Geological and geotechnical aspects of underground coal mining methods within Australia

About one quarter of the coal produced in Australia is by underground mining methods. The most commonly used underground coal mining methods in Australia are longwall, and room and pillar. This paper provides a detailed review of the two methods, including their advantages and disadvantages, the major geotechnical and operational issues, and the factors that need to be considered regarding their choice, including the varying geological and geotechnical conditions suited to a particular method. Factors and issues such as capital cost, productivity, recovery, versatility and mine safety associated with the two methods are discussed and compared. The major advantages of the longwall mining method include its suitability for mining at greater depth, higher recovery, and higher production rate compared to room and pillar. The main disadvantages of the room and pillar method are the higher risks of roof and pillar collapse, higher capital costs incurred as well as lower recovery rate.     

Artificial Neural Network as a Tool for Backbreak Prediction

Backbreak is one of the destructive side effects of the blasting operation. Reducing of this event is very important for economic of a mining project. Involvement of various parameters has made the backbreak analyzing difficult. Currently there is no any specific method to predict or control the phenomenon considering all the effective parameters. In this paper, artificial neural network (ANN) as a powerful tool for solving such complicated problems is used to predict backbreak in blasting operation of the Sangan iron mine, Iran. Network training was fulfilled using a collected database of the practiced operation including blast design details and rock condition. Trying various types of the networks, a network with two hidden layers was found to be optimum. Performance of the ANN model was compared with statistical analysis using datasets which were kept apart from the original database. According to the obtained results, for the ANN model there existed a higher correlation (R² = 0.868) and lesser error (RMSE = 0.495) between the predicted and measured backbreak as compared to the regression model. Also, sensitivity analysis revealed that the inputs rock factor and number of rows are the most and the least sensitive parameters on the output backbreak, respectively.     

Evaluation and Assessment of Blast-Induced Ground Vibrations in an Underground Gold Mine: A Case Study

Natural Resources Research - Ground vibrations induced during rock fragmentation by blasting remain a potential source of hazard for the stability of nearby structures. In this paper, to forecast...     

Prediction of thermal conductivity of rocks by soft computing

The transfer of energy between two adjacent parts of rock mainly depends on its thermal conductivity. Knowledge of the thermal conductivity of rocks is necessary for the calculation of heat flow or for the longtime modeling of geothermal resources. In recent years, considerable effort has been made to develop artificial intelligence techniques to determine these properties. Present study supports the application of artificial neural network (ANN) in the study of thermal conductivity along with other intrinsic properties of rock due to its increasing importance in many areas of rock engineering, agronomy, and geoenvironmental engineering field. In this paper, an attempt has been made to predict the thermal conductivity (TC) of rocks by incorporating uniaxial compressive strength, density, porosity, and P-wave velocity using artificial neural network (ANN) technique. A three-layer feed forward back propagation neural network with 4-7-1 architecture was trained and tested using 107 experimental data sets of various rocks. Twenty new data sets were used for the validation and comparison of the TC by ANN. Multivariate regression analysis (MVRA) has also been done with same data sets of ANN. ANN and MVRA results were compared based on coefficient of determination (CoD) and mean absolute error (MAE) between experimental and predicted values of TC. It was found that CoD between measured and predicted values of TC by ANN and MVRA were 0.984 and 0.914, respectively, whereas MAE was 0.0894 and 0.2085 for ANN and MVRA, respectively.     

Correlating P-wave Velocity with the Physico-Mechanical Properties of Different Rocks

In mining and civil engineering projects, physico-mechanical properties of the rock affect both the project design and the construction operation. Determination of various physico-mechanical properties of rocks is expensive and time consuming, and sometimes it is very difficult to get cores to perform direct tests to evaluate the rock mass. The purpose of this work is to investigate the relationships between the different physico-mechanical properties of the various rock types with the P-wave velocity. Measurement of P-wave velocity is relatively cheap, non-destructive and easy to carry out. In this study, representative rock mass samples of igneous, sedimentary, and metamorphic rocks were collected from the different locations of India to obtain an empirical relation between P-wave velocity and uniaxial compressive strength, tensile strength, punch shear, density, slake durability index, Young’s modulus, Poisson’s ratio, impact strength index and Schmidt hammer rebound number. A very strong correlation was found between the P-wave velocity and different physico-mechanical properties of various rock types with very high coefficients of determination. To check the sensitivity of the empirical equations, Students t test was also performed, which confirmed the validity of the proposed correlations.     

