In order to effectively prevent construction safety accidents, a safety responsibility network model of the five responsible parties was developed based on complex network topology characteristics to reveal the nonlinear coupling mechanism of their safety responsibilities, supported by accident case studies. First, relevant regulations, standards, and literature were analyzed to extract safety responsibility checklists for each party, and a responsibility matrix was constructed. Subsequently, Neo4j software was utilized to visualize the network diagram illustrating the safety influence relationships among the five parties in construction projects. Finally, topological metrics were applied to explore the interdependencies between the parties across multiple dimensions. The results indicate that the construction client plays a central and dominant role in project safety, with particularly significant influence on the contractor. The contractor's impact is primarily concentrated within its organizational boundaries, and its responsibility implementation is susceptible to external influences from other parties. The survey and design units indirectly affect other parties through the construction client. The supervision unit maintains systemic balance and regulation through monitoring and feedback mechanisms. The identification of critical decision-making nodes and passive response factors provides network topology-based evidence for differentiated management of responsible parties.

To address the issue of natural coal ignition in goaf under normal fault geological structures, the oxygen consumption rate and heat release intensity of coal samples were measured using a temperature-programmed oxidation device. Based on a porous media model of the goaf and the gas component transport equation, a numerical model for CO₂ injection via side pressure into the goaf influenced by normal faults was established. The numerical model was used to simulate the mechanism by which the variation in the distance between the working face and the fault affects the width of spontaneous combustion oxidation band in the goaf, and analyze gas migration characteristics under different CO₂ injection locations and flow rates. The results indicate that as the distance between the working face and the fault increases, the width of the oxidation band initially increases and then decreases, reaching a maximum width at 70 m from the fault. With the increase of CO₂ injection depth, the oxidation band width initially decreases and then increases again, reaching a minimum width when the CO₂ injection position is 40 m from the working face. Furthermore, with the increase of CO₂ injection volume, the width of the oxidation band width decreases following a negative exponential trend. When the CO₂ injection rate is 1000m³/h and CO₂ volume fraction at the working face is below 0.4% for safety, width of the oxidation band reaches its minimum.

In order to reduce the risks associated with the continuous growth of airport flight area size and flight volume, the safety resilience assessment of airport flight areas was carried out. First, risk factors were identified by analyzing the historical data of airport flight zones. Second, key risk factor weights were quantified, and a SD-based safety resilience assessment model for airport flight zones was constructed to propose safety resilience indicators. Then, the safety resilience of airport flight zones was assessed through simulation analysis, and targeted enhancement strategies were proposed. Finally, a large domestic airport flight area was taken as the research object to assess its safety resilience. The results show that among the personnel factors, the performance of the flight crew has the greatest impact on the level of operational safety resilience. By controlling the flow in the controlled airspace, enhancing safety awareness and increasing management inputs, the operational safety resilience of the flight area is improved by 9.11%. Among the environmental, equipment and management factors, the degree of improvement of the equipment updating mechanism has the greatest impact on the operational safety resilience level. By accelerating the frequency of equipment renewal, improving equipment deficiencies and increasing management inputs, the operational safety resilience of the flight area is increased by 21.49%.

To address the issues of complex environmental interference and low recognition rates in early-stage tunnel fire detection, an improved YOLOX-based detection method, YOLOX-T, was proposed. The proposed method incorporated a NAM into the YOLOX network to suppress environmental noise and interference, thereby enhancing the model's robustness. A weighted BiFPN was integrated to improve multi-scale feature extraction and fusion. Furthermore, an α-IoU(Intersection over Union) loss function was employed to enhance the detection accuracy of early-stage tunnel smoke and flames, which often exhibit indistinct contours. Addressing the scarcity of publicly available datasets, a tunnel fire dataset encompassing both real-world and simulated scenarios was constructed through web data acquisition, simulated fire experiments, and the augmentation of existing datasets. Experimental results on the self-built dataset demonstrate that, compared to the original YOLOX model, the YOLOX-T method achieves improvements of 1.89% in mean Average Precision (mAP@0.5), 0.88% in mAP@0.5~0.95, 4.57% in precision, and 5.45% in recall. The improved algorithm can achieve better detection performance.

