainty analysis of groundwater inflow into underground excavations by direct utilization of discrete fracture network (DFN) concept. The sources of uncertainty in the groundwater inflow into underground excavations in fractured rocks were classified into two different groups including the geometrical and hydraulic properties of fractures. The main input data for stochastic discontinuum modeling of groundwater inflow were captured from site investigations in Siah Bisheh pumped storage project in Iran. Detailed measurements of groundwater inflow into powerhouse and transformer caverns provided the possibility to determine the hydraulic aperture through back calibration. The validity of calibrated hydraulic aperture was explored by simulation results of the groundwater inflow into transformer cavern, and shows high accuracy when compared with data obtained from field measurements. The statistical results of these groundwater flow simulations with constant calibrated hydraulic aperture reflected the uncertainty associated with geometrical properties of fractures. Finally, the role of hydraulic properties of fractures on the uncertainty of groundwater inflow was investigated by the variation of standard deviation of hydraulic aperture through the sensitivity analysis. The results of this study demonstrated that the geometrical properties of fractures did much greater uncertainty in the groundwater inflow into underground excavations than hydraulic properties. Moreover, it was found that both the mean and standard deviation of simulated groundwater inflow into underground excavations decrease non-linearly by increasing the standard deviation of hydraulic aperture even though it is generally anticipated that the uncertainty of hydrogeological systems increases by increasing the variance of hydraulic parameters.

Calculation of the blasting costs plays a significant role in blast pattern design and reduction of the final extraction cost of minerals. Blasting costs include drilling costs, blasting materials costs, and additional costs of blasting operations. We assessed information from three copper mines in Iran, and found that there is a significant relationship between blasting costs and hole diameter. A relationship was derived to calculate blasting costs per cubic metre as a function of hole diameter, bench height, uniaxial compressive strength, joint set orientation, the cost of drilling, and the unit cost of explosives. This model will enable engineers to estimate blasting costs prior to designing the blast pattern. Based on the model, an increase in the rock strength and the angle between the bench face and the main joint set will increase the blasting cost. On the other hand, the costs will decrease when the hole diameter increases for every range of uniaxial compressive strength.

There are various methods to predict the settlement caused by shallow tunneling, with each method having particular strengths and weaknesses. However, the most important weakness of common methods is the failure to consider all parameters contributing into the settlement. Nowadays, earth pressure balance machines (EPBMs) are commonly applied for tunneling into soft grounds. In this tunneling method, many parameters affect resultant surface settlement, it difficult to estimate the settlement by traditional methods. Soft computing, however, can be devised to cope with such engineering limitations. The aim of this study is to evaluate the ability of the soft computing methods of neuro-genetic system (NGS), adaptive neuro-fuzzy inference system (ANFIS), and gene expression programming (GEP) to predict maximum surface settlement (S max) caused by tunneling in Shanghai Subway LRT Line 2 project. For this purpose, S max is considered as a function of geometric, strength and operational factors, with the factors combined using different methods to reconstruct different models. The results showed that the models with operational factors outperformed other models. Among tested methods, ANFIS and NGS presented the best and the worst forecasts, respectively. With respect to the results of this research, it can be said that, despite the fact that GEP had lower accuracy in comparison to ANFIS, it represented the most suitable method to estimate S max , because of providing useful mathematical equations.

In this paper tunnel probabilistic structural analysis (TuPSA) was performed using the first order reliability method (FORM). In TuPSA, a tunnel performance function is defined according to the boundary between the structural stability and instability. Then the performance function is transformed from original space into the standard normal variable space to obtain the design point, reliability index, and also the probability of tunnel failure. In this method, it is possible to consider the design factors as the dependent or independent random parameters with arbitrary probability distributions. A software code is developed to perform the tunnel probabilistic structural analysis (TuPSA) using the FORM. For validation and verification of TuPSA, a typical tunnel example with random joints orientations as well as mechanical properties has been studied. The results of TuPSA were compared with those obtained from Monte-Carlo simulation. The results show, in spite of deterministic analysis which indicates that the rock blocks are stable, that TuPSA resulted in key-blocks failure with certain probabilities. Comparison between probabilistic and deterministic analyses results indicates that probabilistic results, including the design point and probability of failure, are more rational than deterministic factor of safety.

