Effectively avoiding methane accidents is vital to the security of manufacturing minerals. Coal mine methane accidents are often caused by a methane concentration overrun, and accurately predicting methane emission quantity in a coal mine is key to solving this problem. To maintain the concentration of methane in a secure range, grey theory and neural network model are increasingly used to critically forecasting methane emission quantity in coal mines. A limitation of the grey neural network model is that researchers have merely combined the conventional neural network and grey theory. To enhance the accuracy of prediction, a modified grey GM (1,1) and radial basis function (RBF) neural network model is proposed, which combines the amended grey GM (1,1) model and RBF neural network model. In this article, the proposed model is put into a simulation experiment, which is built based on Matlab software (MathWorks.Inc, Natick, Masezius, U.S). Ultimately, the conclusion of the simulation experiment verified that the modified grey GM (1,1) and RBF neural network model not only boosts the precision of prediction, but also restricts relative error in a minimum range. This shows that the modified grey GM (1,1) and RBF neural network model can make more effective and precise predict the predicts, compared to the grey GM (1,1) model and RBF neural network model.

The surface discontinuous deformation caused by coal mining has great damage to the ecological environment and threatens the safety of human lives. Focusing on the problem of discontinuous deformation (ground fissures and collapsed pits) in mining areas with a thick loess and thin bedrock, this paper uses a coal panel in southern Shanxi in China as research background, and uses field investigation, theoretical analysis and the particle flow code 2D (PFC2D) numerical simulation method to study the movement of overburden and discontinuous ground deformation of mining areas with a thick loess layer and a thin bedrock. The results show that with the continual advance of the working face, the failure of the overlying rock, the changing of force chain shape and the development of cracks under this geological and mining condition have their unique rules. This study analyzes the law of movement of overburden in coal seam mining, explains why discontinuous deformation of the surface occurs in case of a thick loess layer and thin bedrock, and provides reference for the prediction of fracture development under the same geological conditions and the application of the PFC2D in coal seam mining in different geological conditions.

Gob-side entry retaining by roof cutting, a pillarless mining technique, plays a critical role in maintaining continuous production, rapid connection, and enhancing the coal recovery rate in fully mechanized top coal caving working faces. This technique stands as a sustainable development method in coal mining. The present research, set against the backdrop of the Yitang Coal Mine 100602 top coal gob-side entry retaining by roof cutting, investigates the influence of roof-cutting borehole depth, borehole dip angle, mining height, and coal seam thickness on stability in an ultra-thick coal seam under 12 distinct mining conditions. A typical model of overburden structure post-roof pre-splitting was established to study the failure mechanism of the top coal roof. The results reveal that the dip angle and depth of the roof pre-fracturing borehole significantly impact the movement characteristics of the overlying strata. Optimal conditions are found when the dip angle and depth of the roof pre-fracturing borehole, the mining height, and the top coal thickness are 10°, 16 m, 4 m, and 4 m, respectively. Under these circumstances, the load transfer from the goaf to the gob-side entry can be effectively intercepted, mitigating the influence of roof fracture activities on the top coal gob-side entry. Field measurements confirm that suitable anchor-net support can stabilize the roof’s rock structure. This research underpins the significance of roof pre-fracturing for the promotion and application of top coal gob-side entry retaining by roof cutting in ultra-thick coal seams.

Sustainable development in coal mining requires a continuous and efficient method of coal extraction. Research shows that gob-side entries retained through roof cutting retained gob-side (RCGE) are vital for improving mining efficiency, enhancing coal recovery rates, and enabling continuous production. However, the mechanism of surrounding rock deformation during close-distance co-mining of coal seams with this technique is not yet clear. For the Jiaokou coal mine in China, due to an unreasonable stagger distance between upper and lower working faces, the gob-side entries retained at the 9102 tailgate and 10102 headgate experience severe rock pressure, leading to significant prop damage and a sharp reduction in the cross-section of the entry. This greatly hampers the reuse of these entries. To investigate this issue, we established a model to study the stress distribution of surrounding rocks at different stagger distances (20 m, 40 m, 60 m, 80 m, and 120 m) through numerical simulation and optimized the support parameters for the retained entries. Our research found that when the subsidence of the roof in the upper coal seam exceeds 0.74 m but is less than 1.33 m, there is sliding instability in the mining body. When the subsidence exceeds 1.33 m, the mining body will rotate and deform, causing significant mining pressure within the retained entry. A stagger distance of 40 m between the upper and lower working faces can reduce pressure on the face during the mining of the lower coal seam. Extensive field measurements of rock pressure revealed that the damage rate of the single column in the gob-side entries of the upper and lower coal seams does not exceed 5% and 1%, respectively. In summary, this study provides a practical method to reduce damage to entries during the mining process, thereby increasing the continuous production capability of the coal mine. This is critical for the sustainable development of coal mining.

Joint roughness determination is a fundamental issue in many areas of rock engineering, because joint roughness has significant influences on mechanical properties and deformation behavior of rock masses. Available models suggested in the literature neglected combined effects of shear direction, scale of rock discontinuities, inclination angle, and amplitude of asperities during the roughness calculations. The main goals of this paper are to establish a comprehensive parameter that considers the characteristics of the size effect, anisotropy, and point spacing effect of the discontinuity roughness, and to investigate the correlation between the proposed comprehensive parameter and joint roughness coefficients. In this work, the Barton ten standard profiles are digitally represented, then the morphological characteristics of the discontinuity profiles are extracted. A comprehensive parameter that considers the characteristics of the size effect, anisotropy, and point spacing effect of the discontinuity roughness is established, and its correlation with joint roughness coefficients (JRC) is investigated. The correlation between the proposed discontinuity roughness parameter and the joint roughness coefficients can predict the JRC value of the natural discontinuities with high accuracy, which provides tools for comprehensively characterizing the roughness characteristics of rock discontinuities. The roughness index $R_{vh[-{30}^{\circ },0]}$ reflects the gentle slope characteristics of the rock discontinuity profiles in the shear direction, which ignores the segments with steep slopes greater than 30° on the discontinuity profiles. The influence of steep slope segments greater than 30° should be considered for the roughness anisotropy parameter in the future.

Effectively avoiding gas accident is vital to the security of mineral manufacture, and the coal mine gas accident is often caused by gas concentration overrun. The prediction accuracy of gas emission quantity in coal mine is the key to solve this problem. To maintain concentration of gas in a secure range，grey theory and neural network model increasingly diffusely used in forecasting gas emission quantity in coal mine critically. Nevertheless, the limitation of the grey neural network model is that researchers merely bonded the conventional neural network and grey theory. To enhance accuracy of prediction, a modified grey GM(1,1) and RBF neural network model is proposed combined amended grey GM(1,1) model and RBF neural network model. Then the proposed model was put into simulation experiment which is built based on Matlab software. Ultimately, conclusion of the simulation experiment verified that the modified grey GM(1,1) and RBF neural network model not only boosts the precision of prediction, but also restricts relative error in a minimum range. This showed that the modified grey GM(1,1) and RBF neural network model achieves effectiveness in precision of prediction much better than grey GM(1,1) model and RBF neural network model.