In order to promote the precise governance goals of accident risks，a research method combining process-tracking based on actor network theory and case comparative analysis was adopted to study the weak prevention generation mechanism in the process of accident risk governance. Firstly，the technical environment for analyzing the evolution process of accident risk was clarified，including prepositive contexts，structural scenarios，and developmental circumstances，as well as the analysis logic based on Lens Model and dimensions of time，space，and structure，to provide a research foundation for the integrity analysis of accident risk production process. Secondly，based on the case comparative analysis of the tracking of the interaction process between different types of actors and risks，the role change picture of the core actors，homeowners and rainstorm，in the process of accident/disaster risk governance and accident/disaster generation was presented integrally. Finally，a comparative analysis and reflection of case studies based on tracking the interaction between different types of actors and risks were summarized，and the essence and generation mechanism of weak prevention in the process of accident risk governance were proposed. The results indicate that the mixed interaction between human and non-human actors can continuously activate the emergence and mutual construction process of new actors，new intersectionality，and new vulnerabilities in the system，subsequently resulting in the non-stationary evolution of accident risk production environment and risk structure，which leads to intervention failure and recurrence of weak prevention in the process of accident risk governance.

To protect wooden buildings from fire threat，the influence mechanism of aging on wood burning characteristics was discussed. Firstly，the combustion behavior of wood was systematically discussed in four aspects: pyrolysis，flame combustion，smoldering combustion and flame spread. Secondly，the combustion characteristics of naturally aged wood and artificially accelerated aged wood were compared and analyzed. Finally，the effects of aging on wood fire risk were analyzed based on the smoke generation characteristics of wood combustion and the flame-spreading behavior of wood structure buildings. The results show that the mechanical properties of wood are significantly reduced by changing the internal composition and carbonization degree of wood，thus weakening the fire resistance and affecting the smoke generation. At the same time，the changes in physical and chemical properties and structural characteristics of aged wood promote the fire spread of ancient wooden buildings in the initial stage of fire. However，the study on the mechanism of wood carbonization by aging is still insufficient，and the influence of aging mode and environmental conditions on the dynamic characteristics of fire at the later stage of combustion has not been clarified. To evaluate the impact of aging on the fire risk of ancient wooden buildings，it is essential to integrate material science and structural mechanics for effective fire safety measures.

In order to assist in the development of safety hazard management measures for hydropower project construction，the safety hazard texts accumulated during the construction inspection of hydropower projects were collected. Entities and relationships from the semi-structured safety hazard texts were extracted using Python. A knowledge graph of safety hazards was constructed and imported into the neo4j graph database for storage. A Sentence-Bidirectional Encoder Representations from Transformer (BERT) model based on bidirectional coding was built for the semantic matching of construction hazards in hydropower projects. The deep semantic features of target hazards and historical hazards were learned，and the historical safety hazards most similar to target hazards were recommended. Using the Cypher query statement，the governance measures corresponding to the historical security risk were searched. The results show that the Sentence-BERT model has an accuracy of 96.48% in identifying architecturally and historically similar safety hazards，which is significantly better than BERT，Word2vec-Deep Semantic Similarity Model (Word2vec-DSSM)，and BERT-DSSM models. Among 150 randomly selected target safety hazard data，the accuracy rate of testing historical similar safety hazard suggestions reaches 92%，and the retrieval effect of hazard management measures is demonstrated through the hazard knowledge graph，which verifies the applicability and effectiveness of the method.

To predict unsafe events for pilots in real time，a LSTM neural network was used to assess pilot maneuver stability and pilot maneuvering quality was improved by optimizing indicators. Firstly，a set of human-machine maneuvering factors presenting the pilot's maneuvering behavior characteristics was proposed by analyzing the pilot's stability maneuvering QAR data in flight. Secondly，the factors affecting the stability maneuvering of the aircraft were analyzed，and a gray correlation analysis method was used to determine the 15 characteristic parameters of associated risks from the 37 monitoring parameters closely related to the stability of the aircraft. Then，the LSTM model was used to train and test the data to predict the pilot's maneuvering stability，and indicators were proposed to evaluate safety stability quality. Finally，ML was used to rank the importance of relevant influencing factors to improve model validity. The results indicated that the time series model effectively eliminated the interference of parameters with little or no correlation with the prediction results in the original parameters. The stability model can predict risks with high accuracy and provide pilots with a 3-4 s time margin to take preventive measures and reduce unsafe incident occurrence during flight.

To solve the problems existing in the traditional NER methods in the domain of tunnel construction safety，such as fuzzy entity boundary，difficulty in small-sample learning，and insufficiently comprehensive extraction of feature information，an entity recognition method for tunnel construction accident text based on the BERT-BiLSTM-CRF model was proposed. Firstly，the BERT model was used to encode the tunnel construction accident text to obtain word vectors containing semantic features. Then，the word vectors output after the training of the BERT model were input into the BiLSTM model to further obtain the context feature of the tunnel construction accident text and conduct label probability prediction. Finally，by utilizing the constraints of the annotation rules of the CRF layer，the output result of the BiLSTM model was corrected，and the maximum probability sequence annotation result was obtained，so as to realize the intelligent classification of the labels of the tunnel construction accident texts. Comparative experiments were conducted between this model and other four commonly used traditional NER models on the tunnel construction safety accident corpus dataset. The results show that the recognition accuracy rate，recall rate and F₁ value of the BERT-BiLSTM-CRF model are 88%，89% and 88% respectively，and the entity recognition effect is better than other benchmark models. By using the established NER model to recognize the entities in the actual tunnel construction accident texts，its application effect in the domain of tunnel construction safety is verified.

