رضا میکائیل

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.

Specific energy is a key indicator of drilling performance to consider in the feasibility and economic analyses of drilling projects. Any improvement in the specific energy of a drilling operation may reflect an improvement in the overall efficiency of drilling operations. This improvement can be achieved by delivering a suitable cooling lubricant into the drilling environment. The present study examines the mechanical characteristics of the drilled rock, the physical qualities of the cooling lubricant employed, and the drilling rig operational parameters related to the drilling-specific energy (DSE). To this end, seven rock samples (granite, marble, and travertine) were drilled using water and five other fluids as the cooling lubricants. A total of 492 drilling experiments were conducted with a custom-designed and built laboratory-scale drilling rig on cuboid rock specimens. The univariate linear regression analysis of experimental results revealed a significant drop in DSE after using cooling lubricants instead of conventional cooling fluid (i.e. water). Under constant conditions in terms of mechanical properties of the rock, using Syncool with a concentration of 1:100 and soap water with a concentration of 1:120 instead of water led to 34% and 43% DSE reductions in the granite samples, 48% and 54% in the marble samples, and 41% and 50% in the travertine samples, respectively. These variations in specific energy suggest that the drilling efficiency and performance can be augmented using properly selected cooling lubricants.

The hydraulic properties of the rock masses are of great importance in analyzing the behavior and stability of the structures constructed on or in rock mass. Permeability is key parameter among other rock mass features due to its important role in rock mass overall behavior. According to aforementioned reason, numerous efforts have been made by researchers in the field of rock mechanics for its obtaining. To access the rock masses’ permeability, in-situ test methods and simulation techniques could be used. In-situ tests like Lugeon Test are time-consuming and costly and they provide local results. Simulation base methods calculate the permeability of the model that is generated similar to the real region indeed and the developing the results to the real condition always raises substantial challenges. according to the aforementioned reason, direct acquiring of permeability with optimum cost and time which is easily generalizable to the overall of a region would be very important. In this work using crack tensor concept, permeability tensor of Lorestan’s Rudbar dam cavern is calculated efficiently by considering rock mass structural features. Resulted permeability of the power plant’s cavern was obtained equal to  that seems to be acceptable compared to the measured values which is obtained  9/87×10-7 m/s.

One of the criterions for evaluating the cutting of building stone using the diamond wire cutting system is the energy consumption of the cutting wire machine. In this study, in order to evaluate the amount of energy consumed in the cutting process, a laboratory diamond cutting wire system was designed and built with the ability to change and stabilize the operational and tooling parameters of cutting, including linear velocity, backward pulling speed and angle of impact of diamond wire. In order to determine the relationship between the physical and mechanical properties of the stone with the energy consumption of the cutting machine, 6 different samples of travertine were prepared from the mines of East and West Azerbaijan, Isfahan, Yazd and Markazi provinces and The values of physical and mechanical properties including density, porosity, water absorption, uniaxial compressive strength, tensile strength and the speed of ultrasonic waves passed through them were measured in the laboratory. Then, by cutting cubic blocks of the samples with the laboratory diamond cutting wire system, the energy consumption of the device in different situations of the angle of the wire with the sample was recorded. Using regression and univariate fitting, the relationship between each of the rock properties and the specific cutting energy with respect to linear, logarithmic, power and exponential functions was investigated. The results showed that the best value for the angle at which the wire hits the specimen, at which the lowest specific energy consumption is recorded, was 15 degrees.

Iran is one of the countries with the largest number of quarry mines in the world. Diamond cutting wire is usually used in quarries to cut dimension stone cubes, which is accompanied by hazardous events. Therefore, detecting and investigating the possible quarry risks is crucial to have a safe and sustainable mining operation. In mine exploitation, maintaining the safety of vehicles and increasing the knowledge of personnel regarding safety issues can considerably mitigate the number or radius of effect of hazards. Hence, the incidents and risks in the West-Azerbaijan quarries in Iran are investigated in this work. To do so, a list of the hazards and their descriptions are first prepared. Then the hazard risk rating is conducted using the Failure Modes and Effects Analysis (FMEA) method. The number of priorities is calculated for each incident based on probability, intensity, and risk detection probability. Finally, the main causes of risks in the studies quarries are identified. The results obtained show that the most likely dangers in dimensional stone mines in West Azerbaijan are diamond cutting wire breaking, rock-fall, and car accidents, with the priority numbers of 216, 180, and 135, respectively. These hazards can be mitigated by applying some preservative activities such as timely cutting wire replacement, utilizing an intelligent system for cutting tool control, necessary personal training, and considering some preservative points.