In order to effectively improve the comprehensive prevention and control ability of coal mine water disaster accidents, the complex network and robustness analysis method were used to explore the key causes of accidents. According to the report of 111 typical coal mine water disaster cases in China in the past 40 years, an unweighted directed network of coal mine water disaster was constructed. The weights of each cause, such as degree, clustering coefficient and betweenness centrality at different scales of the network were calculated, and the key causes were excavated. The damage of intentional attack to the cause network under three different strategies was evaluated, and the optimal solution of systematic prevention and control was sought. The results show that the coal mine water disaster cause network has the characteristics of small world, and the causes are closely related. The attack ranked by degree has the greatest damage to the model. The corresponding key causes include organizing workers to carry out risky operations, failing to implement the management responsibility of water prevention and control technology, failing to carry out water exploration and drainage work as required, illegal organization of production operations, imperfect safety management institutions, inadequate hidden danger investigation, loopholes in coal mine safety supervision and supervision, and failure of higher-level units to perform safety management responsibilities.

To address the current challenges of lacking unmanned detection systems amid frequent forest fires and inefficient personnel evacuation during uncontrolled fire scenarios, this article proposes a forest fire safety detection method based on collaborativeMUAVs and an optimized shelter location strategy. A dynamic forest fire spread model coupled with multiple influencing factors is developed on the NetLogo platform. MUAVscollaborative search mechanism, grounded in an improved ant colony algorithm, is enhanced by introducing attractive pheromones (guiding searches toward fire clusters) and repellent pheromones (avoiding redundant paths), thereby optimizing the transfer probability of unmanned aerial vehicle (UAV) flight directions. Additionally, a flight model incorporating obstacle avoidance and water-carrying capacity-speed constraints was established. A dynamic evacuation simulation environment was constructed using geographic information system (GIS) data from Rhodes Island, Greece. Experimental results demonstrate that the improved ant colony algorithm reduces convergence time by 15% and 14% under 50% and 60% tree density scenarios, respectively, while search coverage increases by 35.02% and 32.16%. Furthermore, optimized shelter placement combined with the A^* algorithm-based evacuation strategy reduces the overall mortality rate by 2.525%.

To investigate the methane DDT distance and maximum explosion pressure (P_max) in shale fractures, a multi-scale adjustable 3D planar slit detonation system was developed. Experiments with methane-oxygen premixed gas under 4 different hydraulic diameters, along with numerical simulations, were conducted to examine shale gas combustion under high pressure. Results show that methane-oxygen premixed gas can sustain self-propagating explosion within a hydraulic diameter range of 1.9 to 11.43 mm. Both P_max and peak pressure rise rate increase linearly with initial pressure. Under a hydraulic diameter of 11.43 mm, P_max closely approaches theoretical detonation pressure. As the hydraulic diameter decreases, the P_max-to-initial pressure ratio decreases. The initial pressure and the DDT distance follow a power-law relationship. Increasing the initial pressure or reducing the hydraulic diameter can shorten the DDT distance, thereby accelerating the DDT. The simulation shows that methane-oxygen premixed gas explosions can produce an overpressure of 330 MPa, capable of fully fracturing rock cracks.

To improve the safety of air traffic operations, a delay level prediction method based on the combination of spatiotemporal association rule mining and deep learning was proposed. Firstly, the average flight delay time and delay rate were selected as airport delay metrics, and their spatial-temporal correlation characteristics were analyzed. Secondly, the airport delay levels were identified based on Fuzzy-C Means (FCM)clustering algorithm, and the spatiotemporal association rules of airport delay were mined based on (FP(Frequent Pattern)Growth) algorithm. Thirdly, sample data was constructed based on association rules and delay time series, which was put into LSTM model to predict the future airport delay levels. At the same time, attention mechanism was introduced into the prediction model to learn the influence of different rules on prediction. Finally, the actual US flight data were collected for example analysis. The results show that the average prediction accuracy of overall delay levels reaches 0.91 and the prediction accuracy of different periods is all larger than 80%. The connection weight of the attention layer network reflects the influence of each rule on the prediction, which can be used to explain the prediction results.

