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 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.

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.

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 explore the approach for dealing with roof fall by the open TBM in coal mine excavation，the mechanism and pattern recognition of roof fall were investigated considering the unfavorable geological conditions such as abundant water，faults，joints and sandstone with fractured structure. Firstly，the roof fall mechanism was analyzed by utilizing the modified excavation compensation theory and the minimum support stress of surrounding rock which fully considered the intermediate principal stress. Based on the successful case of the main inclined shaft of Kekegai mine and TBM site construction data，the characteristics of roof fall were deeply analyzed. Then，in accordance with the collected on-site feedback monitoring information，the variations of excavation parameters before and after the roof fall were systematically examined，and the machine learning models of random forest (RF)，back propagation (BP) neural network，and Library for support vector machines (LIBSVM) were constructed to effectively identify the roof fall. The results demonstrate that the internal cause of roof fall is the deterioration of sandstone mechanical properties resulting from water-rock interlace in the cataclastic structure，the external cause is the energy release by mechanic-rock action，and the controlling cause is the excavation stress compensation and the timely application of steel anchor (cable) shotcrete + steel arch (steel plate belt). The sharp increase of penetration，and thrust of the hob，the torque of the cutter head and sharp decline of the cutter head speed are the characteristics of roof fall driving parameters. The RF model has the highest prediction accuracy for the classification of roof fall of surrounding rock，and its accuracy rate of identifying roof fall risk is 1.78% and 11.84% higher than that of BP and LIBSVM，respectively.

To eliminate the impact of complexity and uncertainty of safety risks in mountainous scenic areas on operational safety，a risk assessment method for mountainous scenic areas was proposed. Firstly，risk factors in mountainous scenic areas were identified to develop a risk assessment index system including personnel，equipment and facilities，environment，and management. Then，FBN and AHP models were proposed to evaluate risk probabilities and losses. Moreover，an improved ALARP criterion was used to analyze the comprehensive safety risk of mountainous scenic areas. Finally，the performance and effectiveness of the risk assessment method were validated against safety risk assessment in mountainous scenic areas in Beijing. The results indicated that the BN-based risk assessment method for mountainous scenic areas effectively addressed the issue of complex risk factors and interdependent relationships between each level. The combination of BN and triangular fuzzy number can make full use of expert experience and avoid the subjectivity of expert opinions to a certain extent. The key risk factors in mountainous scenic areas were inadequate detection of dangerous amusement facilities，insufficient configuration or arrangement of forest fire prevention facilities，inadequate protective fencing for hazardous amusement projects，and rockfalls and landslides.

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.

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 address the classification and quantitative evaluation issues of unsafe behaviors of civil aviation pilots，the management mode of their unsafe behaviors was optimized. A method for managing unsafe behaviors in civil aviation was proposed focusing on the civil aviation flight field. Firstly，based on unsafe behavior classification theory，intervention and improvement methods for errors and violations were systematically analyzed to distinguish between errors and violations. Secondly，expert interviews and questionnaire surveys were used to propose a classification evaluation index system for pilots' unsafe behaviors. Furthermore，a classification method of unsafe behavior based on quick access recorder (QAR) data and a quantification method of unsafe behavior scores based on flight operations quality assurance (FOQA) were proposed to achieve a classified quantitative assessment of pilots' unsafe behaviors. Finally，the classifying and managing unsafe behavior process was analyzed and validated. The results indicated that the 68 FOQA monitoring events obtained by screening and calculation had different causal behavioral tendencies. The two proposed unsafe behavior classifications and quantitative evaluation methods can be combined with QAR data or FOQA records. Moreover，the classification management of unsafe behaviors of civil aviation pilots can be achieved by combining intervention and improvement measures for errors and violations.