Main geological and mining factors affecting ground cracks induced by underground coal mining in Shanxi Province, China

As one of the largest coal-rich provinces in China,Shanxi has extensive underground coal-mining operations.These operations have caused numerous ground cracks and substantial environmental damage.To study the main geological and mining factors influencing mining-related ground cracks in Shanxi,a detailed investigation was conducted on 13 mining-induced surface cracks in Shanxi.Based on the results,the degrees of damage at the study sites were empirically classified into serious,moderate,and minor,and the influential geological and mining factors(e.g.,proportions of loess and sandstone in the mining depth,ratio of rock thickness to mining thickness,and ground slope)were discussed.According to the analysis results,three factors(proportion of loess,ratio of rock thickness to mining thickness,and ground slope)play a decisive role in ground cracks and can be respectively considered as the critical material,mechanical,and geometric conditions for the occurrence of mining surface disasters.Together,these three factors have a strong influence on the occurrence of serious discontinuous ground deformation.The results can be applied to help prevent and control ground damage caused by coal mining.The findings also provide a direct reference for predicting and eliminating hidden ground hazards in mining areas.

Mechanics criterion and factors affecting overburden stability in solid dense filling mining

The effect of controlling strata movement in solid filling mining depends on the filling rate of the goal. However, the mechanical property of the overburden in the backfill stope and the designed size of the backfill mining workface should also be considered. In this study, we established a main roof strata model with loads in accordance with the theory of key strata to investigate the stability of the overburden in solid dense filling mining. We analyzed the stress distribution law of the main roof strata based on elastic thin plate theory. The results show that the position of the long side midpoint of the main roof strata failed more easily because of tensile yield, indicating that this position is the area where failure is likely to occur more easily. We also deduced the stability mechanics criterion of the main roof strata based on tensile yield criterion. The factors affecting the stability of the overburden in solid dense filling mining were also analyzed, including the thickness and elasticity modulus of the main roof strata, overlying strata loads, advanced distance and length of workface, and elastic foundation coefficient of backfill body. The research achievements can provide an important theoretical basis for determining the designed size of the solid dense filling mining workface.

Failure characteristics of surface vertical wells for relieved coal gas and their influencing factors in Huainan mining area

Based on data from through-hole and logging,we studied the failure characteristics of surface drainage wells for relieved coal gas in Huainan mining area and its influencing factors.The results show that the damaged positions of drainage wells are mainly located at the thick clay layer in the low alluvium and the lithological interface in the upper section of bedrock in west mining area.The failure depth of casing is 244-670 m and concentrates at about 270-460 m deep.These damaged positions are mainly located in the bending zone according to three zones of rock layers in the vertical section above the roof divided. Generally,the casing begins to deform or damage before the face line about 30-150 m.Special formation structure and rock mass properties are the direct causes of the casing failure,high mining height and fast advancing speed are fundamental reasons for rock mass damage.However,the borehole configuration and spacing to the casing failure are not very clear.

A method to calculate displacement factors using SVM

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.

A New Method Based on Association Rules Mining and Geo-filter for Mining Spatial Association Knowledge

Association rule mining methods, as a set of important data mining tools, could be used for mining spatial association rules of spatial data. However, applications of these methods are limited for mining results containing large number of redundant rules. In this paper, a new method named Geo-Filtered Association Rules Mining(GFARM) is proposed to effectively eliminate the redundant rules. An application of GFARM is performed as a case study in which association rules are discovered between building land distribution and potential driving factors in Wuhan, China from 1995 to 2015. Ten sets of regular sampling grids with different sizes are used for detecting the influence of multi-scales on GFARM. Results show that the proposed method can filter 50%–70% of redundant rules. GFARM is also successful in discovering spatial association pattern between building land distribution and driving factors.

Suitable layout of gate roads related to slice mining in an ultra-thick unstable coal seam

We determi：ned a suitable gate road layout in slice mining in an ultra-thick unstable coal seam, using theoretical anallysis and numerical calculations. Based on plasticity theory in terms of limiting equilibrium, the width of chain pillar in the upper slice was calculated to be 18 m. The stress distribution in the chain pillar after the upper slice was mined out was described with numerical simulation. The extent of the effect of stress on the upper chain pillar on the lower solid coal was obtained on the basis of an elastic solution of a distributed force loaded on a half-plane. Three layout designs for lower gate roads were pro- posed and a stability factor was introduced to analyze the stability of the lower pillar with numerical calculation. Gate road translation was determined as the most suitable layout method, which maximizes the extraction rate on the basis of the pillar stability.

Anomalous Cell Detection with Kernel Density-Based Local Outlier Factor

Since data services are penetrating into our daily life rapidly, the mobile network becomes more complicated, and the amount of data transmission is more and more increasing. In this case, the traditional statistical methods for anomalous cell detection cannot adapt to the evolution of networks, and data mining becomes the mainstream. In this paper, we propose a novel kernel density-based local outlier factor(KLOF) to assign a degree of being an outlier to each object. Firstly, the notion of KLOF is introduced, which captures exactly the relative degree of isolation. Then, by analyzing its properties, including the tightness of upper and lower bounds, sensitivity of density perturbation, we find that KLOF is much greater than 1 for outliers. Lastly, KLOFis applied on a real-world dataset to detect anomalous cells with abnormal key performance indicators(KPIs) to verify its reliability. The experiment shows that KLOF can find outliers efficiently. It can be a guideline for the operators to perform faster and more efficient trouble shooting.