To ensure the safe，high-quality，efficient，and environmentally friendly construction of future infrastructure projects，it is crucial to investigate the prevalent issues and characteristics of intelligent safety management and control practices in infrastructure projects. Firstly，the evolutionary process of intelligent safety management and control was comprehensively reviewed. It includes four distinct stages: intelligent identification and control of risk sources，data-driven identification and rectification of safety hazards，enhancement of intelligent safety management and control system，and comprehensive integration of intelligent management and control. On this basis，the successful experiences of different stages were summarized. Secondly，the challenges encountered in intelligent safety management and control were analyzed from the perspective of overall collaborative management，and its characteristics were analyzed from the perspective of "people，objects，environment，and management". Subsequently，starting from the three dimensions of safety management process，the whole life cycle of engineering construction，and the development level，the thoughts on intelligent management and control for infrastructure engineering safety were proposed，focusing on "reverence for safety，intelligent safety，inherent safety，digital intelligence，digital capability，and digital value". Finally，the future development path of intelligent safety management and control was envisioned. The results show that the safety management and control of infrastructure projects are transitioning from experience-based to data-driven approaches，while rapidly integrating new technologies，equipment，and processes. Intelligent safety management and control represents a significant extension of intelligent construction and management in infrastructure engineering. This is mainly evidenced by the transformation of safety management concepts，methods，and systems，as well as the value extraction from data assets. In the future，the intelligent management and control for infrastructure engineering safety will develop to comprehensive data fusion，knowledge-driven，virtual and real integration，human-machine collaboration，and full life-cycle control. At the same time，the importance of data privacy protection and standardised governance will become increasingly prominent.

In order to solve the problems of difficulty in quantifying the risk of airport runway incursion events，poor timeliness and low accuracy，and to enhance the capability of predicting runway incursion risks，a DBN model incorporating reinforcement learning for risk prediction was constructed. Firstly，causal inference theory was combined with grey relational analysis to analyze historical runway incursion events and identify the underlying risk factors. Secondly，Bayesian network(BN) theory was applied to explore the correlations among these factors and quantify these correlations using the Pearson linear correlation coefficient. This process helped in constructing a causation correlations network that effectively represented the propagation of risks associated with runway incursions. Then，the triangular fuzzy method and Hidden Markov Models (HMMs) were utilized to further refine and optimize the DBN parameter learning mechanism. Finally，the model's accuracy was validated using historical data. The results demonstrate that the proposed model's predictions of runway incursion risks closely align with the statistical values of historical data，achieving an accuracy rate of 84%，which represents a significant 10% improvement over Bayesian network predictions. Additionally，the use of mutual information to identify key nodes is found to effectively improve accuracy and discrimination compared to the degree value evaluation method.

In order to clarify the research progress of intelligent risk management in coal mines，the research status of data-driven coal mine safety risk management models was comprehensively analyzed. The prediction methods and analysis models for coal mine safety risk assessment were also reviewed. Firstly，the intelligent risk management was defined，and the scope of analysis was determined by searching relevant literature. Then，the research status，existing problems and development trend of accident big data were reviewed from three aspects: data-driven analysis method，coal mine safety risk assessment model and coal mine big data prediction and early warning platform. The results show that the theory and application framework of data-driven risk analysis in the field of coal mine safety has been basically formed，but it still cannot meet the needs of risk assessment and emergency management. In the application of early warning platform，a unified and general basic framework of big data analysis platform for coal mine safety production has been formed，but its application and promotion in production practice are far from enough. In the future，it is necessary to construct the comprehensive risk assessment model to study the risk of coal mining，starting from improving data quality and integrating dynamic and static multi-source data. Besides，the application of data-driven analysis in production practice should also be strengthened. These works can promote the transformation of coal mine safety risk management mode from empiricism to data-driven，and realize the informatization and intelligence of coal mine safety risk management.

In order to accurately identify unsafe behaviors of personnel in complex industrial sites and reduce the occurrence of safety accidents，an improved YOLOv5 unsafe behavior detection model was proposed. Firstly，an attention mechanism was introduced in the backbone of YOLOv5 to enhance the sensitivity of convolutional networks to unsafe behavior features. Secondly，enriching the number of training samples through image geometric transformation and pixel-level processing enhanced the generalization ability of the detection model in different industrial environments. Then，the detection model was distilled，and the network structure parameters were optimized to accelerate the training of the mode. Finally，the model was trained and iterated 200 times to simulate three types of industrial sites: lifting slings，robot-automated production lines，and operating rooms. It detected whether personnel were wearing safety helmets，work clothes and working in safe areas，and determined the level of danger based on their behavior to ascertain whether they were working safely. The results show that the model can detect 12 types of unsafe behaviors of personnel in complex industrial environments，such as dim light，lighting，and occlusion. The accuracy on the unsafe behavior test set is 98.6%，the recall rate is 99.2%，and the average accuracy is 97.58%.

