The intellectualization of coal mines provides core technical support for the high-quality development of the coal industry. Intelligent texts, especially intelligent policy documents, play an extremely important role in analyzing the trend of intelligent policies in coal mines. This paper collects more than 50 central and local intelligent coal mine policy texts from recent years. The method of text analysis is a tool used for text mining, and semantic networks are generated; it reflects that the policy mainly focuses on promoting large-scale equipment and platform integration, to promote the overall goal of safe, efficient, and intelligent development of coal mining. By analyzing the high-frequency words of the policy from 2016 to 2022, it reflects that the policy trend mainly goes through the following three stages: firstly, eliminate backward enterprises, encourage coal mine automation and mechanization; then, standardize the basic concept of coal mine intellectualization, carry out the transformation of coal mine intellectualization; and the third stage is to promote the application of key technologies of intellectualization, build intelligent demonstration coal mines and reach the acceptance stage, and promote the further development trend of coal mine intellectualization.

Cemented paste backfill (CPB) is widely used in underground mining, and attracts more attention these years as it can reduce mining waste and avoid environmental pollution. Normally, to evaluate the functionality of CPB, the compressive strength (UCS) is necessary work, which is also time and money consuming. To address this issue, seven machine learning models were applied and evaluated in this study, in order to predict the UCS of CPB. In the laboratory, a series of tests were performed, and the dataset was constructed considering five key influencing variables, such as the tailings to cement ratio, curing time, solids to cement ratio, fine sand percentage and cement types. The results show that different variables have various effects on the strength of CPB. The optimum models for predicting the UCS of CPB are a support vector machine (SVM), decision tree (DT), random forest (RF) and back-propagation neural network (BPNN), which means that these models can be directly applied for UCS prediction in future work. Furthermore, the intelligent model reveals that the tailings to cement ratio has the most important influence on the strength of CPB. This research can boost CPB application in the field, and guide the artificial intelligence application in future mining.

Accurately evaluating rockburst intensity has attracted much attention in these recent years, as it can guide the design of engineering in deep underground conditions and avoid injury to people. In this study, a new ensemble classifier combining a random forest classifier (RF) and beetle antennae search algorithm (BAS) has been designed and applied to improve the accuracy of rockburst classification. A large dataset was collected from across the world to achieve a comprehensive representation, in which five key influencing factors were selected as the input variables, and the rockburst intensity was selected as the output. The proposed model BAS-RF was then validated by the dataset. The results show that BAS could tune the hyperparameters of RF efficiently, and the optimum model exhibited a high performance on an independent test set of rockburst data and new engineering projects. According to the ensemble RF-BAS model, the feature importance was calculated. Furthermore, the accuracy of the proposed model on rockburst prediction was higher than the conventional machine learning models and empirical models, which means that the proposed model is efficient and accurate.

In the design and stability of thermal engineering applications, a thorough understanding of the evolution of damage in the rock following high-temperature treatments is crucial. Hence, this study investigates the influence of high temperatures on Egyptian granodiorite rock properties, given its widespread use as ornamental stones and aggregate material for roadways. Temperature effects up to 800 °C on its physical and mechanical responses were examined in conjunction with microstructure alterations. The results show that the density of granodiorite decreases after heat exposure due to a gain in volume and a loss in mass, with volume expansion being the most important component. In addition, the uniaxial compressive strength increases up to 400 °C before reducing linearly as the temperature increases, while the elastic modulus and P-wave velocity show a reducing trend with the temperature. This study suggests that granodiorite has a thermal damage threshold of 400 °C, beyond which its microstructure and physical and mechanical characteristics deteriorate, and granodiorite becomes less brittle and more ductile. Hence, at the mutation range (between 400 and 600 °C), the physical and mechanical responses shift from a stable to an unstable state. As a result, the microstructure of the granodiorite samples was destroyed at 800 °C, resulting in a significant drop in compressive strength and dilemmas in measuring the P-wave and elastic modulus. Accordingly, the findings of this study can be used to aid in the safe handling of this rock in high-temperature conditions.

The deep roadway groups play an important role in transportation and ventilation in coal mine production. Therefore, it is very important to comprehensively analyze the coupling effect of rheological deformation and coal mining on the stability of the roadway groups. In this paper, the disturbance effects of different stop-mining lines on roadway groups under long-term rheology were investigated by numerical simulation, and the failure mechanism of roadway groups with large sections and multiple disturbances in a deep well was revealed. The results show that the long working face will lead to the collapse of key strata, and the influence range will spread to the adjacent roadway groups. When the distance between the working face and the stop-mining line is 100 m, the roadway groups cannot be affected by the working face mining, and the reserved width of the coal pillar can be determined to be 100 m, which increases the stability of the roadway’s surrounding rock and maintains the mine safety production. This paper aims to provide a reference for groups design and control under similar conditions.

As an important technology of thick coal seam mining, fully mechanized mining with a large mining height has high mining efficiency. In order to study the roof safety control of large mining height working face, the 122106 working face of Caojiatan coal mine is taken as the engineering background. The numerical simulation method is used to analyze the control ability of roof subsidence when the support strength is 1.2 MPa, 1.4 MPa, 1.6 MPa, 1.8 MPa, 2.0 MPa, and 2.2 MPa. The results show that the support strength of hydraulic support is negatively correlated with roof subsidence. Through theoretical analysis of the mechanical model of the support and surrounding rock under the filling condition, it is shown that the height of the gap between the filling body and roof is the main influencing factor of roof subsidence: the smaller the height of the gap between the filling body and roof, the better the control effect on the roof. Through numerical simulation, the roof subsidence and surface subsidence under different filling rates are analyzed. The results show that when the filling rate increases to 80% the control of roof subsidence achieves better results. Taking production safety and economic benefits into consideration, when the reasonable support strength of the working face is determined to be 2.0 MPa and the filling rate is 80%, the safety control of the working face roof can be ensured.