The noise of drilling in the dimension stone business is unbearable for both the workplace and the people who work there. In order to reduce the negative effects drilling has on the health of the environment, the drilling noise has to be measured, assessed, and controlled. The main purpose of this work is to investigate an experimental-intelligent method to predict the noise value of drilling in the dimension stone industry. For this purpose,135 laboratory tests are designed on five types of rocks (four types of hard rock and one type of soft rock), and their results are measured in the first step. In the second step, due to the unpredicted and uncertain issues in this case, artificial intelligence (AI) approaches are applied, and the modeling is conducted using three intelligent systems (IS), namely an adaptive neuro-fuzzy inference system-SCM (ANFIS-SCM), an adaptive neuro-fuzzy inference system-FCM (ANFIS-FCM), and the radial basis function network (RBF) neural network. 75% of the samples are considered for training, and the rest for testing. Several models are constructed, and the results indicate that although there is no significant difference between the models according to the performance indices, the proposed construction of ANFIS-SCM can be considered as an efficient tool in the evaluation of drilling noise. Finally, several scenarios are designed with different input modes, and the results obtained prove that the types of rock and the drill bits are more important than the operational characteristics of the machine.

Predicting the amperage consumption of cutting machines could be one of the critical steps in optimizing the energy-consuming points for the dimension stone cutting industry. Hence, the study of the relationship between the operational characteristics of cutting machines and rocks with focusing on the machine's energy-consuming is unavoidable. For this purpose, in the first step, laboratory studies under different operating conditions at different cutting depths and feed rates are performed on 12 soft and hard rock samples. In the continuation of the laboratory studies, the rock samples are transferred to the rock mechanics laboratory in order to determine the mechanical properties (uniaxial compressive strength and modulus of elasticity). The statistical studies are performed in the SPSS software in order to predict the electrical current consumption of the cutting machine according to the mechanical characteristics of the rock samples, cutting depth, and feed rate. The statistical models proposed in this work can be used with a high reliability in order to estimate the electrical current consumed in the cutting process.

Nowadays, many of Iran's dimensional stone quarries are affected by safety hazards, economic risks (such as: market demand decline, reducing of the production quality, increasing of production costs and raising of the final price of the product). This has led to a decline in production capacity or the abandonment of dimensional stone mines. So study these problems and safety hazards is very necessary to improve the status of mines and increase exports rates. The current study is a new approach to introducing economic problems alongside safety hazards for dimensional stone mines of West-Azerbaijan province. It also offers solutions to these problems. In this study, the safety and economic hazards were first identified. Then, the risk severity, probability of occurrence and probability of risk in 10 dimensional stone mines of West-Azerbaijan province were assessed by completing a questionnaire. The risk scores of each risk were determined using the FMEA risk assessment method. The results showed that volatility in mining activities laws, mineral prices, housing market, fixed capital, working capital, financing, renting of extractive products to processing plants and the impact of policy on mining economy including the risk is over 80 (high risk area). Of course, by formulating and implementing the right policies we can guide the construction stone industry to a safe margin from an economic point of view.

Besides stone perishing, generated vibrations during stone cutting process can lead to bending, deflection and finally breakage of cutter disks. Therefore, study and predicting vibrations, is unavoidable in stone cutting process. In this research, by means of laboratory and statistically analysis, a study is carried out to predicting vibration of stone cutting process. For this purpose, a stone cutting machine in laboratory scale which equipped with vibration measurement tools, was provided. On a carbonate rock specimen, the study is carried out under different circumstance such as different practical cut depth (15, 22, 30, 35 mm), different cut advance rate (100, 200, 300, 350, 400 cm/min), different cutter disk rotation speed (1540, 1770, 2550 rpm). Results indicate that increasing in cut depth and cut advance rate, lead to increase in vibration intensity. Vibration intensity quantity even increased 6 times, when cut advance rate varies from 100 to 400 cm/min. Using SPSS software, after laboratory result investigation and statistically data analysis, vibration signal predicting statistical models are presented. Results of statistical tests indicates that the best fitting models enable predicting vibration quantity accurately and precisely with correlation coefficient range of 0.92 - 0.87, confidence level of 90% and error less than 10%.

