This study is focused on the prediction of mining subsidence and its impact on the environment in the Hongqi mining area. The study was carried out by means of a probability integral model based, in first instance bas...This study is focused on the prediction of mining subsidence and its impact on the environment in the Hongqi mining area. The study was carried out by means of a probability integral model based, in first instance based on field surveys and the analysis of data collected from this area. Isolines of mining sub- sidence were then drawn and the impact caused by mining subsidence on the environment was analyzed quantitatively by spatial analysis with Geographic Information System （GIS）. The results indicate that the subsidence area of the first working-mine can be as large as 2.54 km2, the maximum subsidence is 3440 mrn which will cause 1524 houses to be relocated. The entire subsidence area of the mine can reach 8.09 km2, with a maximum subsidence of 3590 ram. Under these circumstances the value of the loss of ecosystem services Will reach 5.371 million Yuan and the cost of relocating buildings will increase to 6.858 million Yuan.展开更多
Prediction of surface subsidence caused by longwall mining operation in inclined coal seams is often very challenging. The existing empirical prediction methods are inflexible for varying geological and mining conditi...Prediction of surface subsidence caused by longwall mining operation in inclined coal seams is often very challenging. The existing empirical prediction methods are inflexible for varying geological and mining conditions. An improved influence function method has been developed to take the advantage of its fundamentally sound nature and flexibility. In developing this method, the original Knothe function has been transformed to produce a continuous and asymmetrical subsidence influence function. The empirical equations for final subsidence parameters derived from col- lected longwall subsidence data have been incorporated into the mathematical models to improve the prediction accuracy. A number of demonstration cases for longwall mining operations in coal seams with varying inclination angles, depths and panel widths have been used to verify the applicability of the new subsidence prediction model.展开更多
Six main influencing factors： slope, aspect, distance, angle, angle of coal seam, and the ratio of depth and thickness, were selected by Grey correlation theory and Grey relational analysis procedure programmed by th...Six main influencing factors： slope, aspect, distance, angle, angle of coal seam, and the ratio of depth and thickness, were selected by Grey correlation theory and Grey relational analysis procedure programmed by the MATLAB software package to select the surface movement and deformation parameters. On this basis, the paper built a BP neural network model that takes the six main influencing factors as input data and corresponding value of ground subsidence as output data. Ground subsidence of the 3406 mining face in Haoyu Coal was predicted by the trained BP neural network. By comparing the prediction and the practices, the research shows that it is feasible to use the 13P neural network to predict mountain mining subsidence.展开更多
In order to improve the precision of mining subsidence prediction, a mathematical model using Support Vector Machine （SVM） was established to calculate the displacement factor. The study is based on a comprehensive ...In order to improve the precision of mining subsidence prediction, a mathematical model using Support Vector Machine （SVM） was established to calculate the displacement factor. The study is based on a comprehensive analysis of factors affecting the displacement factor, such as mechanical properties of the cover rock, the ratio of mining depth to seam thickness, dip angle of the coal seam and the thickness of loose layer. Data of 63 typical observation stations were used as a training and testing sample set. A SVM regression model of the displacement factor and the factors affecting it was established with a kernel function, an insensitive loss factor and a properly selected penalty factor. Given an accurate calculation algorithm for testing and analysis, the results show that an SVM regression model can calcu- late displacement factor precisely and reliable precision can be obtained which meets engineering requirements. The experimental results show that the method to calculation of the displacement factor, based on the SVM method, is feasible. The many factors affecting the displacement factor can be consid- ered with this method. The research provides an efficient and accurate approach for the calculation of displacement in mining subsidence orediction.展开更多
The fully mechanized caving coal mining under the railway in mine area will result in difficulty maintenance of railway because of great distortion and subsidence speed of terrene and railway. If the subsidence foreca...The fully mechanized caving coal mining under the railway in mine area will result in difficulty maintenance of railway because of great distortion and subsidence speed of terrene and railway. If the subsidence forecasting is incorrect and maintenance measure is not suitable in the preceding and the process of mining, the normal operation of the railway in mine area will not be ensured and perhaps the safety accident will be resulted. The railway subsidence forecasting and maintenance system for fully mechanized caving coal face are studied and developed in this connection. Based on the accurate subsidence forecasting of the terrene and railway, the maintenance measure for track and switch turnout in railway is put forward in this system.展开更多
基金Support for this work, provided by the Science and Technology Project of the Land and Resources Department of Henan Province (No.0979)
文摘This study is focused on the prediction of mining subsidence and its impact on the environment in the Hongqi mining area. The study was carried out by means of a probability integral model based, in first instance based on field surveys and the analysis of data collected from this area. Isolines of mining sub- sidence were then drawn and the impact caused by mining subsidence on the environment was analyzed quantitatively by spatial analysis with Geographic Information System （GIS）. The results indicate that the subsidence area of the first working-mine can be as large as 2.54 km2, the maximum subsidence is 3440 mrn which will cause 1524 houses to be relocated. The entire subsidence area of the mine can reach 8.09 km2, with a maximum subsidence of 3590 ram. Under these circumstances the value of the loss of ecosystem services Will reach 5.371 million Yuan and the cost of relocating buildings will increase to 6.858 million Yuan.
