Mine Economic and Management
Hadi Fattahi; Hossein Ghaedi
Abstract
The maximum energy consumption of stone cutting machines is one of the important cost factors during the process of cutting construction stones. Accurately predicting and estimating the maximum energy consumption performance of the cutting machine, along with estimating the cutting costs, can help approach ...
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The maximum energy consumption of stone cutting machines is one of the important cost factors during the process of cutting construction stones. Accurately predicting and estimating the maximum energy consumption performance of the cutting machine, along with estimating the cutting costs, can help approach the optimal cutting operating conditions to reduce energy consumption and minimize machine depreciation. However, due to the uncertainty and complexity of building stone textures and properties, determining the maximum energy consumption of the device is a difficult and challenging task. Therefore, this paper employs the rock engineering system method to solve the aforementioned problem. To this end, 120 test samples were collected from a marble factory in the Mahalat region of Iran, representing 12 types of carbonate rocks. The input parameters considered for the analysis were the Mohs hardness, uniaxial compressive strength, Young's modulus, production rate, and Schimazek’s F-abrasiveness factors. In the study, 80% of the collected data, equivalent to 96 data points, were utilized to construct the model using the rock engineering system-based method. The obtained results were then compared with other regression methods including linear, power, exponential, polynomial, and multiple logarithmic regression methods. Finally, the remaining 20 percent of the data, comprising 24 data points, were used to evaluate the accuracy of the models. Based on the statistical indicators, namely root mean square error, mean square error, and coefficient of determination, it was found that the rock engineering system-based method outperformed other regression methods in terms of accuracy and efficiency when estimating the maximum energy consumption.
H. Fattahi; F. Jiryaee
Abstract
The surface settlement is an essential parameter in the operation of mechanized tunneling that should be determined before excavation. The surface settlement analysis caused by mechanized tunneling is a geo-technical problem characterized by various sources of uncertainty. Unlike the deterministic methods, ...
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The surface settlement is an essential parameter in the operation of mechanized tunneling that should be determined before excavation. The surface settlement analysis caused by mechanized tunneling is a geo-technical problem characterized by various sources of uncertainty. Unlike the deterministic methods, the reliability analysis can take into account the uncertainties for the surface settlement assessment. In this work, the reliability analysis methods (second-order reliability method (SORM), Monte Carlo simulation (MCS), and first-order reliability method (FORM)) based on the genetic algorithm (GA) are utilized to build models for the reliability analysis of the surface settlement. Specifically, for large-scale projects, the limit state function (LSF) is non-linear and hard to apply based on the reliability methods. In order to resolve this problem, the GMDH (group method of data handling) neural network can estimate LSF without the need for additional assumptions about the function form. In this work, the GMDH neural network is adapted to obtain LSF. In the GMDH neural network, the tail void grouting pressure, groundwater level from tunnel invert, depth, average penetrate rate, distance from shaft, pitching angle, average face pressure, and percent tail void grout filling are used as the input parameters. At the same time, the surface settlement is the output parameter. The field data from the Bangkok subway is used in order to illustrate the capabilities of the proposed reliability methods.
H. Fattahi; M. Hasanipanah; N. Zandy Ilghani
Abstract
The mechanical characteristics of rocks and rock masses are considered as the determining factors in making plans in the mining and civil engineering projects. Two factors that determine how rocks responds in varying stress conditions are P-wave velocity (PWV) and its isotropic properties. Therefore, ...
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The mechanical characteristics of rocks and rock masses are considered as the determining factors in making plans in the mining and civil engineering projects. Two factors that determine how rocks responds in varying stress conditions are P-wave velocity (PWV) and its isotropic properties. Therefore, achieving a high-accurate method to estimate PWV is a very important task. This work investigates the use of different intelligent models such as multivariate adaptive regression splines (MARS), classification and regression tree (CART), group method of data handling (GMDH), and gene expression programming (GEP) for the prediction of PWV. The proposed models are then evaluated using several error statistics, i.e. squared correlation coefficient (R2) and root mean squared error (RMSE). The values of R2 obtained from the CART, MARS, GMDH, and GEP models are 0.983, 0.999, 0.995, and 0.998, respectively. Furthermore, the CART, MARS, GMDH, and GEP models predict PWV with the RMSE values of 0.037, 0.007, 0.023, and 0.020, respectively. According to the aforementioned amounts, the models presented in this work predict PWV with a good performance. Nevertheless, the results obtained reveal that the MARS model yields a better prediction in comparison to the GEP, GMDH, and CART models. Accordingly, MARS can be offered as an accurate model for predicting the aims in other rock mechanics and geotechnical fields.
