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Microseismic data are widely employed for assessing rockburst risks; however, significant disparities exist in the monitoring capabilities of seismic networks across different mines, and none can capture a complete dataset of microseismic events. Such differences introduce unfairness when applying the same methodologies to evaluate rockburst risks in various mines. This paper proposes a method for assessing the monitoring capability of seismic networks applicable to heterogeneous media in mines. It achieves this by integrating three gradient boosting algorithms: Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), and Categorical Boosting (CatBoost). Initially, the isolation forest algorithm is utilized for preliminary data cleansing, and feature engineering is constructed based on the relative locations of event occurrences to monitoring stations and the working face. Subsequently, the optimal hyperparameters for three models are searched for using 8508 microseismic events from the a Coal Mine in eastern China as samples, and 18 sub-models are trained. Model weights are then determined based on the performance metrics of different algorithms, and an ensemble model is created to predict the monitoring capability of the network. The model demonstrated excellent performance on the training and test sets, achieving log loss, accuracy, and recall scores of 7.13, 0.81, and 0.76 and 6.99, 0.80, and 0.77, respectively. Finally, the method proposed in this study was compared with traditional approaches. The results indicated that, under the same conditions, the proposed method calculated the monitoring capability of the key areas to be 11% lower than that of the traditional methods. The reasons for the differences between these methods were identified and partially explained. [ABSTRACT FROM AUTHOR]

Background: Coal mining requires safe and effective roadway support to ensure production and worker safety. Anchor support is a common method used for controlling the roof of coal seams. This study aims to analyze the effectiveness of different anchor support schemes and provide a theoretical basis for designing safe and effective roadway support. Methods: The authors used a computer simulation tool called FLAC3D to simulate and analyze the spacing between anchor bolts, anchor bolt length, anchor cable length, and effective roadway roof control, and support the schemes at the western wing roadway in the no. 15 coal seam of no. 1 mine of Ping'an Coal Mine. Results: The study found that using different combinations of anchor bolts and cables with varying lengths could effectively control the deformation of the roadway surrounding rock, depending on the spacing between layers of the coal seam. The most effective support schemes were recommended depending on the specific conditions. Conclusion: The study provides a theoretical basis for the design of anchor support in coal mines, which can ensure the safety of production and improve roadway stability. The results could be useful for other mining operations facing similar challenges in roadway support and stability. [ABSTRACT FROM AUTHOR]

Sedimentary rocks provide information on previous environments on the surface of the Earth. As a result, they are the principal narrators of the former climate, life, and important events on the surface of the Earth. The complexity and cost of direct destructive laboratory tests adversely affect the data scarcity problem, making the development of intelligent indirect methods an integral step in attempts to address the problem faced by rock engineering projects. This study established an artificial neural network (ANN) approach to predict the uniaxial compressive strength (UCS) in MPa of sedimentary rocks using different input parameters; i.e., dry density (ρd) in g/cm3, Brazilian tensile strength (BTS) in MPa, and wet density (ρwet) in g/cm3. The developed ANN models, M1, M2, and M3, were divided as follows: the overall dataset, 70% training dataset and 30% testing dataset, and 60% training dataset and 40% testing dataset, respectively. In addition, multiple linear regression (MLR) was performed for comparison to the proposed ANN models to verify the accuracy of the predicted values. The performance indices were also calculated by estimating the established models. The predictive performance of the M2 ANN model in terms of the coefficient of determination (R2), root mean squared error (RMSE), variance accounts for (VAF), and a20-index was 0.831, 0.27672, 0.92, and 0.80, respectively, in the testing dataset, revealing ideal results, thus it was proposed as the best-fit prediction model for UCS of sedimentary rocks at the Thar coalfield, Pakistan, among the models developed in this study. Moreover, by performing a sensitivity analysis, it was determined that BTS was the most influential parameter in predicting UCS. [ABSTRACT FROM AUTHOR]

Coal-accompanying gas is an essential resource, with numerous mining methods. The practice has proved that injecting high-pressure air into the coal seam can replace and flush the gas in the coal seam, effectively solving the problem of inadequate single gas drainage in soft and low permeability coal seams. This paper uses the finite element method to solve the model, simulate and study the gas drainage by high-pressure air injection in the bedding drilling, and establish a fluid-structure coupling model for gas drainage by high-pressure air injection. The competitive adsorption of N2, O2, and CH4, diffusion and migration of CH4 in coal matrix and fissure, matrix deformation caused by CH4 adsorption, and desorption and control of coal deformation by applied stress are considered in the model. When the fixed extraction time is 600 days (d), the optimal spacing between the extraction hole and injection hole is 12.5 m. The safe extraction effect and minimum drilling amount can be ensured. It provides a basis for guiding gas drainage by injecting high-pressure air on-site. [ABSTRACT FROM AUTHOR]

