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.

In order to explore the factors influencing accident injuries in SRBE，a binary LR model was constructed based on data from 1 411 accidents from a Chinese shipbuilding group. OR were used to quantify the impact of factors such as enterprise，time，location，personnel，environment，and accident types to accident injuries. The result identifies 11 significant factors influencing accident injuries. Non-contract workers face significantly higher risks of injury compared to contract workers. Non-approved hazardous operations pose 3.246 times the risk of accident injuries compared to approved hazardous operations. Male perpetrators present a significantly higher risk of causing accident injuries than female perpetrators. The education level of the workers is predominantly at junior high school，and higher education levels are associated with a lower risk of causing accident injuries compared to those with junior high school education. Accident injuries exhibit seasonal characteristics，with accidents occurring frequently in the second quarter，while relatively fewer and less risky in the first quarter. Peak working hours and workdays significantly increase injury risks compared to off-peak hours and non-working days. Object strikes are the primary type of accidents，and mechanical injuries significantly increase the risk of accident injuries. Ships and workshops are the most likely locations for accident injuries within the shipyards. Longer years of service reduce the risk of accident injuries，while higher temperatures increase it.

In order to ensure the normal passage of vehicles and the safety of the existing tunnel support structure during the blasting through the highway，the evaluation method of engineering blasting effect based on extension-AHP model was proposed. Firstly，by means of investigation and analysis，the blasting effect rating standard and index system were established，and the model was applied to the evaluation of a water diversion project. Secondly，AHP was used to determine the weights of evaluation indexes，and the combined relevance degree of blasting rating was calculated. Finally，the results of the blasting effect rating were verified by acoustic detection test，blasting shock wave test and blasting seismic wave test. The study shows that the combined relevance degree {{{{{Q_j}_{(}}_X}_{-}}_M}_{)} is calculated by extension-AHP model. The blasting effect of the tunnel boring is Q_max=-0.017，and the evaluation grade is a good blasting effect. The surrounding rock loose circle of the tunnel is relatively small and evenly distributed. The influence range of the surrounding rock stability is about 0.5-0.6 m. The blasting energy does not cause the rock rupture zone to further extend the signs of the inward. The energy attenuation trend of blasting seismic waves is different under different wave frequencies. However，the attenuation rate is greater than that of low-frequency component energy in the overall performance of high-frequency component energy. In the same channel，with the increase of the distance between the blasting source and the measurement points，the overall vibration waveform becomes narrower. The main frequency increases first and then decreases，and the main frequency domain moves to the low-frequency direction. The overpressure peak attenuation characteristic of blasting shock wave meets P_S=αl^-γ. With the increase in the distance from the blasting source，blasting shock wave overpressure attenuation coefficient is an increasing trend. The measurement range belongs to the shock wave attenuation zone. The shock wave overpressure peak of the tunnel entrance and the construction outside tend to converge.

To address issues of correlation prediction indicators，outliers，and data imbalance in original data in rockburst prediction，a rockburst prediction method based on LLE-DBSCAN-SMOTE for data processing was proposed. Firstly，the maximum tangential stress of surrounding rock {\sigma }_{\mathrm{\theta }}，uniaxial compressive strength of rock {\sigma }_{\mathrm{c}}，uniaxial tensile strength of rock {\sigma }_{\mathrm{t}}，elastic strain energy index W_{\mathrm{e}\mathrm{t}}，brittle coefficient {\sigma }_{\mathrm{c}} / {\sigma }_{\mathrm{t}}，stress coefficient {\sigma }_{\mathrm{\theta }} / {\sigma }_{\mathrm{c}}，and stress concentration value β characterizing the stress gradient of surrounding rock were selected to construct a rockburst prediction indicator system. Secondly，the LLE algorithm was used for data dimensionality reduction to eliminate the cross-correlation effect between indicators，and the DBSCAN algorithm was introduced to remove outliers. Then，the SMOTE technology was introduced for data balancing. Finally，three types of rockburst prediction models were proposed using Decision Tree (DT)，Random Forest (RF)，and Gradient Boosting Decision Tree (GBDT) algorithms. The prediction accuracy of the data training models before and after processing was compared and analyzed. Moreover，engineering verification was performed through the measurement in the diversion tunnel of Jiangbian Hydropower Station. The results show that the prediction accuracy of the three types of algorithm models which reduce the prediction index from the 7 dimensions of the original data to the 4 dimensions and adopt the graded outlier processing is the highest among the similar models. The rockburst prediction of the Jiangbian Hydropower Station demonstrates that the proposed model significantly improves prediction accuracy compared to similar models using original rockburst data.

To reveal the influence of asymmetric load on gas seepage and extraction radius，a multi-physics coal and gas fluid-solid coupling model was proposed to analyze the gas seepage characteristics of coal seams. Matrix-adsorbed gas was used as the mass source in the proposed model introducing asymmetric loads into the boundary conditions. Furthermore，segmented drilling was used under asymmetric load conditions to optimize the gas extraction radius and improve extraction efficiency. The results indicated that greater stress compressed the cracks inside the concentrated stress zone，making it more difficult for gas to flow and to be extracted more challenging. The gas pressure in the concentrated stress zone decreased by approximately 2% less than that in the original stress zone，and the permeability decreased by about 9%. Asymmetric load had different degrees of influence on the diffusion and seepage processes. Within 180 days，the mass of diffused gas of the original stress area decreased by 19% and the seepage mass decreased by 20.5%，while these values in the concentrated stress zone decreased by 16.9% and 17.9%，respectively. Asymmetric loads had adverse effects on gas extraction，increasing extraction time under uniform load conditions. By adjusting the extraction radius under asymmetric load conditions，not only can the extraction efficiency be improved by approximately 3%，but it can also ensure that the extraction standards are met within 180 days，thereby effectively improving the overall performance of gas extraction.

