In order to explore the evolution mechanism and the best joint strategy for improvement of construction safety climate, with the help of the Mobius ring structure, a three-factor structure model of construction safety climate based on cognition-behavior-environment was constructed from three dimensions of construction workers' cognition, behavior and environment. According to the classification standard of the three-dimensional spatial structure model of construction safety climate, the grading standard of safety climate was divided. The DBN was used to study the changes of the construction safety climate with time. The results show that in terms of influencing factors, safety incentives have the greatest impact on enterprise safety climate and its evolution. In terms of dimensions, the behavioral dimension has the greatest impact. The best joint strategy to improve the construction safety climate is to strengthen the control of safety incentives, safety supervision, workers' safety response, safety consciousness and workers' learning and communication in turn.

In order to explore the effect of cognitive load on workers' hazard identification behavior，a cognitive experiment based on eye-tracking technology for construction site hazard identification was designed firstly. In this experiment，a N-digit task was introduced to increase the cognitive load. Secondly，the gaze and glance data were collected to analyze the static attention allocation，and the scanning path was processed to extract the dynamic transfer characteristics of attention. Finally，three parameters of variance review probability (RP)，transition probability (TP) and switching probability (SP) were selected as the quantitative parameter values to classify the scanning patterns of hazard identification，which explored the influence of cognitive load on hidden hazard identification from the perspective of visual behavior performance. The results show that the level of cognitive load negatively affects hazard identification performance. The subjects with high cognitive load show longer first fixation time，fewer fixation counts and saccade counts，and there is no significant difference in fixation percentage and mean fixation duration. Additionally，based on the attention characteristics，three scanning patterns are identified: sequential inspection，repeated comparison and random discovery. With the improvement of cognitive load level，subjects will pay more attention to identifying single hazard but neglect others during sequential inspection，and reduce the attention in the hazard area but still keep the fixation point quickly and frequently switching during repeated comparison，while the number and time of inspection of hazard areas are reducing simultaneously during random discovery.

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 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 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.

In order to achieve a more comprehensive and effective investment in coal mine safety，aiming at the influence of psychological and behavioral factors of decision makers on safety input decisions，a regret theory-CBDT safety input decision model was established. By analyzing the applicability of CBDT in coal mine safety input，safety input decision-making index system was established from two aspects of safety input and safety output，based on improved DS evidence theory and the evaluation of experts，and the weights of each indicator were calculated. In the CBDT perspective，based on regret theory，the comprehensive perceived utility of each scheme was calculated，and the optimal safety investment scheme was selected according to the comprehensive perceived utility. The result shows that in the process of safety input，policymakers pay more attention to safety facilities and safety technology input，and policymakers will try to avoid safety input schemes with unsatisfactory safety output. The model is based on historical safety investment cases and combines the subjective psychology of decision-makers，it helps select the optimal safety input scheme，makes safety investment decision-making more objective and scientific.

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.

To improve construction workers' safety behavior and enhance the effectiveness of both implicit and explicit safety attitudes on this behavior，this study investigated the interaction between these two types of attitudes and their combined influence on safety behavior. First，an experiment was designed to measure the implicit safety attitudes of construction workers，and Implicit Association Test(IAT) was used to evaluate underlying attitudes. Then，the relationship between implicit and explicit safety attitudes was analyzed based on an explicit safety attitude scale. Finally，the study examined how the three components of both implicit and explicit safety attitudes—cognitive，emotional，and behavioral tendency—affected safety behavior. The results show that construction workers generally exhibit positive implicit safety attitude. However，the correlation between implicit and explicit safety attitudes is weak. Explicit safety attitude，particularly the overall，emotional，and behavioral components，has a significant positive effect on safety behavior，while the correlation between implicit attitude and safety behavior remains weak. When implicit and explicit safety attitudes are aligned，their correlation with and explanatory power for safety behavior increases.

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.

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.