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Coal has remained the primary component of China's energy structure, and high-intensity extraction has continued in the central and western coal-producing regions of China. In contrast to the abundant coal resources, water resources have become extremely scarce in these regions, creating a conflict between coal resource extraction and water resource conservation. The coal mine underground reservoir (CMUR), as a typical technology for combined coal and water extraction and water-preserving coal mining, has been applied in numerous mines in central and western China. This effectively alleviates water resource shortages and achieves the goal of water resource conservation. The CMURs utilizes the goaf created by longwall mining as the water storage space. The reservoir dam structure comprises coal pillars, which serve as protective coal pillars in the mining area, and artificial dam structures that filled the gaps between these coal pillars. The stability of the dam structure under the complex stress effects of hydraulic coupling has been identified as the key to maintaining the safe operation of the CMUR. The mechanical properties, stress field, fracture field, and seepage field ("three fields") change mechanisms, as well as the research results on size optimization of coal pillar dams and artificial dams in CMURs, were systematically reviewed. The core content included the instability and failure mechanisms of dam structures under the comprehensive coupling effects of factors such as dry–wet cycles of mine water, long-term immersion, chemical effects of high-salinity water, dynamic and static loads, and cyclic loads. This paper is considered to have certain reference value for the study of the stability of dam structures in CMURs and to provide some guidance for the safe operation of CMURs. [ABSTRACT FROM AUTHOR]

Intelligent computing is transforming safety inspection methods and response strategies in coal mines. Due to the significant safety hazards associated with mining excavation, this study proposes a multi-source data based predictive model for assessing gas risk and implementing countermeasures. By examining the patterns of gas dispersion at the longwall face, utilizing both temporal and spatial correlation, a predictive model is crafted that incorporates safety thresholds for gas concentrations, four-level early warning method and response strategy are devised by integrating weighted predictive confidence with these correlations. Initially tested using a public dataset from Poland, this method was later verified in coal mine in China. This paper discusses the validity and correlation of multi-source monitoring data in temporal and spatial correlation and proposes a risk warning mechanism based on it, which can be applied not only for safety warning but also for regulatory management. [ABSTRACT FROM AUTHOR]

To improve the recovery for corner coal left over in mines, taking the Xin'an Mine in China as the research object, the gradual increase support mining method for recovering corner coal is proposed in this study and the evolution of the overlying strata pressure is investigated. The dynamic process of the overburden roof in the corner coal and the hydraulic support were simulated by using FLAC3D software. Results show that, under the gradual increase support mining, the abutment pressure over the mining working face is less than 21 MPa. The overlying rock over the coal seam roof forms a periodic breaking--collapse cycle with a periodic pressure step distance of 10 m and the hydraulic support load is 13-25 MPa. Based on that, the gradual increase support mining method including a scheme of three supports and three chutes adding for every 9 m is designed and the corner coal is fully recovered. The obtained conclusions provide a significant reference for predicting strata movement over the corner coal, which can help aid in designing such mining method in engineering applications. [ABSTRACT FROM AUTHOR]

A considerable number of coal mines employed room and pillar mining in the last century in northern China, where the goaf remained stable for a period of time; however, with the increased exposure of coal pillars, their collapse may gradually increase. The stability assessment of these old rooms and pillar goafs is challenging due to their concealment, irregular mining patterns, and the long passage of time. The methodology developed in this study, based on "space-sky-earth" remote sensing such as InSAR to trace historical deformation, the UAV observation of current surface damage, and comparison of mining spaces, can rapidly detect on a large scale the collapse of old goafs and the trend of damage. This study is conducted with an example of a coal mine in Yulin, Northern China, where obtained quantitative surface deformation values were integrated with qualitative surface damage interpretation results, followed by a yearly analysis of the overlying rock movement in accordance with the underground coal mining process. The results show that from 2007 to 2021, corresponding surface deformation and damage occurred following mining progress. However, the room and pillar goaf areas had not undergone any surface deformation, nor had there been incidents of landslides or ground fissures; therefore, it was speculated that no roof collapse had occurred in this region. The surface deformation and damage associated with underground coal mining are complex and influenced by the coal seam occurrence, mining methods, strata lithology, terrain slope, temporal evolution, and anthropogenic modifications. These phenomena are representative of the coal mining area, and this methodology can provide a reference for similar endeavors. [ABSTRACT FROM AUTHOR]

The construction and expansion of high-speed railway networks in China has been occurring at a fast pace, resulting in some lines crossing through coal mine goaf sites. However, the embankment and train loads may trigger the activation of the coal mine goaf ground, posing a threat to traffic safety. To ensure the safety of construction and railway lines, an evaluation model on the activation classification of coal mine goaf ground, taking into account the high-speed railway load, is proposed, which is mainly applicable for middle–deep and level goaf areas using a longwall mining method. Firstly, 12 influencing factors are selected as the underlying evaluation indexes, and the catastrophe progression method model for evaluating the coal mine goaf ground stability is constructed. The findings of the evaluation were found to align with the actual results, indicating the reliability of the model. Then, the additional stress calculation model for high-speed railway ground with different embankment heights, train speeds, and axle loads was established, and the train load disturbance depth with a 5% criterion was determined. The influence degree of load on high-speed railway was divided, and the weight of each factor was determined. Finally, the extension comprehensive evaluation method was used to unite the stability grade of the coal mine goaf site and the influence degree of the train, so the evaluation model on activation classification was proposed. The accurateness and reliability of the proposed model was verified using the Taijiao high-speed railway cases and the model test. [ABSTRACT FROM AUTHOR]