Application of an Expert System for the Assessment of Blast Vibration

The purpose of this article is to evaluate and predict the blast induced ground vibration using different conventional vibration predictors and artificial neural network (ANN) at a surface coal mine of India. Ground Vibration is a seismic wave that spread out from the blast hole when detonated in a confined manner. 128 blast vibrations were recorded and monitored in and around the surface coal mine at different strategic and vulnerable locations. Among these, 103 blast vibrations data sets were used for the training of the ANN network as well as to determine site constants of various conventional vibration predictors, whereas rest 25 blast vibration data sets were used for the validation and comparison by ANN and empirical formulas. Two types of ANN model based on two parameters (maximum charge per delay and distance between blast face to monitoring point) and multiple parameters (burden, spacing, charge length, maximum charge per delay and distance between blast face to monitoring point) were used in the present study to predict the peak particle velocity. Finally, it is found that the ANN model based on multiple input parameters have better prediction capability over two input parameters ANN model and conventional vibration predictors.     

Prediction of Safe Charge to Protect Surrounding Structures Using Support Vector Machine

The present paper mainly with deals the prediction of safe explosive charge used per delay (Q_MAX) using support vector machine (SVM) incorporating peak particle velocity (PPV) and distance between blast face to monitoring point (D). 150 blast vibration data sets were monitored at different vulnerable and strategic locations in and around a major coal producing opencast coal mines in India. 120 blast vibrations records were used for the training of the SVM model vis-à-vis to determine site constants of various conventional vibration predictors. Rest 30 new randomly selected data sets were used to compare the SVM prediction results with widely used conventional predictors. Results were compared based on coefficient of correlation (R) between measured and predicted values of safe charge of explosive used per delay (Q_MAX). It was found that coefficient of correlation between measured and predicted Q_MAX by SVM was 0.997, whereas it was ranging from 0.063 to 0.872 by different conventional predictor equations.     

An expert system based on hybrid ICA-ANN technique to estimate macerals contents of Indian coals

Coal, as an initial source of energy, requires a detailed investigation in terms of ultimate analysis, proximate analysis, and its biological constituents (macerals). The rank and calorific value of each type of coal are managed by the mentioned properties. In contrast to ultimate and proximate analyses, determining the macerals in coal requires sophisticated microscopic instrumentation and expertise. This study emphasizes the estimation of the concentration of macerals of Indian coals based on a hybrid imperialism competitive algorithm (ICA)–artificial neural network (ANN). Here, ICA is utilized to adjust the weight and bias of ANNs for enhancing their performance capacity. For comparison purposes, a pre-developed ANN model is also proposed. Checking the performance prediction of the developed models is performed through several performance indices, i.e., coefficient of determination (R ²), root mean square error and variance account for. The obtained results revealed higher accuracy of the proposed hybrid ICA-ANN model in estimating macerals contents of Indian coals compared to the pre-developed ANN technique. Results of the developed ANN model based on R ² values of training datasets were obtained as 0.961, 0.955, and 0.961 for predicting vitrinite, liptinite, and inertinite, respectively, whereas these values were achieved as 0.948, 0.947, and 0.957, respectively, for testing datasets. Similarly, R ² values of 0.988, 0.983, and 0.991 for training datasets and 0.989, 0.982, and 0.985 for testing datasets were obtained from developed ICA-ANN model.     

Performance of Hybrid SCA-RF and HHO-RF Models for Predicting Backbreak in Open-Pit Mine Blasting Operations

Natural Resources Research - Backbreak is an adverse phenomenon in blasting operation, which can cause, among others, mine walls instability, falling down of machinery, drilling efficiency...