In most rock drilling operations, the low rate of penetration (ROP) can be primarily attributed to the presence of the cuttings produced during drilling and the thermal stresses caused by friction at the bit-rock interface, which can be exacerbated with the increasing strength, hardness, and abrasivity of the drilled rock. In order to improve ROP, drill bit lifetime, and cutting power, it is necessary to minimize the process forces due to the mechanical bit-rock interaction and the thermal stresses generated in the drill hole. Any improvement in these areas is extremely important from both the technical and the economic perspectives. This improvement can be achieved by the use of appropriate cooling/lubricating fluids in the drilling process in order to increase ROP, reduce the temperature of the drilling environment, and create a clean drill hole free of cuttings. In this work, a series of laboratory drilling tests are performed to investigate and compare ROP in the drilling of seven samples of hard and soft rock in the presence of six different cooling-lubricating fluids. The drilling tests are performed on the cubic specimens with a laboratory-scale drilling rig at several different rotation speeds and thrust forces. The statistical analyses are performed in order to investigate the relationship between ROP and the mechanical properties of the rock, properties of the fluid, and machining parameters of the drilling rig. These analyses show that under similar conditions in terms of mechanical properties of the rock using Syncool with a concentration of 1:100 and soap water with a concentration of 1:120 instead of pure water leads to the average 31% and 37% increased ROP in granite, 36% and 43% increased ROP in marble, and 47% and 61% increased ROP in travertine, respectively. These results demonstrate the good performance of these cooling/lubricating fluids in increasing ROP.

In this paper vibration of cutting machine during sawing carbonate and granite ornamental stones was investigated through making a cutting machine on a laboratory scale. For this purpose, properties 7 samples of carbonate stones and 5 samples of granite stones were measured and 211 sawing experiments were performed. Predictives models were developed using different variation of physical and mechanical parameters by incorporating statistical and intelligent methods. The performance of the developed models was evaluated using R2, RMSE, MAPE and VAF criteria for two different types of test datasets consists of Type A and B; data type A included data for rock samples available at the learning stage and data type B included data for rock samples not available in the training phase. The best model for each group of rocks was introduced by taking ranking strategies, evaluation criteria, speed, easiness and reliability of developing method into account. Results indicated that the best model for both rock type was in the form of multivariate nonlinear regression. The similar parameters of these models were depth of cut, feed rate and Schmiazek abrasivity factor. In addition, Young’s modulus and UCS were the special parameters in the carbonate and granite rock models, respectively. These special parameters were in accordance with the finding of sensitivity analysis results.

One of the important steps in designing and implementation of tunneling projects is analyzing and managing the risks from the viewpoint of geotechnical risks. Second part of the Emamzadeh Hashem tunnel is one of the greatest civil projects in Iran that has faced with serious and important challenges in design because of the poor geological structure and geotechnical condition in excavation track. In this research, try to be after the geological studying in the tunnel excavation track, the overall risk amount be assessed by fault tree analysis. During this study, 4 important event including damage to the personnel, damage to the TBM, damage to the restraint system and deflection from the excavation track was identified and studied by using the geotechnical hazards like: encounter to fault and comminuted areas, squeezing, water inrush and tunnel instability. Results of the analysis show that reasons of the higher risk in this project are damage to the TBM and damage to restraint system. So, the higher risk should be reduced by doing some reducer proceedings like concrete injection. After these proceedings, the overall risk came to the poor area in the risk classification. According to the results, in this project, damage to the TBM and damage to restraint system have the highest risks and their risks should be reduced by doing some reducer proceedings like concrete injection. After these proceedings, the overall risk came to the poor area in the risk classification.