To address the oil-type gas emission hazard in coal-oil-gas coexisting mines, a quantitative risk assessment technology has been proposed. Firstly, the stability of the coal seam roof and floor strata was identified as a key parameter for evaluating oil-type gas gushing. The horizontal resistivity distribution uniformity was used to characterize strata stability. A direct current resistivity method was employed to investigate the resistivity distribution characteristics of the roof and floor strata, and a dynamic quantitative detection method for strata stability based on the resistivity variation coefficient was proposed. Additionally, comprehensive consideration of geological structures was integrated, supplemented by static parameters such as mining-induced damage depth, mechanical properties, permeability of the strata, and fault structures. Analytic Hierarchy Process (AHP) based on variable weight theory was employed to quantitatively calculate the weight of each factor relative to the evaluation indicators, thereby establishing a comprehensive quantitative evaluation method for oil-type gas gushing risks. Finally, the proposed method was applied to quantitatively assess the oil-type gas gushing risks in two typical areas of the Huangling mining area. The results align with field data from actual oil-type gas extraction boreholes, validating the method's reliability.

To definite the diffusion characteristics of blasting fumes in downward drift filling mining stopes, taking the downhole filling mining method of Longshou Mine in Jinchuan as an example, numerical simulations and field tests were conducted. The spatial distribution of airflow in drifts and layered roads was studied,and the diffusion patterns of CO and NO₂ in drifts were analyzed. Furthermore, the effects of ventilation shaft locations and drift lengths on CO diffusion were explored, and the ventilation parameters of the Longshou Mine were determined. The results indicated that the airflow field in drifts and layered drifts can be divided into the inflow zone, neutral zone, and return zone. The airflow velocity in the drift. shows the S-shaped distribution, with higher velocity at the bottom, lower in the middle, and moderate at the top. In the vertical cross-section of the drift, the CO volume fraction continuously increases with height. While horizontally, it exhibits a "decrease-then-increase" pattern from the inner to outer side. At the drift waistline, the CO diffusion velocity shows a logarithmic decreasing trend with ventilation time.NO₂ is primarily concentrated below the midline of the drift, and its diffusion velocity is significantly faster than that of CO. The CO diffusion rate is negatively correlated with both the distance from the ventilation shaft to the drift entrance and drift length. When the distance between the ventilation shaft and the drift entrance is ≤40 m, and the drift length is ≤55 m, the CO and NO₂ concentrations in the natural ventilation blasting fumes are below the standard limits below after 30 minutes.

Based on the high and steep slope project of an open-pit slope in cold region, 30 freeze-thaw cycle tests were conducted. The temperature range was set from -30 ℃ to 20 ℃.Subsequently uniaxial variable upper limit cyclic loading-unloading tests as well as synchronous acoustic emission monitoring tests were carried out. Slope rock masses with fracture angles of 0, 25, 50 and 75° were used in potential slip zone. The freezing-dynamic (freeze-thaw cycles and cyclic loading and unloading) combined damage and deterioration characteristics and mechanical properties of slope rock mass were explored in macro and mesoscopic scales. Furthermore, the crack initiation, propagation and failure modes of fractured rock mass were studied. The results show that as the fracture angle increases, the freeze-thaw damage effect on the fractured rock mass gradually decreases, while the compressive strength and elastic modulus exhibit a linear increasing trend with the maximum deformation of fatigue resistance of 0.558 3% at 75°. Compared to ordinary uniaxial loading, the compressive strength of fractured rock masses under cyclic loading and unloading condition decreases by 5.6 MPa. The Felicity ratios of different rock masses decrease with the increase of cyclic levels, and the Felicity ratios at the final failure stage were all below 0.7. As the cyclic loading level increases, the increment of cumulative dissipated energy decreases with the increase of fracture angle. The rock masses mainly exhibit tensile failure, but when the angles exceeded 25°, there is a trend of transformation from tensile and mixed failure to shear failure.

In order to investigate the impact of drill pipe rotation on coal particles transport efficiency during negative pressure sampling, CFD and DEM were used to explore the effects of drill pipe rotational speed and coal particles mass flow rate on gas-solid flow characteristics. The results show that: the maximum axial airflow velocity inside the drill pipe decreases and stabilizes as the transport distance increases, while the maximum tangential velocity rapidly decays and vanishes. As rotational speed increases, the axial airflow velocity remains largely unchanged, but the tangential velocity significantly increases. With higher rotational speed, the spiral flow of coal particles becomes more pronounced, the length of the vortex region increases, and the length of the suspension region decreases. The number of coal particles entering the drill pipe decreases, and the distribution of particles above 5 m/s increases and becomes more dispersed. During rotation, the actual solid-to-gas ratio inside the drill pipe is lower than the set value, and coal particles transport efficiency increases and then decreases with the rise in rotational speed.

