In order to improve the efficiency of gas extraction, numerical simulation methods were employed to analyze the characteristics of changes in gas volume fraction, gas breakthrough time, coal permeability, CH₄ extraction rate, and extraction quantity under different gas injection pressures. The results show that after flue gas injection, the volume fraction of CH₄ shows a downward trend, while the volume fractions of CO₂, N₂ and O₂ show an upward trend. The increase in injection pressure will shorten the breakthrough time of each gas in the flue gas, reduce the permeability of the coal seam and increase the gas extraction volume. At 180 days of extraction, the gas injection pressure increased from 0.5 MPa to 2.5 MPa, the coal seam permeability decreased from 8.722 7×10^-17 m² to 8.711 5×10^-17 m², and the extraction volume increased from 449.01 m³ to 715.30 m³. The gas drainage rate shows the variation characteristics of rising - falling - rising - falling. From the perspective of the influence of gas injection pressure on drainage, the gas injection pressure has a relatively small impact on the drainage in the first two stages.

In order to reduce tunnel lining diseases and extend the safe service life, a diagnostic framework for tunnel lining diseases was constructed, and maintenance and reinforcement methods for diseased tunnels were explored from the perspectives of diagnosis and reinforcement. The application status of technologies for diagnosing tunnel diseases and maintaining/reinforcing tunnels was reviewed. The advantages and disadvantages of various detection technologies were systematically identified, and disease diagnosis methods and reinforcement measures were summarized. The results show that non-destructive testing technologies have evolved from single/semi-automatic to integrated/intelligent systems. Acoustic wave, ground-penetrating radar, and laser scanning technologies have different efficiency and accuracy characteristics. Although automatic detection systems integrating photogrammetry and multi-sensor fusion have gradually been applied, they face challenges in the synchronous detection of multiple diseases. Traditional maintenance and reinforcement technologies are difficult to meet the requirements of low cost and high safety. Although new technologies such as Fiber Reinforced Polymer (FRP) are advantageous, they have issues such as high cost and insufficient environmental adaptability.

To prevent accidents caused by miners' unsafe behaviors induced by physical fatigue and reduce the impact of physical fatigue on safe behaviors abality, a physical fatigue response test was carried out. First, forty adult male participants were equally divided into two groups, with each group induced to develop MEF and MPF respectively. Subsequently, a series of operational performance tests were conducted to measure attention level, reaction speed, judgment accuracy, and body coordination ability. Then, paired sample t-tests were employed to analyze pre- and post-fatigue behavioral data, investigating the effects of physiological fatigue on safety-related performance capabilities. Finally, evidence-based management recommendations were formulated according to the analytical results. The findings reveal that muscular endurance fatigue significantly compromise attention levels through its effects on mean pupil diameter(P<0.01), blink frequency(P<0.01), and saccadic rate(P<0.05). While muscular explosive power fatigue also demonstrates measurable influence on attentional performance as evidenced by altered pupil diameter(P<0.01), its effect magnitude is statistically inferior to that of endurance fatigue. Also, muscular explosive power fatigue reduces the body coordination ability, as both hand-eye coordination ability(P<0.05) and fine operation ability(P<0.05)have significantly declined. However, muscle explosive fatigue does have a certain improvement effect on reaction speed, with simple reaction time increasing by 0.028 s(P<0.01) and comprehensive reaction time increasing by 0.020 s(P<0.01). Physiological fatigue has no significant impact on judgment accuracy.

To balance stope stability with mining economy and to inform stope span design in similar engineering projects, a comprehensive investigation was conducted on the stope span and its stability, using the -200 m level of a copper mine as a case study. The Mathews stability chart method was adopted to evaluate the stope stability by calculating the rock mass stability index and hydraulic radius, thereby defining a reasonable range of stable stope spans. Based on the analysis, four stope span scenarios were designed and numerically simulated using finite difference software to study the stope responses. Comparative analyses were performed on roof displacements and the characteristics of plastic zones in the surrounding rock. The results show that as the stope span increases from 12 m to 18 m, vertical displacement of the roof increases gradually, and the volume of the plastic zone fluctuates slightly. However, when the span reaches 20 m, a significant increase in roof displacement and a sudden expansion of the plastic zone which leads to through-going failure is observed, severely compromising roof stability. An optimal stope span of 18 m is recommended for the -200 m level of the copper mine. The stope span design of mining should consider the influence of factors such as orebody occurrence conditions, safety and efficiency.

To achieve high-precision recognition of wall hollowing sound signals and improve multi-category detection accuracy, a multi-feature fusion method for wall hollowing detection based on BO-SVM was proposed. First, the collected knocking sound signals from different wall types were preprocessed by pre-emphasis, framing, and windowing, and both MFCC and MSC were extracted. The two acoustic features were concatenated at the frame level and normalized to construct a fused feature dataset. Then, a BO-SVM classification model was developed, and the kernel function penalty and parameters were optimized using five-fold cross-validation to establish the MFCC+MSC-BO-SVM model. Finally, classification experiments were conducted using hollow and non-hollow from multiple wall types, including cement, coating, marble, and ceramic tile walls. The results show that the fused features outperform single features in terms of accuracy, recall, and F₁-score. The MFCC+MSC-BO-SVM model achieves an overall recognition accuracy of 96.36%, representing improvements of 6.61%, 9.58%, 15.27%, 13.90%, and 5.02% compared with standard SVM, Random Forest, K-Nearest Neighbor, Grid Search-optimized SVM, and Chaos Particle Swarm Optimization SVM respectively. Furthermore, the BO method can obtain the optimal parameter combination with fewer iterations, showing superior convergence and classification stability.

