Chuanqi Zhu

Traditional methods for geostressfield inversion face issues such as weak physical interpretability and insufficient generalization ability. This study pioneers the application of Physics-Informed Neural Network (PINN) to this problem, developing a data- and physics-driven inversion algorithm. The framework incorporates a constitutive-equation-based regularized loss function as a hard constraint during training to ensure physical consistency. To address boundary load uncertainty, two quantification approaches—Bayesian linear regression and surrogate model optimization—are proposed to establish 95% confidence intervals for boundary coefficients. Verification based on simple three-dimensional models and actual geological models of mines shows that PINN inversion achieves a mean absolute relative error as low as 0.0772%, with an error of 15.67% under sparse sampling conditions—significantly lower than the 31.07% error of the traditional Back propagation neural network. This demonstrates excellent robustness and data efficiency. In the practical engineering application of complex geological bodies, the average error of principal stress inversion is 9.35% with a minimum error of 0.137%. All inversion results fall within the permissible accuracy range of engineering, and the stress distribution conforms to basic laws, with an average error of 0.453 in the constitutive relation. Compared with BP neural network and multiple linear regression methods, it shows obvious accuracy advantages. This method provides a new solution for intelligent ground stress prediction with high accuracy, high efficiency, and strong physical interpretability, and also lays the foundation for early identification of geological disasters.

Limited by the actual investigation of coal mine engineering, the measured data obtained are often based on small sample characteristics. How to probabilistically de-integrate the prior information to obtain meaningful statistical values has received increasing attention from geotechnical engineers. In this study, an optimal copula function identification method for multidimensional geotechnical structures of coal mine roofs under the Bayesian approach is proposed. Firstly, the characterization method of multidimensional roof parameter correlation structures is proposed based on copula theory, and 167 sets of measured data from 24 coal mines at home and abroad are collected to study the measured identification results using the Bayesian method. Secondly, Monte Carlo simulation is utilized to compare the correct recognition rates of the commonly used AIC criterion and the Bayesian approach under different correlation structures. Finally, the influencing factors affecting the successful recognition rate of the Bayesian approach are analyzed. The results show that compared with the traditional AIC criterion, the Bayesian approach has more marked advantages in correctly recognizing the multidimensional parameter structures of roofs, and the number of measured samples, the strength of correlation coefficients, and the prior information have a major effect on the correct recognition rate of the optimal copula function under different real copula functions. In addition, the commonly used Gaussian copula has a better characterization effect in characterizing the multidimensional parameter correlation structure of the coal mine roofs, which can be prioritized to be used as a larger prior probability function in the evaluation process.

The occurrence of severe mine pressure in stope is of high risk, and the mining depth is the main control factor which affecting the occurrence of mine pressure in stope. In order to study the influence law of mining depth on stope strata behavior, taking 11318E fully mechanized working face in Xieqiao Coal Mine of Huainan Mining Group as the engineering background, the stress distribution and evolution characteristics of stope surrounding rock under mining depth were studied by similar material simulation. And the evolution laws of mechanical characteristics of stope surrounding rock under different mining depths of 600 m and 1200 m were simulated. It is concluded that with the increase of mining depth, the damage range of surrounding rock on the mining field increases significantly. The initial collapse of the immediate roof above the working face is not affected by the mining depth. The periodic weighting caving step decreases with the increase of mining depth, and the height of two zones above the working face increases with the increase of mining depth. The stress transfer and evolution characteristics of the strata above the stope were revealed. The increase of front caving angle indicates that the pressure on the support of the working face is greater, indicating that the support of the working face should be strengthened with the increase of mining depth. The research results have a guiding role in the control of surrounding rock and disaster prevention of deep mine stope.

Strata control of ultrasoft coal seam has been a critical problem for mining and geotechnical engineers in years. In this paper, the No. 66207 longwall panel at Xinzhuangzi coal mine, Anhui, China, was used as an example for study. A systematic approach using laboratory testing, numerical simulation, and field validation was implemented to investigate the influences of roadway layout and presplit blasting on mechanical properties of surrounding rock. Based on the results, an inward-stagger roadway layout with presplit blasting on roof was proposed for the regional strata control. The investigation on the relationship between angle of repose of ultrasoft coal and water content showed that the angle of repose first increased then decreased with increasing water content. The peak value was observed at 17.659% water content, suggesting water injection into ultrasoft coal seam can improve the coal mechanical properties and rib stability. The “high resistance, integral beam, two-stage rib+roof support system” was design to replace the traditional equipment, which can support the ultrasoft coal seam. The combination of this system and proposed “difference stepping” mining technique was capable of preventing roof and rib from failure, as well as mitigating the rockfall during moving hydraulic support. Based on the field validation, it was found that the stress concentration coefficient was relatively low during mining process. This was able to effectively manage the mining-induced stress while improving the productivity three times than without the technique. There was also no failure event observed during mining, such that the safety of mine workers was improved significantly.