To solve the issues that the bias, drift, gain, sticking and mutation fault modes of the current sensor in a battery pack are difficult to detect, recognize and evaluate, a comprehensive diagnosis strategy based on model fusion was proposed. A normal battery model with current as input and voltage as output (CIVO) was established. Based on the one-to-many relationship between the current sensor and batteries in the pack, the cumulative sum of the log-likelihood ratios of the residuals of the voltage of each cell was used as the detection index. A bias/drift fault model and a gain fault model with voltage as input and current as output (VICO) were established. Based on the residual variance of fault current, the model matching was performed on each fault mode. The quantitative evaluation of the bias, drift and gain modes were achieved by introducing a fault parameter to the fault model. The results show that based on CIVO, the five fault modes can be reliably detected. The sticking mode takes the shortest detection time and the drift mode requires the longest detection time, attributed to the slow-change characteristics of the fault current. Based on VICO, five fault modes can be accurately recognized. The quantitative evaluations of the bias, drift and gain modes are highly accurate, with the evaluation results of 0.396 2 A (experimental value 0.4 A), 1.641 7×10^-4 (experimental value 1.5×10^-4) and 0.201 6 (experimental value 0.2), respectively.

In order to improve the interpretability of excavation deformation prediction, this study developed an interpretable machine-learning model aimed at predicting the deformation of excavation retaining walls. A comprehensive analysis was conducted to evaluate the influence of different feature variables on the prediction outcomes. Firstly, a large number of excavation support structure parameters were used as a dataset, and 80% of the dataset was used to build a prediction model for the maximum lateral deflection of the retaining wall using the XGBoost (eXtreme Gradient Boosting)model. Then, the model was tested based on the remaining 20% of the dataset, and the accuracy of the model was evaluated by four indicators, i.e., the coefficient of determination, bias factor, mean absolute percentage error, and root mean square error. Finally, combined with the XGBoost model, the SHAP(SHapley Additive exPlanations) method was applied to complete the global explanation of the excavation feature variables, the partial analysis of individual samples, and the analysis of interaction effects of feature variables. The results show that the proposed method can provide both global and local explanations for the deformation prediction of excavation. At the global level, it not only provides the importance ranking of feature variables, but also gives the distribution of SHAP values. At the local level, the deformation prediction results of individual samples are decomposed into the base value and the contribution of each feature variable, which can quantify the impact of individual feature variables.

To prevent the impact of external risks on cluster supply chain network, the propagation mechanism of external risks in cluster supply chain network was explored based on cascade failure theory. Feasible strategies to curb the spread of risks in cluster supply chain networks under external risk impacts were also explored through numerical simulation using Python. The results indicate that implementing risk tolerance enhancement strategies in important enterprises cannot prevent the eventual collapse of the network, but can effectively slow down the speed of risk propagation and reduce the impact of each step in the risk propagation process. However, the implementation of risk mitigation strategies by important enterprises can effectively prevent the spread of risks in the network and prevent the collapse of clustered supply chain networks. The implementation of risk tolerance enhancement strategies in the global supply chain can effectively prevent the spread of risks in the network and prevent network collapse. Although establishing a supply chain risk sharing mechanism cannot prevent cluster supply chain network paralysis under deliberate attacks, it can effectively reduce the number of failed nodes under random attacks and prevent cluster supply chain network paralysis.

In order to solve the problem of increasing difficulty of water injection in deep coal seam and poor inhibition effect of emission, based on the theoretical method of wetting modification of coal seam, wetting agent was used to act on gas-bearing coal body to inhibit gas desorption and migration. The fluorocarbon FS-3100 surfactant with strong wettability was used to test the influence of wettability on the gas-water migration process in coal through gas-containing coal desorption test and fracturing fluid displacement flow test, and to explore the change mechanism of coal wettability on gas-containing coal desorption efficiency and flow capacity. The results show that the surface tension of water is reduced to 17.9 mN /m after adding wetting agent in water, and the contact angle of coal water is only 3-3.5°. After the wetting agent was used to modify the coal body, the desorption rate of gas in the coal is significantly reduced. The gas desorption rates of Xinjing and Baode coals are reduced to 46.08% and 39.2%, respectively, which are 8.4% and 9.8% higher than that of water. After the wetting agent acts on the coal body, the displacement flow rate of the Xinjing coal sample increases from 8.59% to 14.10%, and the Baode coal sample increases from 10.65% to 16.67%, and the water injection capacity of the coal body is significantly enhanced. The wetting agent molecules are adsorbed on the surface of the coal matrix, which reduces the interfacial tension between coal and water and the surface energy of the coal body, so that the friction resistance of the water molecules flowing in the pores and fissures of the coal body is reduced, and the water molecules can infiltrate smaller-scale pores and produce stronger water lock effect. Finally, the gas desorption and migration adsorbed on the pores and coal surface are inhibited, and the gas plugging effect is formed.