Elastic modulus (E) is a key parameter in predicting the ability of a material to withstand pressure and plays a critical role in the design of rock engineering projects. E has broad applications in the stability of structures in mining, petroleum, geotechnical engineering, etc. E can be determined directly by conducting laboratory tests, which are time consuming, and require high-quality core samples and costly modern instruments. Thus, devising an indirect estimation method of E has promising prospects. In this study, six novel machine learning (ML)-based intelligent regression models, namely, light gradient boosting machine (LightGBM), support vector machine (SVM), Catboost, gradient boosted tree regressor (GBRT), random forest (RF), and extreme gradient boosting (XGBoost), were developed to predict the impacts of four input parameters, namely, wet density (ρwet) in gm/cm3, moisture (%), dry density (ρd) in gm/cm3, and Brazilian tensile strength (BTS) in MPa on output E (GPa). The associated strengths of every input and output were systematically measured employing a series of fundamental statistical investigation tools to categorize the most dominant and important input parameters. The actual dataset of E was split as 70% for the training and 30% for the testing for each model. In order to enhance the performance of each developed model, an iterative 5-fold cross-validation method was used. Therefore, based on the results of the study, the XGBoost model outperformed the other developed models with a higher accuracy, coefficient of determination (R2 = 0.999), mean absolute error (MAE = 0.0015), mean square error (MSE = 0.0008), root mean square error (RMSE = 0.0089), and a20-index = 0.996 of the test data. In addition, GBRT and RF have also shown high accuracy in predicting E with R2 values of 0.988 and 0.989, respectively, but they can be used conditionally. Based on sensitivity analysis, all parameters were positively correlated, while BTS was the most influential parameter in predicting E. Using an ML-based intelligent approach, this study was able to provide alternative elucidations for predicting E with appropriate accuracy and run time at Thar coalfield, Pakistan. [ABSTRACT FROM AUTHOR]

The traditional anchoring method of bolts has insufficient control over the surrounding rock of the coal roadway. Based on this background, full-stress anchoring technology of bolts was proposed. Firstly, a mechanical relationship model of a bolt-drawing, anchoring interface was established to obtain the equations of the axial force and obtain shear stress distribution as well as the decreasing-load transfer law of the anchoring section of bolts. Through studying the prestress-loading experimental device of bolts, we found that increasing the initial preload could increase the axial force under the same conditions and the retarded anchoring section could control the axial-force loss of bolts in the middle of the anchoring section. Under the full-stress anchoring mode, the effect of applying a pre-tightening force was better than that of applying a pre-tightening force under traditional anchoring methods. Moreover, FLAC3D (Fast Lagrangian Analysis of Continua 3D; ITASCA (Ita sca International Inc), Minnesota, USA) numerical simulation calculation was performed. Under the full-stress anchoring mode of bolts, the increased anchoring length reduced the damage of the anchoring section, with a wider control range of the rock formation and higher strength of the compressive-stress anchoring zone. Based on the above research, four methods for applying the full-stress anchoring technology of bolts in engineering were proposed. The full-stress anchoring technology of bolts in the coal roadway has been applied in the support project of the return-air roadway at working face 3204 of the Taitou Coking Coal Mine of the Xiangning Coking Coal Group, Shanxi. The maximum moving distance of the roof and floor of the roadway was reduced from 200 to 42 mm, and the maximum moving distance on both coal sides was reduced from 330 to 86 mm. The full-stress anchoring technology of bolts was able to control the surrounding rock in the coal roadway. [ABSTRACT FROM AUTHOR]

Research on drilling response characteristics of two-wing polycrystalline diamond compact (PDC) bit in different rocks is an important way to further understand the mechanism of rock-breaking, improve drilling efficiency, and identify the rock formation interfaces in coal mines. However, the research on the drilling response characteristics of two-wing PDC bits is relatively rare due to the harsh environment in coal mines. In this study, a series of two-wing PDC bits were used to drill in sandstone and sandy mudstone to study the response characteristic of sound pressure level, displacement curves, longitudinal vibration, and rock cuttings size. The collected data were processed by MATLAB based on the 3σ principle. The cuttings were collected through a newly designed cuttings collection device. The experimental results show that the sound pressure level and the longitudinal vibration are larger when drilling in the higher strength sandstone, which is opposite to the cuttings particles and the penetration rate. The reduction of drilling efficiency is more obvious when drilling in sandstone with a worn bit. Therefore, drilling efficiency can be improved by optimizing the bit structure so that the broken rock blocks flow out of the anchor holes as early as possible to avoid being broken into cuttings. [ABSTRACT FROM AUTHOR]