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Combined mining is when two working faces separated by a certain distance simultaneously advance in the same direction, which commonly results in stress concentration and rockburst. This paper investigates the rockburst mechanism due to inclined coal seam combined mining under complex geological conditions in the Da'anshan Coal Mine, China. Based on the experimental analysis, there is an impact tendency of the No.10 coal seam. A theoretical model is established to evaluate rockburst risk due to combined mining. In addition, a 3D multifactor numerical model of inclined coal seam combined mining is established. The novel "PyFlac-Exc" calculation code and the "PyFlac-Eng" elastic strain energy calculation script are compiled. The evolution of elastic strain energy is obtained during combined mining. Theoretical and numerical results show that a rockburst occurs in the western fourth working face of the No. 10 coal seam, which is consistent with the on-site rockburst accident. In addition, the kernel density estimation method is proposed to reduce stress concentration by changing the coal pillar widths and misalignment distances. Numerical results show that the elastic strain energy levels are low for the same coal pillar width, and the misalignment distance is arranged from 50 to 80 m. With the same misalignment distance, a coal pillar width greater than 45 m can effectively reduce the rockburst risk. [ABSTRACT FROM AUTHOR]

Rockburst is a kind of complex and catastrophic dynamic geological disaster in the development and utilization of underground space, which seriously threatens the safety of personnel and environment. Due to the suddenness in time and randomness in space, the prediction of rockburst becomes a great challenge. Microseismic monitoring is capable to continuously capture rock microfracture signals in real time, which offers an effective means for rockburst prediction. With the explosive growth of monitoring data, the conventional manual forecasting methods are laborious and time-consuming. Therefore, artificial intelligence was introduced to improve prediction efficiency. A novel tree-based algorithm was proposed. Its basic idea was to automatically recognize precursory microseismic sequences for the real-time prediction of rockburst intensity. The database consisting of 1500 microseismic events was analyzed. To establish precursory microseismic sequences, dimensionality reduction of the database was first implemented by t-SNE algorithm. Then, k-means clustering algorithm was employed for labelling 1500 microseismic events. Before that, canopy algorithm was adopted to determine the number of clusters. Finally, 300 precursory microseismic sequences were formed by the grouping rule. They were further partitioned into two parts through stratified sampling: 70% for training and 30% for validation. The validation results indicated that the precursor tree with pruning achieved a higher prediction accuracy of 98.9% than one without pruning on the validation set. And the increase was separately 12.2%, 9.2% and 28.6% on the whole validation set and each classes (low/moderate rockburst). In comparison with low rockburst, moderate rockburst was minority class. The improved accuracy on moderate rockburst suggested that pruning can enhance the recognition ability of precursor tree for the minority class. Additionally, two extra rockburst cases were collected from a diversion tunnel in northwestern China, which provided a complete workflow about how to apply the built precursor tree model to achieve field rockburst warning in engineering practice. The tree-based algorithm served as a new and promising way for the real-time rockburst prediction, which successfully integrated field microseismic monitoring and artificial intelligence. [ABSTRACT FROM AUTHOR]

Ascending mining method is a widely used method in China for coal mine safety. To determine whether ascending mining method can eliminate or reduce the outburst risk or not in Xinzyao Mine, China, a numerical model was built by FLAC3D software to investigate the fracture evolution and accumulation and dissipation law of energy during the process of ascending mining. And a dual water blocking system was used to verify the fracture evolution obtained from the numerical model. The results show that FLAC3D model can predict the fracture evolution in overburden with a reasonable error. The height of water-conducting fractured zone of no. 14-3 coal seam is about 82.5 m, and it increases by 27 % after no. 12-2 coal seam is mined. The largest energy accumulated in no. 12-2 coal seam is about 32.5 kJ/m when mining it directly. The energy accumulated in no. 12-2 coal seam decreases by more than 40 % when ascending mining method is adopted. Ascending mining method can effectively eliminate the outburst risk of the upper coal seam. The research is helpful for the determination of ascending mining method and the further study of the prevention mechanism of rock burst. [ABSTRACT FROM AUTHOR]

Accurate prediction of short-term rockburst has a significant role in improving the safety of workers in mining and geotechnical projects. The rockburst occurrence is nonlinearly correlated with its influencing factors that guarantee imprecise predicting results by employing the traditional methods. In this study, three approaches including including t-distributed stochastic neighbor embedding (t-SNE), K-means clustering, and extreme gradient boosting (XGBoost) were employed to predict the short-term rockburst risk. A total of 93 rockburst patterns with six influential features from micro seismic monitoring events of the Jinping-II hydropower project in China were used to create the database. The original data were randomly split into training and testing sets with a 70/30 splitting ratio. The prediction practice was followed in three steps. Firstly, a state-of-the-art data reduction mechanism t-SNE was employed to reduce the exaggeration of the rockburst database. Secondly, an unsupervised machine learning, i.e., K-means clustering, was adopted to categorize the t-SNE dataset into various clusters. Thirdly, a supervised gradient boosting machine learning method i.e., XGBoost was utilized to predict various levels of short-term rockburst database. The classification accuracy of XGBoost was checked using several performance indices. The results of the proposed model serve as a great benchmark for future short-term rockburst levels prediction with high accuracy. [ABSTRACT FROM AUTHOR]