Estimation of rock load is very important parameter to design the support system because it is a function of many parameters such as stress magnitude, rock mass behavior, excavation method and etc. Several methods are used to estimate this parameter such as experimental, empirical and numerical methods. In this study based on the actual collected data from five tunnels in non squeezing ground condition, a new empirical method is proposed to estimate the rock load with considering the post failure behavior of rock mass and it is estimated using the drop to deformation modulus ratio (was named η). Finally the relation between the rock load and the drop to deformation modulus ratio, η, in non squeezing ground condition is estimated. Based on the statistical analysis, the maximum correlation between both parameters is achieved using of Eqs. 9–11 to estimate the drop modulus. It is cleared that the amplitude of η, is high and to increase the correlation between mentioned parameters, the classification of data is performed in two methods, in the first method, all data is classified in two classes such as $$\eta \le 0.1$$ and $$\eta > 0.1$$ and in the second method, all data is classified in five classes [according to the proposed classification by Hoek and Brown (1997) and Osgoui and Unal (2009)] as very weak (GSI 0.1) is higher than other equations (including five classes) so it is proposed that the mentioned equations are used to estimate the rock load.

Ground vibration around mining areas is one of the undesirable phenomena of production blasts. To predict vibration magnitude due to production blasts in Sarcheshmeh Copper Mine, the seismic wave characteristics are recorded in two parts of south-east and south-west mine by seismometers. The blasts of each part are grouped by cluster analysis, and using multivariate analysis for each of the groups, models were defined to predict peak particle velocity (PPV). For model validation, first PPV values of some recorded experimental blast in the mine was predicated and the root mean squared error (RMSE) and mean absolute error were calculated for the cases of after- and before-grouped seismic data. In the south-west region of the mine, the RMSE is calculated after and before clustering the data 8.9 and 11.95, respectively. These values are shown; the created models after clustering in comparison with before clustering could decrease the rate of error up to 3. This rate of error and the value of mean absolute error are shown; the performance of created models is improved after the cluster analysis predicated the PPV. But in the south-east region of the mine, the value of RMSE by means of created models after clustering the data is increased in comparison with before clustering up to 0.22. This shows that the performance of models after clustering is decreased.

Various analytical models have developed to determine fire flame length under tunnel ceilings during fire emergency based on the regression and dimensional analysis. Artificial intelligence techniques are now being used as an alternate to statistical techniques. In this study, the artificial neural network (ANN) is applied to forecast fire flame length in tunnels. Moreover, particle swarm optimization algorithms were used for ANN training in order to overcome very slow convergence and easy entrapment in a local minimum of back propagation training algorithms. The model predicts flame length using Fire Heat Release Rate, Air velocity, Tunnel Width, Tunnel Height and Tunnel Cross Section. The predictive PSO-ANN model was implemented on the MATLAB and developed based on a database including 44 data sets from large scale fire test programs. The coefficient of determination (R2), the variance account for (VAF) and the root mean square error (RMSE) were calculated to check the prediction performance of the model. The R2, VAF and RMSE indices were obtained as95.884, 99.86% and 1.05.These indices revealed that the developed model is suitable for practical use in tunnels.

The purpose of this study was to investigate the effect of bolt profile on load transfer mechanism of fully grouted bolts in jointed rocks using analytical and numerical methods. Based on the analytical method with development of methods, a new model is presented. To validate the analytical model, five different profiles modeled by ANSYS software. The profile of rock bolts T3 and T4 with load transfer capacity, respectively 180 and 195 kN in the jointed rocks was selected as the optimum profiles. Finally, the selected profiles were examined in Tabas Coal Mine. FLAC analysis indicates that patterns 6+7 with 2NO flexi bolt 4 m better than other patterns within the faulted zone. Keywords: Fully grouted bolt, Load transfer mechanism, Jointed rocks, Analytical and numerical methods

The scrutiny of health and safety of personnel working in underground coal mines is heightened because of fatalities and disasters that occur every year worldwide. A methodology based on fuzzy TOPSIS was proposed to assess the risks associated with human health in order to manage control measures and support decision-making, which could provide the right balance between different concerns, such as safety and costs. For this purpose, information collected from three hazardous coal mines namely Hashouni, Hojedk and Babnizu located at the Kerman coal deposit, Iran, were used to manage the risks affecting the health and safety of their miners. Altogether 86 hazards were identified and classified under eight categories: geomechanical, geochemical, electrical, mechanical, chemical, environmental, personal, and social, cultural and managerial risks. Overcoming the uncertainty of qualitative data, the ranking process is accomplished by fuzzy TOPSIS. After running the model, twelve groups with different risks were obtained. Located in the first group, the most important risks with the highest negative effects are: materials falling, catastrophic failure, instability of coalface and immediate roof, firedamp explosion, gas emission, misfire, stopping of ventilation system, wagon separation at inclines, asphyxiation, inadequate training and poor site management system. According to the results, the proposed methodology can be a reliable technique for management of the minatory hazards and coping with uncertainties affecting the health and safety of miners when performance ratings are imprecise. The proposed model can be primarily designed to identify potential hazards and help in taking appropriate measures to minimize or remove the risks before accidents can occur.