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The Weibull distribution is used to describe the heterogeneity of rock hydraulics and embedded into the Fish program which is based on the discrete element method. The developed program overcomes the limitation of the Universal Distinct Element Code (UDEC) software regarding the number of parameter groups, which cannot exceed 50. A method for parameter assignment of heterogeneous rocks is proposed together with a method for estimating the initial flow rate value of heterogeneous models. Based on the established heterogeneity calculation model, the influence of block homogeneity, hydraulic aperture homogeneity, and stress on the seepage characteristics is studied. The results indicate that under zero stress conditions, the flow rate is positively correlated with N 0.5 showing a strong linear relationship. The linear relationship is gradually enhanced with the increase in the shape parameters. The relationship between the flow rate and shape parameters is logarithmic with a correlation coefficient greater than 0.9654. The relationship between the flow rate and the axial pressure and confining pressure can be described by quadratic and cubic polynomials, respectively, based on which we further discuss the variation characteristics of equivalent hydraulic apertures under the various axial pressures, confining pressures, and shape parameters. Article highlights A method for the interaction between MATLAB and UDEC data is proposed, which can realize the construction and parameter assignment of heterogeneous models, This program also breaks through the limitation that the number of parameters in UDEC can not exceed 50. A method for estimating the initial total flow value Q of a heterogeneous model is proposed. The seepage characteristics of heterogeneous rocks and the relationship between flow rate and axial pressure and confining pressure are analyzed under different stress environments.

Constructional backfill mining with cemented gangue backfill column can solve the environmental issues caused by mining activities and the accumulation of waste gangue at a low cost. To study the deformation and instability properties of cemented gangue backfill columns during the advancement of coal mining face, five step-by-step loading paths were adapted to mimic the different loading processes of the roof. The lateral deformation at different heights and axial deformation of the sample were monitored. The results show that the deformation and instability of the backfill column have the properties of loading paths and are affected by the step-by-step loading path. When stress-strength ratio (SSR) is less than 0.6, the lateral of backfill column shrinks during the creeping process. In high-stress levels, lateral creep strain develops faster than axial creep strain. The backfill column has characteristics of axial creep hardening and lateral creep softening during the step-by-step loading process. The instantaneous deformation modulus and instantaneous Poisson’s ratio show an upward trend. The strength of backfill column under the step-by-step load is related to loading paths and is no less than uniaxial compressive strength. The non-uniformity of the lateral deformation of backfill column leads to excessive localized deformation that mainly occurs in the middle, causing the overall instability. The development of cracks of backfill column under step-by-step load could be divided into 4 stages according to SSR. Under different step-by-step loading paths, the axial creep strain rate is nearly a constant before entering the accelerated creep stage. A nonlinear creep constitutive model with a creep strain rate trigger was proposed to depict the development of axial strain under step-by-step load. This research could provide a scientific reference for the design of the advancing distance and cycle for the hydraulic support, and reinforcement of the backfill column.

To investigate the failure mechanism of reinforced coal pillars and compare their pre- and post-reinforcement bearing capacity, a series of acoustic emission (AE)-monitored uniaxial compression tests were carried out on three types of specimens, namely coal, concrete, and concrete-encased coal. The results show that the stress–strain curve of encased coal sample shows obvious double-peaked behavior, different from encasing concrete or coal alone. This characteristic can be attributed to differences in the mechanical properties of the internal coal and encasing concrete. The encasing concrete is of a high stiffness than coal, absorbing high stress than coal during deformation. The encasing concrete has a higher elastic modulus and will take a larger share of the load before failure occurs. The internal coal with lower strength and peak strain will cause it to fail first, and the load will transfer to the stronger concrete in the surrounding. The evolution of the AE energy release and AE event locations both confirm this failure process. In addition, the strength of encased coal samples falls between coal and concrete, and the stress values of the double peak can be estimated by the presented formula, The maximum supporting capacity of the reinforced coal pillar was closely related to the radius ratio and elastic modulus ratio of the individual parts, and the corresponding bearing capacity of the reinforced coal pillar can also be calculated. This study provides a useful reference for understanding the failure mechanism of reinforced coal pillars.