H. Fattahi
Abstract
The tensile strength (σt) of a rock plays an important role in the reliable construction of several civil structures such as dam foundations and types of tunnels and excavations. Determination of σt in the laboratory can be expensive, difficult, and time-consuming for certain projects. Due ...
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The tensile strength (σt) of a rock plays an important role in the reliable construction of several civil structures such as dam foundations and types of tunnels and excavations. Determination of σt in the laboratory can be expensive, difficult, and time-consuming for certain projects. Due to the difficulties associated with the experimental procedure, it is usually preferred that the σt is evaluated in an indirect way. For these reasons, in this work, the adaptive network-based fuzzy inference system (ANFIS) is used to build a prediction model for the indirect prediction of σt of sandstone rock samples from their physical properties. Two ANFIS models are implemented, i.e. ANFIS-subtractive clustering method (SCM) and ANFIS-fuzzy c-means clustering method (FCM). The ANFIS models are applied to the data available in the open source literature. In these models, the porosity, specific gravity, dry unit weight, and saturated unit weight are utilized as the input parameters, while the measured σt is the output parameter. The performance of the proposed predictive models is examined according to two performance indices, i.e. mean square error (MSE) and coefficient of determination (R2). The results obtained from this work indicate that ANFIS-SCM is a reliable method to predict σt with a high degree of accuracy.
Hadi Fattahi
Abstract
The uniaxial compressive strength of weak rocks (UCSWR) is among the essential parameters involved for the design of underground excavations, surface and underground mines, foundations in/on rock masses, and oil wells as an input factor of some analytical and empirical methods such as RMR and RMI. The ...
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The uniaxial compressive strength of weak rocks (UCSWR) is among the essential parameters involved for the design of underground excavations, surface and underground mines, foundations in/on rock masses, and oil wells as an input factor of some analytical and empirical methods such as RMR and RMI. The direct standard approaches are difficult, expensive, and time-consuming, especially with highly fractured, highly porous, weak, and homogeneous rocks. Numerous endeavors have been made to develop indirect approaches of predicting UCSWR. In this research work, a new intelligence method, namely relevance vector regression (RVR), improved by the cuckoo search (CS) and harmony search (HS) algorithms is introduced to forecast UCSWR. The HS and CS algorithms are combined with RVR to determine the optimal values for the RVR controlling factors. The optimized models (RVR-HS and RVR-CS) are employed to the available data given in the open-source literature. In these models, the bulk density, Brazilian tensile strength test, point load index test, and ultrasonic test are used as the inputs, while UCSWR is the output parameter. The performances of the suggested predictive models are tested according to two performance indices, i.e. mean square error and determination coefficient. The results obtained show that RVR optimized by the HS model can be successfully utilized for estimation of UCSWR with R2 = 0.9903 and MSE = 0.0031203.
Rock Mechanics
H. Fattahi; N. Zandy Ilghani
Abstract
Horizontal directional drilling is usually used in drilling engineering. In a variety of conditions, it is necessary to predict the torque required for performing the drilling operation. Nevertheless, there is presently not a convenient method available to accomplish this task. In order to overcome this ...
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Horizontal directional drilling is usually used in drilling engineering. In a variety of conditions, it is necessary to predict the torque required for performing the drilling operation. Nevertheless, there is presently not a convenient method available to accomplish this task. In order to overcome this difficulty, the current work aims at predicting the required rotational torque (RT) to operate horizontal directional drilling on the 7 effective parameters including the length of drill string in the borehole (L), axial force on the cutter/bit (P), total angular change of the borehole (KL), radius for the ith reaming operation (Di), rotational speed (rotation per minute) of the bit (N), mud flow rate (W), and mud viscosity (V). In this paper, we propose an approach based on the model selection criteria such as various statistical performance indices mean squared error (MSE), variance account for (VAF), root mean squared error (RMSE), squared correlation coefficient (R2), and mean absolute percentage error (MAPE) to select the most appropriate model among a set of 20 candidate ones to estimate RT, given a set of observed data. Once the most appropriate model is selected, a Bayesian framework is employed to develop the predictive distributions of RT, and to update them with new project-specific data that significantly reduce the associated predictive uncertainty. Overall, the results obtained indicate that the proposed RT model possesses a satisfactory predictive performance.
