Hunan Vocational College of Safety Technology

The stability of the mine pillar is a key issue related to the safe mining underground. Reinforcing the mine pillar is an important method to improve its stability. To reveal the reinforcement effect and mechanism of concrete-encased mine pillars, laboratory tests and field engineering application studies were conducted. Four groups of tests were carried out considering different sample sizes, rock strengths, encasing material strengths, and encasing layer thicknesses. The results demonstrated that mortar-encased rock specimens exhibited significant improvements in peak stress and axial peak strain. The reinforcement effectiveness was inversely proportional to the specimen’s height-to-diameter ratio and rock strength, while directly proportional to the wrapping material strength and layer thickness. Orthogonal range analysis revealed the sensitivity ranking of influencing factors as follows: encasing thickness > specimen height-to-diameter ratio > encasing material strength > rock strength. After encasing, the failure mode transitioned from integral failure to fragmented failure, with encased specimens demonstrating enhanced energy absorption capacity and bearing capacity. Increasing encasing strength and thickness induced a tendency towards plastic deformation failure. The encased rock-specimen system can be regarded as a parallel composite structure of rock and mortar layer. This configuration not only increases the bearing capacity of the mortar layer but also significantly enhances the rock’s intrinsic bearing capacity through confining pressure provided by the encasing material, which grows substantially with improvements in encasing material strength and thickness. Field applications in mines demonstrated that concrete-encased reinforcement of key area pillars can effectively control overall ground pressure in mining operations. The research results of this paper indicated that the reinforcement of mine pillars by concrete wrapping can enhance the stability of mine pillars and provide a new idea for improving the safety of mines.

The propagation and attenuation characteristics of blast stress waves in geotechnical media directly influence the fracture behavior of the medium and serve as a crucial basis for optimizing blasting parameter design. To examine the attenuation characteristics of cylindrical blast waves in rocks, the indoor blasting experiments were conducted using sandstone with cylindrical charges. Digital image correlation technology was employed to successfully capture the full-field strain evolution around the borehole during blasting, and the strain–time curves of the rock surrounding the borehole were obtained. To account for the influence of blasting stress wave loading rates on dynamic elastic modulus, Split Hopkinson Pressure Bar tests were performed to establish a precise relationship between dynamic elastic modulus and strain rate. By analyzing the attenuation of the peak strain, a stress wave attenuation equation within the fractured zone was developed, and the stress wave attenuation index was examined. The results indicated that the experimental method effectively simulated the blasting process of cylindrical charges. The strain wave propagation was accompanied by energy transformation, where the descending phase of the strain–time curve represented the rapid energy input to the rock near the borehole due to blast loading, whereas the ascending phase reflected the radial release of elastic energy, further promoting the development of circumferential cracks, albeit at a lower energy release rate than the descending phase. As the distance from the blast center increased, both the dynamic elastic modulus and strain rate of the rock under blast loading decreased, leading to differences between the attenuation characteristics of stress waves and strain waves, with the former following a power function decay. The complex nature of stress wave attenuation in rocks was primarily governed by physical attenuation properties, with the physical attenuation index exceeding the geometric attenuation index in crushed and cracked zones. Finally, the accuracy of the stress wave attenuation equation and the reliability of the experimental method were validated by analyzing the fracture morphology of the blasted specimens and the extent of the cracked zone.

Currently, cities’ security vulnerabilities are diverse, complex, and interrelated, making the building of resilient cities an essential requirement to deal with these risks and ensure the long-term growth of cities. China is making headway toward resilient urban governance, as evidenced by a focus on ecological hazards and widespread application in urban planning, social development, and other areas. However, as urbanization continues, traditional governance techniques confront the challenge of “lack of clarity, inadequate execution, and slow response.” To address these difficulties, this work introduces a novel dual-core-driven model of ‘defense system + organism’. By analyzing the main urban safety risks currently present in Changsha City and employing literature synthesis, field surveys and interviews, and comparative analysis, it investigates the application status of resilient urban governance and empirically constructs a ‘three-dimensional fifteen-wing’ governance system, achieving a transition from passive defense to an intelligent–adaptive governance paradigm.

Deep learning shows great potential in the field of structural damage recognition, but training requires a large amount of high-quality data. In this paper, we take the subway sleeper girder as the research object, and use the finite element method to construct the strain field damage dataset. Based on the strain equivalence hypothesis to determine the damage simulation method, study the impact of damage units on the strain field of the sleeper beam, through the setting of different damage conditions for simulation and analysis, to verify the effectiveness of the method in the construction of the dataset, and to lay the foundation for the subsequent deep learning to realize the automated identification of structural damage of the subway sleeper beam.

Aiming at the complex nonlinear characteristics of soft soil foundation under dynamic load, this paper proposes an adaptive compensation control method based on deep reinforcement learning, aiming to improve the response accuracy and stability of the foundation in a dynamic environment. By introducing the deep Q network (DQN), this method realizes the modeling and control optimization of the dynamic behavior of the foundation, and designs a dynamic load characteristic compensation algorithm to cope with complex load disturbance scenarios. The experimental results show that the proposed control method has significant advantages over the traditional PID control in terms of response error, convergence speed and compensation control quantity, especially in the case of fixed load disturbance and random dynamic load disturbance. It shows strong robustness and adaptability. The effectiveness of this method in complex dynamic control scenarios is verified by simulation experiments and performance evaluation, which provides technical support and theoretical basis for the intelligent control of soft soil foundation dynamic problems, and has important engineering application value.