A structural reliability evaluation method for gas pipeline segments with multiple defects, based on Copula functions, was proposed to address the characteristics of the interrelationship between defects and the difficulty in quantifying the highly nonlinear coupling effects of multiple defects. First, based on the limit state equations of pipeline segments corresponding to different defects, Monte Carlo (MC) method was used to calculate the failure probability and probability distribution of pipeline segments under the influence of a single defect. Second, the probability distribution of each defect were treated as random variables, and the Copula functions were employed to study the impact of the correlation between different defects on the pipeline failure probability. The joint failure probability of the pipeline segment under the coupling effect of two defects was obtained. Third, the reliability of the pipeline segment with multiple defects was determined based on the second-order narrow-bound theory. Finally, using this method, the impact of the correlation between corrosion and crack defects on the structural reliability of gas pipeline segments was explored, and the variation of pipeline reliability was analyzed under different defect depths and wall thickness ratios (0.2-0.8). The results show that, under the condition that the failure probabilities of the defects are at the same order of magnitude and do not differ significantly, the correlation of the same type of defects is stronger, and the coupling effect has a greater impact on the pipeline failure probability. Compared with traditional series methods that assume independence between defects, which underestimate the pipeline reliability, the results of this method are more consistent with practical needs. This approach provides theoretical support for the stable operation of gas pipeline segments with multiple defects.

To explore innovative pathways for the development of emergency management discipline and address its current bottlenecks, this study systematized the theoretical logic of emergency management discipline construction based on value co-creation theory and designed a four-dimensional framework encompassing goal optimization, community building, full-process value co-creation, and technological empowerment. The results indicate that China's emergency management discipline construction currently faces challenges including fragmented value cognition, insufficient stakeholder coordination, unbalanced resource allocation, and superficial technological empowerment. An integrated theoretical framework incorporating value coupling, community construction, full-process co-creation, and technological empowerment can be constructed to guide disciplinary practice. Applying value co-creation theory can address deep-seated contradictions in discipline construction, thereby promoting its transformation from fragmentation toward systematization.

To enhance the accuracy and robustness of miner behavior recognition in underground belt conveyor areas and reduce the occurrence of unsafe behaviors, a multimodal feature fusion-based recognition method was proposed. The method integrated RGB and skeletal modalities, where an improved Efficient Attention(EA)-SlowFast networkwas employed to extract RGB features, and an improved Mobile-Enhanced(ME)-YOLO was utilized for miner detection. The detected miners were subsequently processed by Lite-HRNet for pose estimation to extract skeletal keypoints, which were further encoded using an improved Depthwise Convolution and Channel Prior Attention Spatio-Temporal Graph Convolutional Network (DC-STGCN) to obtain skeletal features. Finally, late fusion of RGB and skeletal features was performed for unsafe behavior recognition. In the experimental design, both public datasets (Human Motion Database 51(HMDB51), University of Central Florida 101 Action Recognition Dataset(UCF101)) and a self-constructed underground coal mine unsafe behavior dataset were adopted for validation. The results demonstrate that, compared with single-modal baseline models, EA-SlowFast and DC-STGCN achieve recognition accuracies of 71.6% and 68.3% on HMDB51, and 88.3% and 85.4% on UCF101, respectively. The multimodal fusion model outperforms the unimproved fusion baseline, achieving 75.4% and 93.5% on HMDB51 and UCF101, respectively. On the self-constructed dataset, ME--YOLO attains a Mean Average Precision(mAP)@0.5 of 91.8% with an inference speed of 38 f/s, satisfying real-time requirements. The fusion model further achieves an accuracy of 90.6%, confirming the effectiveness of the proposed approach in complex underground belt conveyor environments.

In order to study the accident risks caused by the simultaneous explosions of multiple storage tanks in chemical industrial parks, a research method for the overpressure damage evolution process of multiple tank explosions was proposed. First, the explosion damage probability of a single storage tank and the explosion damage probability distribution of a single storage tank were calculated. Then, considering the simultaneous explosions of multiple storage tanks, the overpressure damage evolution process of multiple tank explosions was studied. Finally, the regional damage probability distribution of the simultaneous explosions of multiple storage tanks was obtained. The results show that the evolution result is mainly based on the regional damage probability distribution of Tank o₂, and the subsequent regional damage probability distribution decreases gradually. Through the research, the tank explosion sequence set with the maximum damage probability, the corresponding damage probability set and the tank damage probability distribution set are obtained, and the calculation of the regional damage probability distribution of the simultaneous explosions of multiple storage tanks is realized. The complexity of the storage tank explosion process in chemical industrial parks is caused by the differences in the energy provided during the initial explosion and the differences in the overpressure damage evolution process of storage tank explosions.

In order to increase the systematicity and flexibility of disaster response behavior at the grassroots level, a triangle model of county disaster resilience was constructed. Taking the rainstorm in Jianchang, Liaoning Province in 2024 as an example, social network analysis method was used to establish an association network among rainstorm disaster events, county disaster bearing bodies and emergency management plans. The system dynamics model reflecting the evolution process of county disaster resilience was designed by introducing the time series dimension, and the individual attributes of network nodes were used as the basis for model evaluation. The results show that the county disaster resilience shows an evolutionary trend of slowly decreasing first and then steadily increasing, and the growth rate gradually accelerates. During the pre-disaster warning stage, disaster events, disaster-bearing bodies, and emergency management plans respectively possess the characteristics of suddenness, exposure, and absorption. Organizational preparations, communication preparations, material preparations, and publicity preparations have the greatest room for optimization. During the disaster response stage, disaster events, disaster-bearing bodies, and emergency management plans respectively possess coupling and uncertainty, sensitivity, and adaptability characteristics. There is still room for improvement in the capacity to evacuate the masses, communication guarantee mechanisms, and the resilience of power facilities. During the post-disaster recovery stage, disaster-bearing bodies and emergency management plans respectively possess adaptability and adjustment characteristics. At this time, it is necessary to optimize the planting structure of crops and enhance the flood resistance capacity of roads. Emergency management plans always have the characteristic of learning throughout the entire cycle of disaster response.

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.

To enhance operational efficiency and ensure flight safety, a multi-objective flight slot optimization model was developed by comprehensively considering the requirements of airlines, air traffic control authorities, slot coordination authorities, and airports, subject to airport capacity, terminal waypoint capacity, and safety requirements. The model aimed to achieve balanced and efficient slot allocation within airport clusters, and its effectiveness was validated using actual flight data from Beijing Capital International Airport, Beijing Daxing International Airport, and Tianjin Binhai International Airport in the Beijing-Tianjin-Hebei airport cluster. The results show that the optimized slot distribution becomes more balanced, with peak-hour flight numbers reduced by 7%, the average adjustment per airline decreased by 18.2%, and the total adjustment across the airport cluster reduced by 10.4%. At Beijing Capital Airport, capacity utilization efficiency during peak hours is improved, with redundancy increasing from 2.9% to 8.3%. At Beijing Daxing Airport, slot distribution becomes more balanced, with redundancy increasing from 1.6% to 6.5%. At Tianjin Binhai Airport, the optimized flight volume is maintained within capacity limits, improving safety margins, with redundancy rising from -13.9% to 8.3%. The model effectively enables the rational allocation of flight schedules, significantly enhancing operational safety and resource utilization efficiency.

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 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.