Water inrush disasters poses a great threat to the safe exploitation of coal resources. To solve this problem, the transient electromagnetic method (TEM) was proposed to accurately detect the water accumulation in the goaf. The electromagnetic response characteristics of different water-filled goaves were studied by electromagnetic field theory, numerical simulation and field verification. Through the models of 100% water accumulation, 50% water accumulation, 0% water accumulation, 100% water accumulation with collapsed rock, 50% water accumulation with collapsed rock and 0% water accumulation with collapsed rock goaf, the characteristics of induced voltage attenuation curves were studied. Meanwhile, the relationship between the attenuation voltage value and area of the transmitting coil, the depth of the goaf, the background resistivity, and the delay time were also simulated. The results illustrate that the attenuation curve of induced voltage presented a regular exponential decay form in the 0% water accumulation model but existed abnormal exaltation for voltage in water-filled model. Through the linear fitting curve, it can be seen that the abnormal intensity of the induced voltage becomes stronger as the distance between the measuring point and the center of the target decrement. Moreover, the abnormal amplitude of the induced voltage increases with the rise of the water accumulation and collapsed rock will weakly reduce the low-resistivity anomalous effect on the water-accumulated goaf. In addition, the response value of the attenuation voltage increased as the area of the transmitting coil increases, but decreased with increasing delay time and increasing background resistivity and depth of the target body. The field detection results of the Majiliang coal mine also confirmed the theoretical analysis and the numerical simulation.

Nowadays, adversarial examples of images which are generated by adding special perturbations on their hosts make great impact on many deep neural network (DNN)‐based computer vision tasks and bring security risks. With the deep insight into this field, researchers realize that many simple image processing methods can easily reduce the attack success rate. Therefore, studies on robust adversarial examples that are able to defend destruction become the emphasis. However, most existing methods focus more on the distortion in user transmitting procedures and physical deformation such as JPEG compression and brightness. Given that scale transformation is one of the commonest measures in data transformation and enhancement, a two‐step algorithm is proposed to eliminate the effect caused by resizing during the model inferring process. The first step is to select pixels that can affect the classification result through a DNN. As a binary classification task, the network predicts whether the pixel deserves to be modified or not. The second one is to strategically compute the amplitude of noise and add it to the host image. Experiments verify that this method resists the scale transformation and guarantees the invisibility of humans as well.

A variety of dust control methods are often applied in coal mines, among which the application of wet scrubbers has proven to be an efficient technology for the removal of dust in airstreams, rather than diluting or confining the dust. In this paper, a wet scrubber design was developed. Based on a self-designed experimental test platform, the total dust concentration, respirable dust concentration, air volume, and average pressure drops of wet scrubbers with 12, 16, 20, and 24 blades were measured under different water intake conditions. The results show that the different water intake levels have only minimal effects on the air volume of the wet scrubbers. However, increased water intake had improved the dust removal efficiency of the wet scrubbers with the same number of blades. The wet scrubber with 16 blades was found to have the best dust removal efficiency at a water intake level of 1.35 m 3 /h. Its total dust and respirable dust removal efficiency reached 96.81% and 95.59%, respectively. The air volume was 200.4 m 3 /min, and the average pressure drop was determined to be 169.4 Pa. In addition, when the wet scrubber with 16 blades was applied in a coal preparation plant in China’s Shanxi Province, it was observed that the total dust concentration had fallen below 8.1 mg/m 3 , and the respirable dust concentration had fallen below 5.9 mg/m 3 . Therefore, the results obtained in this research investigation provide important references for the use of wet scrubbers to improve coal production environmental conditions.

China is endowed with a large quantity of residual coal resources that require upward mining. The stability of interburden strata structures and accurate determination are crucial for safe mining. Therefore, we established a mechanical model of disturbed voussoir beam structures of interburden strata in upward mining. The model was solved, and stability analysis and instability mechanism analysis were conducted. Based on this model, a new method for determining the feasibility of upward mining was proposed and applied to the upward mining of coal seam No. 7 in Baijiazhuang Coal Mine. A physical simulation experiment and numerical simulation were conducted to validate the method. Through research, it was found that the model had two instability mechanisms: rotation instability and sliding instability. When the disturbance load crossed the critical block of the structures, the model was most likely to experience sliding instability. When the disturbance load acted entirely on the critical block, rotation instability was more likely to occur. The result of the determination, performed using the new method, showed that there was no rotation instability or sliding instability in the interburden strata structures of coal seam No. 7, indicating that the coal seam could be mined upward. This result was consistent with the determinations using the statistical method, “three-zone” method, and balanced surrounding rock method. Physical and numerical simulations revealed that the upward mining of coal seam No. 7 caused the subsidence, rotation, and separation compaction of the interburden strata structures but that the structures remained stable. The results indicate that the proposed model and method have accuracy and applicability, being able to guide the practical feasibility determination of upward mining.