In order to solve the problems that machine learning exists in the hazard intelligent prediction field of tunnel water inrush, such as relatively simple models and imperfect prediction accuracy, a prediction model based on the stacking ensemble learning was proposed. Firstly, the tunnel water inrush disaster dataset was established by collecting 232 groups of water inrush disaster data from 95 tunnels, and the data was preprocessed. Then, 3 base learners and 2 meta learners were selected to train 8 sets of stacking ensemble models in different combinations, and 6 sets of optimal ensemble models were selected. Finally, the optimal stacking ensemble model was selected by comparing and analyzing the prediction results of 6 groups of parameters optimized and stacking ensemble model with the grid search parameters and the 5-fold cross-validation hyperparameter optimization model. The results show that SVM(Support Vector Machine )+NB (Naive Bayes) + LR (Linear Regression) ensemble model is obtained after the optimal single model SVM is improved with the stacking ensemble learning method. Its accuracy, recall, and F₁ score are 0.94, 0.91, and 0.92, respectively. The overall prediction effect is better than that of other compative models, and it can accurately predict the hazard level of tunnel water inrush.

To reveal the distribution characteristics of ceiling radiant heat flux in full-scale tunnel fires, the numerical simulation research was carried out. Longitudinal ventilation velocities, heat release rates and effective fire heights were changed. The distribution of ceiling radiant heat flux in road tunnel vehicle fires, the maximum ceiling radiant heat flux and its position were analyzed. The influences of the longitudinal wind speed, the heat release rate, and the effective fire height on each parameter and their important degrees were revealed. Results show that under the natural ventilation, affected by the heat release rate and effective fire height, the radiant heat fluxes under the ceiling above the fire source surface show a uniform distribution, or the law that the middle radiant heat fluxes are low while the two sides are high. Under the longitudinal ventilation, the ceiling radiant heat flux increases and then decreases. The peak value of the heat flux occurs near the center of fire source and the downstream of fire source. At large effective fire source heights, the peak values of the maximum ceiling radiant heat flux are obtained under the condition of wind speed lower than the critical velocity. While reducing the effective fire height and increasing the heat release rate, The peak value of maximum ceiling radiant heat flux individually occurs both when the wind speed is less than and when it is greater than the critical wind speed. When the wind speed below the critical velocity, the maximum ceiling radiant heat flux presents right above the surface of the fire source, and it is weakly affected by the heat release rate, longitudinal ventilation and the effective fire height. When the wind speed above the critical velocity, the maximum ceiling radiant heat flux increases with the heat release rate, and it is less affected by the wind velocity. The research results can provide some reference basis and data supports for tunnel fire prevention and control and tunnel safety design.

To improve the safety of urban medical waste recycling and disposal, an optimization methodology for the medical waste transportation network was proposed, where the facility location, vehicle-routing and vehicle acquisition were simultaneously optimized. Firstly, according to the environmental transmission characteristic of medical waste viruses and uncertainty of urban emergency response time, BP neural network model was used to simulate the dynamic evolution process of risk, and a four-dimensional prediction model was designed. Secondly, introducing vehicle volume and capacity constraints, an optimization model, minimizing the total cost and risk, was developed by two-commodity flow formulation. The solution procedure was also developed by improving the NSGA-II algorithm based on the complexity of model. Finally, a case study in Shanghai and several tests were provided to demonstrate the workability. The computational results show that the new model and approach can provide multiple efficient plans within 672 seconds, and they are sensitive to some parameters. Compared to the traditional risk assessment, new model can provide a reduction of 3.47% and 13.04% in total cost and risk respectively. Using the risk prediction technique, a decrement of 7.41% in total risk can be achieved when comparing to current policy. New algorithm can reduce the CPU time by at least 49.44% and keep stable performance in solving problems of different scales while comparing to traditional multi-objective optimal methods.