The complete knowledge of the cutting process and the performance of the sawing machine can help increase the efficiency and quality of the manufactured product.  During the field studies, 12 rock samples from two kinds of ornamental stones were collected and some major mechanical properties including uniaxial compressive strength (UCS), Mohs hardness (Mh), Schimazek’s F-abrasiveness factors (SF-a), and young modulus (YM) were measured. Also, Fuzzy C-means has been used as a soft computing technique in this study. All different studied rock samples are classified into 3, 4, and 5 classes. Then, the results of classification were compared with consumed energy values. Finally, it can be concluded that FCM could be a simple but efficient tool in the evaluation of the sawability of ornamental stone as one of the most important factors to estimation and prediction of production cost and productivity of processing plants.

The rock sawability and prediction of consumed energy is one of the most important factors in the estimation and prediction of production cost and productivity of processing plants. In this study, for laboratory tests, some rock blocks were collected from the studied quarries. Then, by using the fuzzy C-means clustering approached and considering laboratory results of rock samples, all of the rocks have been classified in 3, 4, and 5 separate clusters. The results clearly showed that the FCM algorithm was used as a reliable and efficient tool for classifying the ornamental stone.  

Nowadays, with the increasing growth of uncertain problems, the use of soft computing techniques with high ability in solving these kinds of problems has increased significantly. Assessment of the sawability of ornamental stone is the most important factor in the identification and prediction of production cost and productivity of processing plants. Given the unreliability of all experimental laboratory, one of the most important tasks in the sawability of ornamental stone’ classification is using a method with the highest possible accuracy. According to the importance of the issue, in this study, the Fuzzy C-means algorithm is used for the classification. Four important physical and mechanical characteristics of 12 rock samples are considered for the classification of sawability of ornamental stone from two kinds of ornamental stone, including hard and soft rock groups using Fuzzy C-means Optimization, including uniaxial compressive strength, Mohs hardness, Schmiazek F-abrasivity factor, and Young's modulus. Finally, in this study, the results of the classification are compared with consumed energy.

In this study employing Fuzzy C-means as soft computing technique and some major mechanical properties such as uniaxial compressive strength, Mohs hardness, Schmiazek F-abrasivity factor, and Young's modulus to evaluate the sawability of ornamental stone, 3 to 5 classes are considered. Generally, according to this research, the following remarks can be concluded: 1- The 12 rock samples from two kinds of ornamental stones, including hard and soft rock groups, are evaluated and tested. 2- A comparison was made between 3 different models (3 classes from 3 to 5 classes) with consumed energy of the sawing machine. The results show that all 12 rock samples are classified as very suitable. 3- In comparison to all classes, it can be concluded that FCM is a reliable technique for clustering the sawability of ornamental stone with highly acceptable degrees of robustness.

The blasting method is one of the most important operations in most open-pit mines that has a priority over the other mechanical excavation methods due to its cost-effectiveness and flexibility in operation. However, the blasting operation, especially in surface mines, imposes some environmental problems including the ground vibration as one of the most important ones. In this work, an evaluation system is provided to study and select the best blasting pattern in order to reduce the ground vibration as one of the hazards in using the blasting method. In this work, 45 blasting patterns used for the Sungun copper mine are studied and evaluated to help determine the most suitable and optimum blasting pattern for reducing the ground vibration. Additionally, due to the lack of certainty in the nature of ground and the analyses relating to this drilling system, in the first step, a combination of the imperialist competitive algorithm and k-means algorithm is used for clustering the measured data. In the second step, one of the multi-criteria decision-making methods, namely TOPSIS (Technique for Order Performance by Similarity to Ideal Solution), is used for the final ranking. Finally, after evaluating and ranking the studied patterns, the blasting pattern No. 27 is selected. This pattern is used with the properties including a hole diameter of 16.5 cm, number of holes of 13, spacing of 4 m, burden of 3 m, and ammonium nitrate fuel oil of 1100 Kg as the most appropriate blasting pattern leading to the minimum ground vibration and reduction of damages to the environment and structures constructed around the mine.