The mechanical properties of fissured sandstone will deteriorate under water–rock interaction. It is crucial to extract the precursor information of fissured sandstone instability under water–rock interaction. The potential of each acoustic emission (AE) parameter as a precursor for instability in the failure process of fissured sandstone was investigated in this study. An experimental dataset comprising 586 acoustic emission experiments was established, and subsequent classification training and testing were conducted using three machine learning (ML) models: AdaBoost, MLP, and Random Forest (RF). The primary parameters for identifying the instability risk state of fissured sandstone include acoustic emission ringing count, energy (mV·ms), centroid frequency, peak frequency, Rise Angle (RA), Average Frequency (AF), b value, and the natural/saturated state of fissured sandstone: state. To enhance data utilization, a 10-fold cross-validation method was employed during the model training process. The machine learning models were developed and designed to identify the instability risk of fissured sandstone under the natural and saturated states. The results demonstrated that the established RF model was capable of identifying fissured sandstone instability risks with an accuracy of 97.87%. Feature importance analysis revealed that state and b value exerted the most significant influence on identification results. The Spearman correlation coefficient was utilized to assess the correlation between input features. This study can provide technical support to identify the risk of instability of fissured sandstones under both natural and saturated water conditions. Based on the models developed in this study, it is possible to implement an early warning method for instability in fissured sandstone that meets realistic working conditions. Compared with the traditional empirical and formulaic methods, the machine learning method can more quickly process huge amounts of AE data and accurately identify the damage state of fissured sandstone.

The accurate calculation of the height of fractured water-conducting zone (FWCZ) is of great significance for mine optimization design, water disaster prevention, and safety production of the coal mines. In this article, a height-prediction model of FWCZ based on extreme learning machine (ELM) is proposed. To address the issues of low prediction accuracy and challenging parameter optimization, we optimized the ELM model using the gray-wolf optimization algorithm (GOA), whale optimization algorithm (WOA), and salp optimization algorithm (SOA). These optimization algorithms mitigate the issues of slow convergence, poor stability, and local optimality associated with traditional neural networks. The mining depth, mining height, overburden strata structure, working face length, and coal seam dip angle are selected as the main controlling factors for the height of FWCZ. A total of 42 fields-measured samples are collected and divided into 2 subsets for training and validating with a ratio of 36/6. The prediction capability of GOA-ELM, WOA-ELM, and SOA-ELM models are evaluated and compared, and the results show that the calculation results of the three models are optimized compared with the ELM model. The prediction capability of GOA and WOA are similar, while the prediction results of SOA-ELM are better than the other two models, and the relative errors of the test sets are all less than 10%. Therefore, the SOA-ELM model is finally applied to predict the height of FWCZ formed after the mining of No.15 coal seam in Xinjian Coal Mine. Finally, we verified the prediction results using measured data from the borehole television detection instrument, which showed good consistency. This provides further evidence of the effectiveness of the swarm intelligence optimization algorithm in predicting the height of FWCZ.

It is of great significance for ecological environment protection to clarify the regional evolution characteristics of shallow water under the disturbance of multi-working face mining. In this paper, the catastrophe theory method, GIS spatial analysis function and FEFLOW numerical calculation method were comprehensively used to study the instability risk and evolution law of shallow water systems in the Zhuan Longwan Coal Mine. The results show that: the Zhuan Longwan Coal Mine is divided into five areas (small risk area, light risk area, middle risk area, heavy risk area and special risk area) based on catastrophe theory, among which the middle risk area has the largest area of 16,616,880 m2, and the special risk area has the smallest area of 1,769,488 m2. Based on the results of catastrophe zoning, the evolution law of shallow water under multi-surface disturbance in different zones is expounded. In the middle-risk area, the water level drop at measuring point 4 is the largest, which is 0.525 m, and the water level drop at measuring point 5 is the smallest, which is 0.116 m. The study aims to provide a basis for regional coal development planning and research on the method of water-retaining coal mining.