Rock Mechanics
H. Fattahi; N. Babanouri; Z. Varmaziyari
Abstract
The dynamic response of slopes against earthquake is commonly characterized by the earthquake-induced displacement of slope (EIDS). The EIDS value is a function of several variables such as the material properties, slope geometry, and earthquake acceleration. This work is aimed at the prediction of EIDS ...
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The dynamic response of slopes against earthquake is commonly characterized by the earthquake-induced displacement of slope (EIDS). The EIDS value is a function of several variables such as the material properties, slope geometry, and earthquake acceleration. This work is aimed at the prediction of EIDS using the Monte Carlo simulation method (MCSM). Hence, the parameters height, unit specific weight, cohesion, friction angle, vibration duration, and maximum horizontal acceleration are used to predict the EIDS values. To do this, a multiple non-linear regression relationship is first derived between EIDS and the independent variables. Then MCSM is performed based on the developed regression equation. The results obtained demonstrate that the stochastic approach used is able to successfully reproduce the EIDS values and calculate the confidence intervals. The average of the measured and simulated values for EIDS was 4.34 cm and 4.48 cm, respectively. Eventually, the results of a performed correlation sensitivity analysis revealed that the maximum horizontal acceleration had the greatest impact on EIDS.
H. Fattahi; N. Babanouri
Abstract
The tensile strength (TS) of rocks is an important parameter in the design of a variety of engineering structures such as the surface and underground mines, dam foundations, types of tunnels and excavations, and oil wells. In addition, the physical properties of a rock are intrinsic characteristics, ...
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The tensile strength (TS) of rocks is an important parameter in the design of a variety of engineering structures such as the surface and underground mines, dam foundations, types of tunnels and excavations, and oil wells. In addition, the physical properties of a rock are intrinsic characteristics, which influence its mechanical behavior at a fundamental level. In this paper, a new approach combining the support vector regression (SVR) with a cultural algorithm (CA) is presented in order to predict TS of rocks from their physical properties. CA is used to determine the optimal value of the SVR controlling the parameters. A dataset including 29 data points was used in this study, in which 20 data points (70%) were considered for constructing the model and the remaining ones (9 data points) were used to evaluate the degree of accuracy and robustness. The results obtained show that the SVR optimized by the CA model can be successfully used to predict TS.
H. Fattahi
Abstract
Slope stability analysis is an enduring research topic in the engineering and academic sectors. Accurate prediction of the factor of safety (FOS) of slopes, their stability, and their performance is not an easy task. In this work, the adaptive neuro-fuzzy inference system (ANFIS) was utilized to build ...
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Slope stability analysis is an enduring research topic in the engineering and academic sectors. Accurate prediction of the factor of safety (FOS) of slopes, their stability, and their performance is not an easy task. In this work, the adaptive neuro-fuzzy inference system (ANFIS) was utilized to build an estimation model for the prediction of FOS. Three ANFIS models were implemented including grid partitioning (GP), subtractive clustering method (SCM), and fuzzy c-means clustering method (FCM). Several important parameters such as cohesion coefficient, internal angle of friction, slope height, slope angle, and unit weight of slope material were utilized as the input parameters, while FOS was used as the output parameter. A comparison was made between these three models, and the results obtained showed the superiority of the ANFIS-SCM model. Also performance of the ANFIS-SCM model was compared with multiple linear regression (MLR). The results obtained demonstrated the effectiveness of the ANFIS-SCM model.
Z. Bayatzadeh Fard; F. Ghadimi; H. Fattahi
Abstract
Determining the distribution of heavy metals in groundwater is important in developing appropriate management strategies at mine sites. In this paper, the application of artificial intelligence (AI) methods to data analysis,namely artificial neural network (ANN), hybrid ANN with biogeography-based optimization ...
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Determining the distribution of heavy metals in groundwater is important in developing appropriate management strategies at mine sites. In this paper, the application of artificial intelligence (AI) methods to data analysis,namely artificial neural network (ANN), hybrid ANN with biogeography-based optimization (ANN-BBO), and multi-output adaptive neural fuzzy inference system (MANFIS) to estimate the distribution of heavy metals in groundwater of Lakan lead-zinc mine is demonstrated.For this purpose, the contamination groundwater resources were determined using the existing groundwater quality monitoring data, and several models were trained and tested using the collected data to determine the optimum model that used three inputs and four outputs. A comparison between the predicted and measured data indicated that the MANFIS model had the mostpotential to estimate the distribution of heavy metals in groundwater with a high degree of accuracy and robustness.