One of the most crucial factors involved in the optimum design and cost estimation of rock sawing process is the rock abrasivity that could result in a significant cost increase. Various methods including direct and indirect tests have been introduced in order to measure rock abrasivity. The Schimazek’s F-abrasiveness factor ( ) is one of the most common indices to assess rock abrasivity.  is the function of three rock parameters including the Brazilian tensile strength ( ), median grain size ( ), and equivalent quartz content ( ). By considering its formulation, it has been revealed that the coefficient of each parameter is equal, which is not correct because each parameter plays a different role in the rock abrasion process. This work aims to modify the original form of  by introducing three correction factors. To calculate these correction factors, an integrated method based on a combination of the statistical analysis and probabilistic simulation is applied to a dataset of 15 different andesite rocks. Based on the results obtained, the values of -0.36, 0.3, and -0.89 are suggested as the correction factors of ,  and , respectively. The performance of the modified Schimazek’s F-abrasiveness factor ( ) is checked not only by the wear rate of diamond wire but also by the cutting rate of the wire sawing process of Andesite rocks. The results obtained indicate that the wear rate and cutting rate of andesite rocks can be reliably predicted using . However, it should be noted that this work is a preliminary one on the limited rock types and further studies are required by incorporating different rock types.

Predicting the ampere consumption in carbonate rock sawing process is very important for the determination of the electrical energy cost per unit of production. In addition, ampere consumption prediction may be used for selecting the optimum operation parameters to obtain high production rate. In this study, it is aimed to develop fuzzy classification systems to evaluate and classify the carbonate rock based on physical and mechanical properties such as Brazilian tensile strength, equal quartz content, grain size, uniaxial compressive strength, Young modulus and Mohs hardness. Varieties of seven carbonate rocks such as Azarshahr travertine, Hajiabad travertine, Dare-bukhari travertine, Harsin marble, Salsali marble, Anarak marble and Haftooman marble were classified by developed fuzzy classification system. To validate the classification’s results, ampere consumption was recorded during sawing process for each studied rocks. The results of the study show that the three-class fuzzy classification system is capable to evaluate the carbonate rock saw-ability and ampere consumption during soft dimensional stone sawing process.

Nowadays, very important issues are considered by geological and mining engineers in extraction process of minerals. The important goals are: productivity and high production rates, improve working conditions and lower operating costs. The most suitable option to achieve these goals is to replace the traditional extraction methods with full-mechanized methods. The mechanization of the extraction operations is time consuming process and it requires high investment costs. So, cause special sensitivity in all design and implementation phases. Therefore, it is important to design, investigate and identify the capability of mechanization of coal layers in the first stage. For this purpose, the engineering capabilities of the coal layers of different mines should be analyzed that is important and necessary to achieve the above goals. Therefore, criteria affecting the mechanization of coal layers were identified. Then, 17 Stopes were studied that located in the Eastern Alborz Basin and Tabas mine. The combined used method to evaluate mechanization capability is combination method of Fuzzy set theory and FDAHP. The results of the fuzzy classification system presented in current study show that the T layer of Tabas has a good quality, the K11 layer of the Takht mine is located in the middle class and other layers have not qualify for mechanization properly. In the following, the results of this study were validated with current mining conditions. The results of the study showed that the multifactorial fuzzy classification system can reliably use to classify the mechanization of coal layers based on geological conditions.

The longwall method is the most commonly method used in extraction of coal layers. The mining review records shows that the majority of the dangers of coal underground mines related to the sudden and uncontrolled roof fall-out of the stopes. Predicting the behavior and the ability to destroy the roof rock is very important for the successful execution of longwall method and reducing its risks. In this study, we will present a new fuzzy classification system by using the theory of fuzzy sets and fuzzy multi-criteria approach to evaluate the roof quality of the coal mines. 

In the first step, 8 parameters were selected as effective factors in the classification system. This selection was performed after reviewing the parameters affecting the roof quality of coal mines. The parameters include: Uniaxial Compressive Strength (UCS), Brazilian Tensile Strength (BTS), moisture sensitivity, spacing and continuity of discontinuities, thickness of layers, joints slope and difference of stopes’ and critical joints’ orientation.
 

Fuzzy classification system was presented after determining the parameters weight. The fuzzy classification system can evaluate and categorize the roof quality of coal mines. The classification qualitatively classified the quality of roofs in 3 classes (good, fair and poor). 

In order to evaluate the performance of the fuzzy classification system data of to 15 stopes in Eastern Alborz coal mines were evaluated. The results of this study showed that we can evaluate the quality of the roof rock with fuzzy classification system with high reliability in for coal mines roofs.

