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.

In order to improve the safety cognition level of underground caverns construction workers，safety requirement and safety capability were introduced as mediating variables，and occupational burnout was used as a moderating variable to construct a moderated chain mediation model. A structured questionnaire was designed using 5 scales: risk perception，safety requirement，safety capability，occupational burnout and safety cognition. A questionnaire survey and data analysis were conducted on 312 underground caverns construction workers，and SPSS 26.0 and AMOS 26.0 software were used to test the mediating effect and moderating effect.The results show that risk perception directly and positively affects the safety cognition of underground cavern construction workers，while occupational burnout plays a negative moderating role between the two. Risk perception also affects the safety cognition of construction workers through the independent mediating effect of safety requirement and safety capability，as well as through the chain mediating effect of safety requirement and safety capability. Therefore，motivating and improving the risk perception，safety requirement and safety capability of construction workers，while reducing their occupational burnout，can effectively improve their safety cognition level.

To reduce the safety risk of fireworks production enterprises and eliminate the subjectivity and static limitations of the current safety evaluation mechanism，an evaluation indicator system of the safety risk for firework production enterprise was constructed based on the grounded theory (GT). The evaluation values and weights of the evaluation indicators were calculated by applying the frequency-based Analytic Network Process-Back Propagation Neuron Network (ANP-BPNN) model. The dynamic evaluation of the safety risk level of firework production enterprise was achieved based on SD，and verified with an enterprise involved in a major fireworks explosion accident of fireworks. The results show that the dynamic evaluation model of safety risk for fireworks production enterprises can accurately capture the development trend of the safety risk. The interval between safety risk evaluations of fireworks production enterprises is flexibly adjusted based on predicted time for safety risk level to reach the higher risk. The continuous dynamic evaluation of safety risk can ensure that the enterprise always maintains a safe production state.

In order to examine how construction workers' safety motivation affects their behavior，SDT was used to divide the safety motivation into two types: autonomous and controlled. Subsequently，a hypothetical model linking workers' safety motivation，opportunity，ability，and behavior was constructed by integrating the analytical framework of the AMO theory. Finally，469 frontline construction workers were taken as the research subjects for data collection，and structural equation modeling (SEM) was used for hypothesis testing and empirical analysis. The results show that the autonomous safety motivation of construction workers is significantly positively correlated with safety behavior，and the controlled safety motivation of construction workers is significantly negatively correlated with safety behavior. Safety motivation and safety competence play a positive moderating role between workers' autonomous safety motivation and safety behavior，while they show a significant negative moderating relationship between workers' controlled safety motivation and safety behavior.