文摘Prediction of surface subsidence caused by longwall mining operation in inclined coal seams is often very challenging. The existing empirical prediction methods are inflexible for varying geological and mining conditions. An improved influence function method has been developed to take the advantage of its fundamentally sound nature and flexibility. In developing this method, the original Knothe function has been transformed to produce a continuous and asymmetrical subsidence influence function. The empirical equations for final subsidence parameters derived from col- lected longwall subsidence data have been incorporated into the mathematical models to improve the prediction accuracy. A number of demonstration cases for longwall mining operations in coal seams with varying inclination angles, depths and panel widths have been used to verify the applicability of the new subsidence prediction model.
文摘Six main influencing factors： slope, aspect, distance, angle, angle of coal seam, and the ratio of depth and thickness, were selected by Grey correlation theory and Grey relational analysis procedure programmed by the MATLAB software package to select the surface movement and deformation parameters. On this basis, the paper built a BP neural network model that takes the six main influencing factors as input data and corresponding value of ground subsidence as output data. Ground subsidence of the 3406 mining face in Haoyu Coal was predicted by the trained BP neural network. By comparing the prediction and the practices, the research shows that it is feasible to use the 13P neural network to predict mountain mining subsidence.
基金the Research and Innovation Program for College and University Graduate Students in Jiangsu Province (No.CX10B_141Z)the National Natural Science Foundation of China (No.41071273) for support of this project
文摘In order to improve the precision of mining subsidence prediction, a mathematical model using Support Vector Machine （SVM） was established to calculate the displacement factor. The study is based on a comprehensive analysis of factors affecting the displacement factor, such as mechanical properties of the cover rock, the ratio of mining depth to seam thickness, dip angle of the coal seam and the thickness of loose layer. Data of 63 typical observation stations were used as a training and testing sample set. A SVM regression model of the displacement factor and the factors affecting it was established with a kernel function, an insensitive loss factor and a properly selected penalty factor. Given an accurate calculation algorithm for testing and analysis, the results show that an SVM regression model can calcu- late displacement factor precisely and reliable precision can be obtained which meets engineering requirements. The experimental results show that the method to calculation of the displacement factor, based on the SVM method, is feasible. The many factors affecting the displacement factor can be consid- ered with this method. The research provides an efficient and accurate approach for the calculation of displacement in mining subsidence orediction.
文摘The fully mechanized caving coal mining under the railway in mine area will result in difficulty maintenance of railway because of great distortion and subsidence speed of terrene and railway. If the subsidence forecasting is incorrect and maintenance measure is not suitable in the preceding and the process of mining, the normal operation of the railway in mine area will not be ensured and perhaps the safety accident will be resulted. The railway subsidence forecasting and maintenance system for fully mechanized caving coal face are studied and developed in this connection. Based on the accurate subsidence forecasting of the terrene and railway, the maintenance measure for track and switch turnout in railway is put forward in this system.