Prediction of the production rate of the cutting dimensional stone process is crucial, especially when chain saw machines are used. The cutting dimensional rock process is generally a complex issue with numerous effective factors including variable and unreliable conditions of the rocks and cutting machines. The Group Method of Data Handling (GMDH) type of neural network and Radial Basis Function (RBF) neural network, as two kinds of the soft computing method, are powerful tools for identifying and assessing the unpredicted and uncertain conditions. Hence, this work aims to develop prediction models for estimating the production rate of chain saw machines using the RBF neural network and GMDH type of neural network, and then to compare the results obtained from the developed models based on the performance indices including value account for, root mean square error, and coefficient of determination. For this purpose, the parameters of 98 laboratory tests on 7 carbonate rocks are accurately investigated, and the production rate of each test is measured. Some operational characteristics of the machines, i.e. arm angle, chain speed, and machine speed, and also the three important physical and mechanical characteristics including uniaxial compressive strength, Los Angeles abrasion test, and Schmidt hammer (Sch) are considered as the input data, and another operational characteristic of the machines, i.e. production rate, is considered as the output dataset. The results obtained prove that the developed GMDH model is able to provide highly promising results in order to predict the production rate of chain saw machines based on the performance indices.

 Tunneling under the water table is a compatible condition for projects. Most important consequences of tunneling in this situation are water inrush, bad working condition, mudflow, low advance rate, and humans injuring. Especially for TBM tunneling that required high investments funds, these hazards can impose high costs for a project. This paper estimates water inflow by the finite element method in the second part of Emamzade Hashem tunnel and providing risk management for these hazards using the FMEA method. 

The second part of Emamzade Hashem tunnel is the longest road tunneling project in Iran that will be excavated by a TBM. TBM tunneling in hard rock and faulty zone have lots of hazards that may cause many stops in the project according to previous similar projects around the world. Risk management could classify hazards and make them predictable and controllable.

In this paper, water flow ratio was estimated by numerical simulation with FLAC 2D. In the following, the classical risk of water inrush was calculated by the FMEA method with studding some similar projects. A fuzzy analysis was then applied to increase the accuracy of the results. To this end, after estimating water inflow for each section of the tunnel, the time and costs of each hazard were calculated by studying similar projects. Risk number was estimated and managed through the FMEA method based on the calculations. The reduction actions were suggested for the hazards with high-risk numbers.

The results of the study indicated that tunneling in the middle part of the tunnel has high risk due to high water pressure according to classical and fuzzy analysis. It has resulted that the performance of fuzzy analysis is higher than classical analysis.

Performance prediction of diamond wire saws and estimation of sawability are important in the cost estimation and the planning rock cutting projects. In this paper, the performance prediction of diamond wire saws in cutting carbonate rocks is considered on 14 different carbonate rocks in stone quarries. Uniaxial compressive strength (UCS), Brazilian tensile strength (BCS), Schmidt hammer value (SHv), Los Angeles (LA) abrasion and production rate (Ph) of diamond wire saws are determined in separated reference. The scope of this study is to evaluate the capability of two different methods in order to predict production rate of carbonate rock using diamond wire saw. A non-linear model presented by Ataei et al. and new proposed non-linear model are presented. An application of Imperialist Competitive Algorithm (ICA) is used to determine the non-linear production rate estimator coefficients. According to the calculated statistical error between the forecasted and real measured values of production rate, ICA-based model has the lower values of MAPE, MAE, VARE, MEDAE and RMSE than the previous model in the literature, while it has the higher value of VAF than the previous model in the literature. It is concluded that the production rate of carbonate rock using diamond wire saw can reliably be estimated using the developed model.

Today, one of the challenges in safety management of under-ground excavations is the water inflow into them. The tunnels and underground constructions are causing to remove a pile of soil and rock area and significant changes in the state of tension it’s around environment. Invasion of groundwater in-to the excavation area is a major problem in excavation of tunnels that is located below the hydrostatic levels. The un-derground-water inflow into the tunnels is an important issue in the tunnel engineering. The subsidence of the ground in hydrostatic levels, reduce the aqueduct water resources and springs that are located in downstream of the tunnel, are en-vironmental problems of the tunnel excavation operations. In the present study, the groundwater inflow into the Kouhin tunnel railway is modeling with FLAC2D software. The effects of groundwater inflows in the tunnel that while excavation are studied by numerical model. In other hands, initially in-vestigated the adverse environmental effects caused by the Kouhin railway tunnel excavation (subsidence the ground water levels and changes in the groundwater inflow in the region) and then provide some strategies for reducing these effects. The strategies that can be used in prevention of groundwater flow into the underground tunnels are: Under-ground water level recovery with application of concrete sealing operations without drainage and simultaneous use of forepoling methods that to be presented as a solution to re-duce environmental impact. Doing this requires using of con-crete lining with high strength properties to withstand hydro-static pressure of water collected on the back cover.