In order to solve the difficult problem of coal wall spalling of soft seam in deep mining, the occurrence mechanism of coal wall spalling in deep mining is studied based on the limit equilibrium theory of D‐P criterion. Through the analysis of the formula of plastic zone, it can be concluded that the coal wall spalling of soft coal seam is related to mining depth, mining height, stress concentration factor, internal friction angle of coal, cohesion, and the compressive strength of coal body. Also, the change of uniaxial compressive strength of soft coal with different moisture content is studied through laboratory experiments. The experimental results show that the uniaxial compressive strength of soft coal reaches the maximum value when the moisture content is about 3.3%. The comprehensive research shows that the cohesion and strength of coal mass can be improved by increasing the working resistance of support, water injection in advanced deep‐hole coal seam of two roadways, and pre‐grouting in shallow hole of coal wall. The control measures can effectively control the coal wall spalling of soft coal seam. The research results have important guiding significance for safe and efficient mining of soft coal seam.

The physical and mechanical properties of soft coal body constitute one of the most important factors inducing coal wall spalling. In order to explore the mechanical essence of coal instability disaster and stability enhancement of water injection, the 7^# coal in Huainan mining area is taken as the research object. Firstly, the distribution characteristics of coal particle size, point‐load strength, original water content, microstructure characteristics, and shear strength of coal under different water contents are measured by laboratory tests. Then, based on the test results, the cementation morphology and force evolution law of granular coal water in coal body are analyzed using liquid bridge theory. The results show the following: (1) With the increase of particle size, the mass ratio of granular coal increases gradually. The percentage of particle coal with particle size less than 2.5 mm accounts for 47.157%, fractal dimension is 2.172, and uniaxial compressive strength and tensile strength are 3.822 MPa and 0.165 MPa, respectively. (2) The coal body is dry (the original moisture content is 1.336%), containing a large number of loose particles, pores, fissures, and other microfabrics. This “low water content and multiporosity” feature is the essential reason for its low strength, fragmentation, and instability and disaster. (3) In the process of water content increasing from 0.966% to 26.580%, the shear stress‐displacement curve of coal body gradually changes from softening type to hardening type, and the failure type transitions from brittleness to ductility. The cohesive force increases first and then decreases, while the angle of internal friction almost has no change. (4) After reasonable water injection, the shape of liquid bridge in coal body changes into capillary tube, and the liquid bridge force reaches the maximum value, which transforms from a highly unstable bulk to a stable continuum. The research results have important theoretical significance and practical value for the safe and efficient mining of soft coal seams.

To study the effect of loading rate on the progressive damage and failure characteristics of coal, an ultrasonic detector and a camera were used to measure the P‐wave velocity and record the failure process of cuboid coal samples in uniaxial compression tests with five loading rates. The mechanical properties, damage process, and failure characteristics of the samples were analysed, and the mechanism of the advancing velocity of the working face in coal failure was discussed. The results show that, as the loading rate increases, the peak strength of the sample generally shows an increasing trend, but the elastic modulus changes irregularly. The sample is more prone to local failure before the peak strength. An increase in the loading rate rapidly promotes damage in the sample and accelerates the transition from internal damage to macroscopic failure, with no obvious effect on the ratio of damage threshold to corresponding peak strength. At low loading rates, the samples mainly experienced static failure; the failure form was spalling, and the failure range was wide. At high loading rates, the samples were prone to dynamic failure in the local area, manifested as the ejection of slabs and debris. A greater loading rate produced smaller and thinner slabs and a greater ejection velocity. Properly increasing the advancing velocity of the working face is conducive to reducing spalling to prevent large‐area roof fall, but it may increase the possibility of coal burst in local areas. The results of this study provide a reference for roof control and coal burst disaster prevention on the working face in deep coal mining.

In the loading and failure process of the coal and rock medium, with the increasing of the damaging and failure extent, the medium state gradually changed from the continuous, the quasicontinuous, and the discontinuous to the loose successively. To evaluate the damaging failure extent of the coal and rock mass and distinguish the state of the coal and rock mass medium, first, the medium state index was defined based on the acoustic wave propagation model. Then, based on the coal mass in the working face 8512 in the Chenjiagou Coal Mine in China, the acoustic character and the mechanical character experiments were conducted. Through experiments, the variation law of the ultrasonic wave velocity and the medium state index of the coal mass with the strain under the compressive state were acquired; with the increasing of the strain, the medium state indexes successively experienced the variation process including stabilising around 0, slow increasing, dramatic increasing, and stabilising around 1. Then, based on the variation law of the medium state index, the coal and rock mass medium state judgement model was constructed. This model was applied to judge the coal mass medium state. After it, the mudstones in the roof of the same working face were regarded as the research object, the mudstone medium state was divided into 4 types according to the medium state judgement model, namely, continuous, quasicontinuous, discontinuous, and bulk solid, and the mudstone failure state was basically consistent with the continuous, quasicontinuous, discontinuous, and bulk solid state. The adaptability and rationality of the judgement model was verified. Finally, engineering application of the judgement model was conducted. The distribution law of the top coal with different medium state was acquired. In the top coal that was 186 m away from the working face, it belonged to the continuous medium state. As for the top coal that was 6 m to 186 m far away from the working face, it was under the quasicontinuous medium state. For the top coal that was 3 m to 6 m far away from the working face, it was under the discontinuous medium state. For the top coal that was less than 3 m far away from the working face and behind the working face, it belonged to the bulk solid medium state. The state judgement model can successfully distinguish the medium state of the coal seam and mudstones in the roof in the working face 8512. This provided a new approach to evaluate the damaging failure extent of the coal and rock medium in the Chenjiagou Coal Mine.