In order to explore the role of intelligent and digital empowerment in the modernization of emergency management systems and capacities, this study identified the key influencing factors and clarifies their interrelationships. CiteSpace was applied to cluster and analyze relevant domestic and international literature, while NVivo12plus, combined with expert consultations, was used to identify 16 influencing factors from four dimensions: environment, mechanism, technology, and data. DEMATEL method was employed to calculate the influence, dependence, centrality, and causality of each factor, and ISM was used to construct a hierarchical structure and reveal their internal logical relationships. By integrating the DEMATEL and ISM models, five key factors were identified, namely political and legal frameworks, cultural awareness, demand and supply, management strategies and planning, and managerial attention and support, which rank highest in multiple indicators and play a significant role in promoting modernization.

To enhance urban resilience against disaster chains, this study proposed an analytical framework and methodology for identifying influencing factors from a disaster chain perspective. Firstly, a comprehensive list of urban resilience influencing factors was established by focusing on the core capacities of resilient cities within disaster chain contexts. Secondly, an improved fuzzy DEMATEL-ISM integrated method was introduced to identify top-level factors, clarify hierarchical structures and interaction pathways among influencing factors, and analyze resilience enhancement approaches from economic and demographic perspectives. Finally, taking the resilience construction practice of Chengdu in the context of disaster chain as an example, the rationality of this theoretical analysis was verified. Results indicate that the top-level design factors, aimed at addressing disaster chains, primarily focus on prevention, preparedness, and adaptation. Fundamental factors include insurance protection, emergency command in the disaster chain scenario and government departments' ability to jointly handle chain disruptions to reduce disasters, adequacy of emergency resource preparation, and ecological environment restoration and governance. Direct influencing factors encompass housing quality, medical assistance capacity, emergency evacuation capacity, and basic living security. The most effective approaches to enhance economic resilience in disaster chains are strengthening insurance protection and accelerating infrastructure repair, both promoting economic recovery. Enhancing population resilience relies primarily on measures such as housing security, life support, emergency evacuation support, and basic living security. Chengdu maintains relatively sound resilience construction under disaster chain scenarios, though certain aspects require further optimization.

In order to more accurately capture the evolutionary patterns of disaster risks and facilitate scientific risk assessment, this study innovatively integrated the dual natural and social attributes of disasters. By leveraging the outcomes from event extraction, event relationship identification, and event generalization, a method based on causal association strength was developed to mine event logic. Subsequently, an emergency event evolutionary graph was constructed for assessing chain-reaction disaster risks. Combined with a similarity matching method that incorporates the multi-dimensional attributes of disaster events, an empirical study was conducted using earthquake disasters as a case study. The results demonstrate that the proposed disaster risk assessment framework based on the event evolutionary graph can rapidly identify historical earthquake events with high similarity to the Luding earthquake case. Furthermore, by leveraging the intrinsic interconnections and evolutionary paths among risks revealed by the graph, the framework can predict secondary disasters (e.g., landslide-dammed lakes) and derivative social risks (e.g., rumor propagation).

In order to realize scientific and efficient post-disaster transfer of the population after the occurrence of major natural disasters, the emergency bus evacuation planning considering the transfer urgency was studied according to the different severity of the disaster. Firstly, a continuous emergency bus evacuation model was established with the goal of minimizing the evacuation completion time and evacuation delay satisfaction degree. Secondly, a HS-GA based on transfer urgency coding was designed and applied for problem-solving, and the effectiveness of the algorithm was verified using examples of different scales. Finally, the evacuation planning considering the transfer urgency was compared with those without considering the transfer urgency based on the case. The results show that the HS-GA is better than Genetic Algorithm (GA) and Neighborhood Search GA (NS-GA). According to the case solved by HS-GA, a comparison is made with evacuation planning that does not consider transfer urgency. When transfer urgency is considered, the evacuation delay dissatisfaction degree is reduced by 51.4%. Additionally, shorter evacuation completion times are observed at both Class I and Class II disaster sites. The average time taken to arrive at the shelter is also found to be shorter. At the same time, the dissatisfaction degrees of evacuation delay considering the urgency of transfer under different emergency bus quantity are all lower. The evacuation model considers the evacuation priority of the hard-hit areas under different severity of the disaster, and can make reasonable dispatch arrangements under the circumstance of limited rescue vehicles, which is more objective and in line with the actual situation.

In order to study the scientific and reasonable location selection of regional emergency rescue stations and base-level emergency rescue stations in the planning and construction stage of the subway network, firstly, the topology model of the subway network was constructed based on complex network theory. Relevant indicators were selected to construct the evaluation system of node importance, and GWR model was used to predict passenger flow to supplement the research data. Secondly, aiming at the highest efficiency, optimal coverage and lowest cost, the multi-objective location model of regional emergency rescue stations was established, and NSGA-Ⅱ algorithm was used to solve the optimal scheme. At the same time, a hybrid analysis calculation method was designed to solve the comprehensive importance value parameters of nodes in the model, and based on the site selection results of regional emergency rescue stations, the enumeration method was used to solve the optimal solution of base-level emergency rescue stations. Finally, Xi'an metro planning network was taken as an example to verify the calculation. The results show that compared with the optimal solution in the case of "no consideration of node importance", the regional location solution with "consideration of node importance" saves about 2.19% in cost, improves the rescue efficiency by about 9.43%, and exceeds the optimal solution with about 4.66% in coverage. Therefore, the location scheme of "considering node importance" is more in line with the actual needs of the subway network.