The process of pollutant adsorption from industrial wastewaters is a multivariate problem. This process is affected by many factors including the contact time (T), pH, adsorbent weight (m), and solution concentration (ppm). The main target of this work is to model and evaluate the process of pollutant adsorption from industrial wastewaters using the non-linear multivariate regression and intelligent computation techniques. In order to achieve this goal, 54 industrial wastewater samples gathered by Institute of Color Science & Technology of Iran (ICSTI) were studied. Based on the laboratory conditions, the data was divided into 4 groups (A-1, A-2, A-3, and A-4). For each group, a non-linear regression model was made. The statistical results obtained showed that two developed equations from the A-3 and A-4 groups were the best models with R2 being 0.84 and 0.74. In these models, the contact time and solution concentration were the main effective factors influencing the adsorption process. The extracted models were validated using the t-test and F-value test. The hybrid PSO-based ANN model (particle swarm optimization and artificial neural network algorithms) was constructed for modelling the pollutant adsorption process under different laboratory conditions. Based on this hybrid modeling, the performance indices were estimated. The hybrid model results showed that the best value belonged to the data group A-4 with R2 of 0.91. Both the non-linear regression and hybrid PSO-ANN models were found to be helpful tools for modeling the process of pollutant adsorption from industrial wastewaters.

One of the most significant and effective criteria in the process of cutting dimensional rocks using the gang saw is the maximum energy consumption rate of the machine, and its accurate prediction and estimation can help designers and owners of this industry to achieve an optimal and economic process. In the present research work, it is attempted to study and provide models for predicting the maximum energy consumption of the gang saw during the process of soft dimensional rocks with the help of an intelligent optimization model such as random non-linear techniques, i.e. the Hybrid ANFIS-DE and Hybrid ANFIS-PSO algorithms based upon 4 physical and mechanical parameters including uniaxial compressive strength, Mohs hardness, Schimazek’s F-abrasiveness factors, Young modulus, and an operational characteristic of the machine, i.e. production rate. During this research work, 120 samples are tested on 12 carbonate rocks. The maximum energy consumption of the cutting machine during this work is measured and used as a modeling output for evaluating the performance of cutting machine. Also meta-heuristic algorithms including DE and PSO algorithms are used for training the Adaptive Neural Fuzzy Inference System (ANFIS). In addition, the PSO algorithm has a higher ability in terms of model output and performance indices and has a superiority over the differential evolution algorithm. Furthermore, comparison between the measured datasets with the ANFIS-DE and ANFIS-PSO models indicate the accuracy and ability of the ANFIS-PSO model in predicting the performance of gang saw considering the machine’s properties and the cut rock.

Evaluation and prediction of performance of diamond wire saw is one of the most important factors involved in planning the dimension stone quarries. The wear rate of diamond wire saw can be investigated as a major criterion to evaluate its performance. The wear rate of diamond wire saw depends upon non-controlled parameters related to rock characteristics and controlled parameters related to characteristics of the cutting machine and operational parameters. Under the same working conditions, the wear rate of diamond wire saw is strongly affected by the rock properties. This is a key factor that required in evaluating the wear rate of diamond wire saw. In this work, the four major dimension stone properties uniaxial compressive strength, Schimazek F-abrasivity factor, Shore hardness, and Young's modulus were selected as the criteria to evaluate the wear rate of diamond wire saw using the harmony search algorithm (HSA). HSA was used to cluster the fifteen different andesite quarries located in Turkey. The studied dimension stones were classified into three classes. The results obtained show that the algorithm applied can be used to classify the performance of diamond wire saw according to its wear rate by only some famous physical and mechanical properties of dimension stone.