In order to reduce the risk of boil-over accidents in storage tanks and develop efficient prevention and control strategies, two-dimensional transient numerical simulations of the boiling process of oil-water two-phase system in closed containers were carried out using Fluent software. The effects of different tank types, initial water layer thickness, oil-water ratio and other factors on the boiling process were analyzed through numerical simulation. The phase distribution, temperature field and hydrodynamic behavior were studied during the whole process of the combustion triggering to the end of the boil-over. The results show that for the same tank type, the higher the water content of the oil, the smaller the intensity of boiling overflow. When the tank boils over, the oil-water interface undergoes violent instability, and the two phases of the medium in the container are interspersed with each other, presenting a chaotic state. The liquid near the tank wall of boils upward first, with the peak temperature of the liquid phase up to 400 K. When the horizontal storage tank boils over, the overall temperature of the liquid phase is lower than that in the vertical storage tank. The onset of boiling overflow in the horizontal tank occurs earlier than in the vertical tank. Therefore, the boiling overflow in the vertical tank is more dangerous.

In order to solve the index ambiguity problem in the risk assessment process of downward layered mining filling body stability, the filling body stability assessment model was constructed. Firstly, 12 factors affecting the stability of the filling body, such as cohesion, exposed area, stress ratio, etc., were selected as risk evaluation indicators to establish the evaluation index system. Secondly, the cloud model theory was introduced to calculate the cloud digital characteristics of each indicator, and the comprehensive weight was obtained by using G1-RF, and a comprehensive evaluation model of the stability of layered mining filler was constructed based on the G1-RF combined empowerment cloud model to determine the stability risk level of the mine filler. Finally, the evaluation method was applied to the stability analysis of the mine filler in the actual project. The results show that the comprehensive evaluation method can effectively solve the problems of indicator ambiguity and weak relevance in the process of risk assessment, more accurately and quickly evaluate the stability of the filling body, and realize the visualization of the risk level.

In order to improve the safety of existing large-span steel grid structure stadiums and gymnasiums, from the three perspectives of foundation, superstructure and enclosure structure, 24 factors influencing layer indexes were constructed, and the safety evaluation index system was weighted by game theory comprehensive subjective and objective weighting method. The importance grade of components was divided by load transfer path analysis, and the critical value of safety grade of different indexes was strictly defined, and the matter-element extension evaluation model was established. Based on the matter-element extension model, the data collection and model application of five existing large-span steel grid structure stadiums were carried out, and the stadium was taken as an example for empirical analysis. The results show that the safety grade of the stadium is grade II under the normal load (working conditions 1, 2), but the safety grade is grade III under the extreme load (working condition 3), and the corrosion and deformation of the components need to be monitored. The agreement rate between the evaluation results of the five existing large-span steel grid structure stadiums model and the on-site inspection results by experts is 91.35%. The research confirms that the model can effectively quantify the structural safety of existing large-span steel grid structure stadiums under complex working conditions.

To reduce traffic accident risks, on-road experiments were conducted to investigate the differences in potential risk perception ability between skilled and unskilled drivers under two typical risk scenarios: dynamic motorcycle-following and parallel overtaking. A wearable eye tracker was employed to collect drivers' dynamic visual parameters, with their visual characteristics analyzed across different scenarios. The results demonstrate that skilled drivers exhibit significantly stronger risk perception abilities than unskilled drivers in both scenarios. Specifically, in the dynamic motorcycle-following scenario, skilled drivers show a higher probability of fixating on distant areas ahead, enabling better prediction of upcoming traffic conditions. In the parallel overtaking scenario, skilled drivers display shorter fixation durations, along with greater horizontal search breadth and vertical search depth. Moreover, the proportion of fixation time on rearview mirrors is significantly higher for skilled drivers compared to unskilled drivers, indicating superior visual search efficiency and enhanced rear traffic monitoring capability. Evaluations using the grey near-optimal comprehensive evaluation method reveal that skilled drivers achieved significantly higher scores in hazard perception ability.