In order to investigate the thermal runaway characteristics of lithium-ion batteries following short-term exposure to high or low temperatures shocks during transport and usage，thermal runaway tests were conducted on fully charged lithium-ion batteries. These batteries were subjected to temperature shocks at -40 to 60 ℃ for 24，48，and 96 hours before external heating was applied. Analyses were performed on thermal runaway characteristic parameters to explore change rules in thermal runaway characteristics after short-term high- or low-temperature shocks. The results indicate that the overall severity of thermal runaway and the intensity of ejected flames were attenuated by the shock factor. With rising shock temperature，the time difference (Δt₁) between the onset of thermal runaway and the rupture of the battery safety valve shows slight fluctuations overall，while both the time difference (Δt₂) between peak temperature and the onset of thermal runaway and the initial thermal runaway temperature tends to increase. In contrast，peak thermal runaway temperature and flame heat flux decrease relatively. When the impact temperature decreases，Δt₁ gradually shortens，while Δt₂ initially increases and then decreases with lower temperatures. Both the onset and peak temperatures of thermal runaway drop significantly，with reductions of 15.2 and 175.4 ℃，respectively，observed at -40 ℃，along with a reduction in flame heat flux. Additionally，with extended shock durations，Δt₁，initial and peak temperatures of thermal runaway，and flame heat flux all decrease.

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 improve the recognition accuracy and detection speed of traffic participants by intelligent networked vehicles and traffic monitoring systems so that they can timely respond to the safety hazards in the mixed traffic environment in urban space，a mixed traffic participant detection model in urban space based on the improved YOLOv8n algorithm was proposed. Firstly，geometric transformation and pixel transformation enhancement strategies were employed in the data input stage to prevent overfitting and improve robustness，and generalization. Secondly，the SPD-Conv module was used to replace all original convolution layers of the YOLOv8n algorithm，which enhances the feature extraction capability for low-resolution small targets. Meanwhile，the CA module was added to the fusion structure of the neck network of the YOLOv8n algorithm to improve the recognition accuracy of key information with almost no additional computational overhead. Then，the boundary box loss function EIoU was used to replace the original loss function，enabling the model to achieve superior convergence speed and recognition stability. Finally，the ablation and comparison experiments were carried out with the public and self-built integrated traffic participant dataset，and the real-time detection experiment was carried out with the automatic driving experiment platform. The experimental results show that compared to the YOLOv8n model，the improved SEC-YOLO model has increased mAP and FPS by 3.2% and 7.9% respectively. The SEC-YOLO model outperforms mainstream models in terms of mAP and FPS as well. The average accuracy of real-scene detection on the automatic driving experimental platform is around 95%. The SEC-YOLO algorithm model achieves higher detection accuracy for urban traffic participants，with stronger robustness and real-time performance.

The construction speed and number of vehicles passing through multi-portal underpass tunnels are increasing，and special driving environments such as long downhill slopes at the portals and underground merging areas cause frequent traffic accidents. To deeply analyze the variation characteristics of lateral offset in tunnel special sections，a test vehicle equipped with inertial navigation and Mobileye was used to perform a real-car natural driving test in a typical multi-entry underpass tunnel. Based on the tunnel's alignment and spatial variation characteristics，the test section was divided into the external tunnel section，the downhill section，the internal section，the underground merging section，and the internal tunnel section. The results indicated that the vehicle trajectories in the other four sections were more complex than the external section of the tunnel，and the lateral offset increased significantly，reaching up to 1.888 to 2.184 times the external section of the tunnel. The lane offset variation rate of the underground merging section and the inner section of the entrance was the smallest. The land discreteness of the underground merging section in the tunnel was the largest，and the discreteness of the entrance downhill section was the smallest with a standard deviation of only 0.111. The average width of the predicted interval of the entrance downhill and underground merging sections were 0.2 m and 0.24 m，respectively. Therefore，the driving safety hazards of the entrance downhill and underground merging sections were higher than other sections.

In order to seek the best way of laying median strip facilities，four typical forms of median strips in China were established through driving simulation，and trajectory and speed-related data were extracted. Based on the horizontal right of way in the road spatial right of way，the road utilisation rate was selected as an indicator to analyse the difference characteristics between the actual right of way and the nominal right of way. The results show that the overall range of fluctuations in driver trajectories at different height facilities is significantly different. Among the rightward offsets triggered by the shy away effect，the trajectory offset reaches the maximum in the reboundable traffic cylinders scenario R₅，followed by the traffic separate railings R₇ and raised pavement markers R₂，which are larger than that of the double yellow line scenario R₁ without the facility. While adding facilities triggers a shy away effect in drivers，a certain degree of lateral offset can improve road utilisation rate，up to 11.15%，which was found in R₅. At the same time，the installation of the facility inhibits the speed of drivers，achieving a two-way improvement in traffic safety and traffic design. Finally，the width of the median strip to satisfy the maximum nominal right-of-way utilisation was calculated to be 0.844 m and the facility's height to be 138.62 cm.