In order to assess the collision risk of paired approach to closely spaced parallel runways，a collision risk model based on fuzzy Bayesian and event tree analysis was developed. Initially，the paired approach procedure was delineated，and risk factors inherent in it were identified through the Failure Mode and Effect Analysis (FMEA) method. Subsequently，a Bayesian network model addressing hazards during the paired approach and potential control failures was constructed，leveraging the identified risk factors. The probability of the root node was determined through a combination of expert survey weighting and statistical analysis of historical data. Some root node probobilities and the conditional probability of the intermediate nodes were fuzzified utilizing seven-level linguistic variables，followed by de-fuzzification using the incentre of area. Additionally，priori probabilities and sample data were input into BN software for expectation-maximization(EM)parameter learning，facilitating the determination of hazard proximity and control failure probabilities. Lastly，considering the time series relationship between hazardous approach，control failure，and collision events，the collision risk associated with paired approaches was assessed employing event tree analysis，and a sensitivity analysis was conducted on the BN. The result shows that hazardous approach sensitivity is highest to pilot operating level，while control failure sensitivity is highest to poor maintenance. If the probability of poor pilot performance surpasses 12%，and the likelihood of inadequate maintenance surpasses 0.17%，the paired approach operation fails to meet the safety target level.

Flight trajectory prediction plays a crucial role in ensuring safe and efficient air traffic operation. In order to consider the implicit correlations between flight trajectory characteristics，the encoding and decoding operations of the prediction framework in the transformer model were enhanced. Firstly，the convolutional block was improved，and ordinary convolutions were applied to capture the correlations between neighboring time series trajectory characteristics，and dilated convolutions were added to capture correlations between non-neighboring time series trajectory characteristics. Secondly，multi-head self-attention was employed to perform calculation based on the spatiotemporal features of the flight trajectory combined with the importance of attention scores. Thirdly，probabilistic sparse method was designed to reduce the computational complexity of the multi-head self-attention and improve the model's computational efficiency. Finally，an experimental platform was established to verify the flight trajectory prediction framework. The results show that compared to the traditional transformer model and the other three neural network models，the improved transformer model shows a 14.4% improvement in time performance. By using root mean square error(RMSE) and mean absolute error(MAE) as evaluation metrics，the average prediction deviations of the improved transformer model for trajectory features such as longitude，latitude，and altitude are 0.027 and 0.021，respectively. These deviations are reduced by 0.072 and 0.063 compared to the traditional transformer model's average prediction deviations of 0.099 and 0.084. Sensitivity analysis on the lengths of prediction sequences indicates that the improved transformer model is more stable than the baseline models.

This study investigated the protective effect of an active steering control strategy when VRU start from the outside or inside of a curve and cross the road with uniform acceleration，deceleration，and uniform velocity. Firstly，the spatial positional relationship models for vehicles and VRU，the safety assessment models，and the active steering safety distance models were established to propose an active steering control strategy. Then，a lateral collision avoidance controller was designed using the quintic polynomial lane-change method，the Frenet coordinate transformation method，and the model predictive control method. Finally，with electric bicycle riders as the collision avoidance targets，18 mixed conditions were constructed based on the state of the target lane status，the movement direction，and the speed of the electric bicycle to verify the collision avoidance effect of the active steering control strategy. The results indicated the active steering control strategy avoided collisions between vehicles and electric bicycles under mixed conditions. The preceding，ego，and following vehicles in the target lane can drive normally during lane-changing，and the vehicle ride comfort was satisfactory.

To provide intelligent and systematic decision support for building safety management，building fire accidents data was collected and summarized. The knowledge graph of building fire accidents was developed to construct a knowledge graph database. Based on the dimensions of time，space，theme，and important entities，the implementation process of the intelligent question-answering system was innovatively presented. Moreover，the intelligent analysis of building fire risk was performed. The results showed that daytime and summer were high-risk periods for building fires. The frequency of building fire accidents in East China was significantly higher than that in other regions，and the fire risk of building fires was higher in electrical and warehouse areas. Reinforced concrete frame structures and factory buildings were more prone to building fires. Most ignition sources were combustible solids，and the main cause of fire accidents was illegal construction behavior.

In order to prevent accidents caused by the failure of the flange sealing groove in the hydrogenation reactor，failure analysis methods such as macroscopic inspection，fracture analysis，ferrite detection，chemical composition analysis，hardness detection，metallographic detection，SEM analysis and operation process analysis were adopted. The influencing factors of damage mode，start-stop operation process，ferrite content in the weld overlay layer，material properties，and abnormal elements were studied，and the reasons for the failure of the flange sealing groove at the outlet of the hydrogenation reactor were analyzed. The results indicate that the failure of the sealing groove is mainly due to the high stress between the weld overlay layer and base metal of the sealing groove，which produces stress corrosion cracking under the action of a corrosive medium containing F，S and other elements. There is no weld overlay layer on the bottom surface of the cracked flange sealing groove，and the transgranular cracks started at the junction of the surface weld overlay layer and the non-weld overlay layer，mainly on the surface of the non-weld overlay layer side. According to the failure causes，the corresponding improvement measures are put forward from the aspects of manufacturing，material selection and maintenance.