In order to improve the emergency management capability for the purpose of urban underground comprehensive pipeline corridor construction, the general framework of pipeline corridor safety system was established by analyzing the regional emergency management system within the pipeline corridor, utilizing the theory of knowledge synergy, and combining the regional synergistic emergency management system with the scenarios of comprehensive pipeline corridors from the perspective of the application in comprehensive pipeline corridors. The framework was structured around the core of “risk-emergency-crisis”. The work breakdown structure-risk breakdown structure(WBS-RBS) method was adopted to complete the identification and grading of the risk of emergency management of the pipeline corridor. The probability of the risk of emergency management of the pipeline corridor was evaluated using analytic hierarchy process(AHP) method. Finally, the safeguards required for the establishment and operation of the multi-agency emergency knowledge management system of the integrated corridor were described across three dimensions of organization, technology, and supervision. The results show that the knowledge synergy theory is demonstrated to have a great advantage in building a regional collaborative emergency management system model. The safety system framework of the urban comprehensive pipeline corridor is designed to predict all kinds of risks in the structure, equipment and operation of the pipeline corridor. It is also capable of maintaining safety and stability during emergencies.

To understand evacuation behavior and psychological characteristics in the event of a sudden fire in an ancient town, and to accurately identify the factors influencing personnel evacuation behavior, a questionnaire survey on evacuation behavior was conducted in an ancient town in Zhejiang Province. The survey focused on three aspects: basic population characteristics, awareness and attitudes toward fire risk in the ancient town, and evacuation behavior. Subsequently, the correlation between evacuation behavior and factors such as age group, frequency of fire drills, and understanding of fire risks in ancient towns was comprehensively analyzed using the Pearson's Chi-square test and eXtreme Gradient Boosting (XGBoost) machine learning algorithm. The results indicate that tourists (77.78%) are more aware of fire risks in the ancient town than local residents and merchants (22.22%). Age has a significant impact on returning behavior. The higher the level of education, the less likely respondents are to engage in overtaking behavior during evacuation (decreasing from 85% to 33%). Individuals who have not proactively familiarized themselves with emergency exits are more likely to exhibit herd and 'habitual' behavior when choosing evacuation exits (increasing from 8% to 33% and from12% to 20%, respectively). Special attention should be given to fire safety training for local residents and merchants. Fire safety management in the ancient town should be strengthened, and evacuation plans tailored to different age groups should be developed to improve evacuation capabilities in the event of a sudden fire in the ancient town.

In order to effectively identify construction risks and prevent safety accidents, this study investigated the safety evaluation method for metro station construction in confined space under 500 kV high-voltage lines. First, a safety evaluation index system for metro station construction under high-voltage lines was established by integrating AHP and 3MET(Management,Man,Machine,Environment,Technology) method. Then, the weights of indicators were calculated using the IOWA operator, and the cloud model was applied to determine evaluation standard clouds and cloud characteristic values, thereby deriving the comprehensive safety evaluation grade. Finally, the proposed method was validated through a case of the University Town Central Road Station on Chongqing Metro Line 15, with corresponding mitigation measures proposed for high-risk indicators. The results demonstrate that the evaluation model accurately identifies high-risk sources in metro station construction under high-voltage lines, with the safety evaluation grade classified as lower risk, consistent with on-site conditions. Key considerations for safety measures include the electromagnetic field effects of high-voltage lines, risk monitoring and protection, mechanical equipment selection and its safety functions. The application of multi-dimensional layered protective structures could effectively mitigate construction risks under high-voltage environments.

In order to improve the safety condition of highways, 26 320 highway traffic accident records in France from 2018 to 2022 were selected as the research object. Three representative algorithms were selected to impute missing values in the data, including the RF algorithm, the expectation-maximization (EM) algorithm, and the K-nearest neighbors (KNN) algorithm. The impact of different imputation algorithms on data stability was compared based on the changes in variable variance before and after imputation. The Apriori association rule algorithm was then applied to analyze the causes of highway accidents with different severity levels using the completed dataset. The results indicate that after missing value imputation, the RF algorithm demonstrates superior stability. Compared to the model trained on the original data, the accuracy is improved by 5.66%, the recall rate is increased by 9.22%, and the F1 score is enhanced by 9.91%. It is found that passenger vehicles are more likely to cause property damage accidents; motorcycles are prone to cause injury accidents on roads with lower speed limits and fatal accidents on roads with higher speed limits. The use of safety equipment is significantly related to the severity level of accidents.

To evaluate the seismic resilience of urban group buildings under different demands, an integrated assessment methodology was developed for urban building clusters' earthquake resistance. A tree-augmented naive Bayesian method was employed to construct a BN model that rapidly predicted structural damage states under various target earthquakes through multi-parameter analysis including building types, construction eras, and structural configurations. Multiple resilience evaluation theories and methods-comprising the resilience index method, resilience element rating method, risk-resilience analysis method, and group buildings resilience indicator evaluation method-were integrated to comprehensively assess the seismic resilience of group buildings in a specific street of Fengtai District, Beijing. Comparative analyses were conducted to examine the advantages, limitations, and applicability of these approaches. The results indicate that the resilience index method proves suitable for rapid assessment of building recovery capacity, while the resilience element rating method facilitates intuitive visualization of structural resilience levels. The risk-resilience analysis method demonstrates superior capability in handling disaster uncertainties and stochastic characteristics, whereas the group buildings resilience indicator evaluation method emphasizes the critical influence of functional requirements on comprehensive assessments.

In order to reduce the various risks associated with storm and flood hazards, an ontology model of storm and flood hazard risk is proposed. Storm and flood disaster risk elements were identified from five aspects: environmental factors, equipment and facility factors, management factors, human factors, and information factors. The concepts of storm flood class and state space were given respectively, and data attributes and object attributes of each hazard element are defined. And visualization of the storm flood ontology model based on Protégé application. The results show that the storm and flood ontology model can express the risk elements and attributes clearly and accurately, and retrieve the links between the risk elements scientifically and quickly, so as to help the decision-makers respond effectively. Providing a data storage and retrieval platform for heavy rainfall and flood disaster risk assessment, it can realize user knowledge reuse and sharing, and provide a reference for emergency response and scientific decision-making by the government and emergency management authorities at the same time.

In order to improve the emergency management ability and effect of local governments and promote the modernization of emergency management systems and capacity, the game theory was used to construct a game model. The evolutionary game relationship between audit institutions, local governments and surrounding governments was discussed. The root causes of the three were explored through model stability analysis and numerical simulation. Corresponding countermeasures and suggestions were put forward according to the analysis results. The results show that the benefit of emergency management is the key to the evolution of the behavior of local governments and surrounding governments. In addition, the probability of active supervision by audit institutions and the size of audit costs have an impact on the behavior choices of the two governments, and the willingness of the two governments to participate in emergency management also has an impact on each other. For audit institutions, the cost of auditing and the degree of accountability are key factors that influence their behavioral choices.

To gain a deep understanding and evaluate the command capability and current status of counties in response to flood disasters, this paper identified 12 major influencing factors through literature analysis. Social network analysis was conducted using Ucinet software, and Netdraw software was utilized for visualization to depict the social network relationship diagram among these influencing factors. By calculating the in-degree and out-degree, as well as centrality of the influencing factors, indicator classification was performed. Drawing on the maturity model of command and control capability, an assessment model based on social networks for the maturity of county-level flood disaster emergency command capability was developed. Puyang county was selected as the empirical research object. Through questionnaires and interviews, a total of 342 data entries from 24 townships in Puyang County were collected to evaluate the county's flood disaster emergency command capability. The results indicate that it is feasible to classify indicators hierarchically through social network analysis. The established indicator system is scientifically and accurately reflected in the comprehensive operational level of county-level flood disaster emergency command. The assessment model can effectively evaluate the maturity levels of emergency command capability.

To grasp the development trends in urban emergency management research under extreme circumstances, a systematic review of the main studies and research characteristics in this field has been conducted. Using the scientific knowledge mapping tool VOSviewer software and Web of Science Core Collection (Wos CC) as the data retrieval source, this article performed a visual analysis on research literature on emergency management in extreme situations. The results show that in recent years, the research on urban emergency management in extreme situations has been increasing year by year, but the overall number of articles published is not much, with a total of 145 articles from 2004 to 2024. The United States, China, Australia are among those with the highest number of research papers, making significant contributions to the development of this field. Obvious research teams have been formed, mainly in China and the United States, but there is a lack of international cooperation. Through cluster analysis, the research topics can be summarized into five aspects, namely, disaster prevention management, disaster prediction models, urban vulnerability analysis, smart cities, and the impact of climate characteristics. Currently, as the probability of cities facing extreme risks increases, it is urgent to strengthen international cooperation, fully utilize the latest technologies such as artificial intelligence and big data, and construct a theoretical system for urban emergency management capable of facing extreme situations.