Frequent heavy rainfall events cause a dramatic increase in attached elevator scaffolding accidents. In order to improve construction safety and reduce the accident rate under heavy rainfall scenarios，an accident causation analysis model based on combination of IFRAM and BN was proposed. The model first qualitatively identified accident mechanisms and explored system functional resonance using IFRAM. Next，IFRAM was mapped to a BN quantitative analysis model，and the prior probabilities of each root node were computed using cloud optimization. Finally，taking the Xi'an "9.10" accident as an example，empirical research was conducted，and corresponding preventive measures were proposed. The results indicate that accidents are most likely to occur when the safety status is IV. The core causes of climbing accidents include workers violating regulations，failure to conduct mandatory supervision such as standing by and heavy rainfall. The combination of factors such as heavy rainfall environment and overloading of the frame after rain is the key to inducing frame climbing accidents.

In order to investigate the influence of internal and external factors on the safety state of the tailings dam，a method for analysing the safety state of the tailings dam based on heterogeneous hierarchical diagrams was proposed. Firstly，a hierarchical causal graph was constructed based on a priori knowledge to link key factors such as environment，seepage field and stress field with the safety status of tailing dams，and an evaluation index system was established by combining the attribute characteristics of heterogeneous nodes. Secondly，the cloud model and set-pair analysis theory were used to quantitatively calculate the potential logical relationship between the heterogeneous causal graph and the safety stability of the tailings dam. A dynamic interval calculation method for quantitative indicators and a safety status level calculation model were proposed to convert the fuzziness and uncertainty of complex and diverse evaluation indicators into quantitative expressions. Finally，a tailings dam in Luoyang was used as an example to verify the scientificity of the model.The results show that the model can quantitatively analyse the link between factors and states and identify the causes of negative changes in stacked dams. The conclusions of the model analysis can be used for the safety management of the dam-building process.

In order to improve the perception level of fault water damage in coal mine，a fault water inrush stage sensing method based on fault activation evolution mechanism and key control factors was proposed. The evolution characteristics of working face floor and fault failure were studied through similar simulation tests of fault water inrush evolution process. The stage characteristics of monitoring parameters and the change of water inflow were revealed by taking the stress in the failure zone of floor，the stress in the fracture zone of fault and the water pressure in the water channel as the stage monitoring parameters. The key controlling factors of water inrush phase transformation were determined by grey correlation analysis method. Then，according to the fault water inrush analysis and research process，the fault water inrush stage perception method was proposed. The study determines that the numerical variation characteristics of monitoring parameters such as stress of floor failure zone，fault fracture zone and water pressure in water channel show obvious stage characteristics during fault activation water inrush. The order of grey correlation degree between control factors and water inflow is as follows: stress in floor failure zone > stress in fault fracture zone > water pressure in water channel，through identification of key control factors. Through the identification of key control factors，the perception of fault water inrush stage can be realized，and the perception method based on grey correlation analysis is feasible in principle.

In order to mitigate the impact of traffic accidents on the operation efficiency of freeways，and improve the throughput capacity of the accident area，based on real-time information via vehicle-to-vehicle and vehicle-to-road，a cooperative lane change guidance strategy in freeway traffic accident areas was proposed with the safety potential field theory. Firstly，in view of various traffic accidents in different lanes in two-lane or multi-lane traffic of one-way，the guidance area of collaborative lane change was divided into four areas: accident protection area，guidance transition area，collaborative lane change guidance area and free lane change area. The guidance threshold of lane change was determined to update vehicle status. Furthermore，the safety potential field of traffic accidents was established，and the corresponding guidance strategies of vehicle cooperative lane change were proposed according to different scenarios. The calculation methods of the safe distance for vehicle lane change and the latest lane change position were given. Finally，based on simulation of urban mobility (SUMO) software，the simulation results were verified in various scenarios. The results show that in the two-lane of one-way scenario，the optimization effect is most obvious when the vehicle cooperative guidance rate is at 75%. In the multi-lane of one-way scenario，the optimization effect is most obvious when the guidance rate is at 50%. Meanwhile，through comparative analysis，it is found that the average speed of vehicles passing through the accident section is increased by up to 6.3% and the maximum vehicle delay is reduced by 14.6% after adopting the lane change guidance strategy.

In order to monitor the health state of construction formwork support systems and prevent the risk of safety accidents caused by formwork collapses，a new intelligent monitoring method combining EMI and CNN for joints of formwork support systems was proposed. Firstly，based on the electromechanical coupling and sensing-driving characteristics of PZT，PZT-joint coupling model was built based on the electromechanical impedance sensing mechanism. Secondly，the original conductivity of PZT patch，coupled with the monitored structure，was used as a monitoring signature for identifying joint looseness based on the EMI technique. Thirdly，EMI-CNN model was built with the 801 original conductance signals of PZT over the sensitive frequency range as the inputs，and the nine degrees of joint looseness as the outputs. In total，the dataset consisted of 189 samples，162 for training and 27 for testing. At last，taking an actual formwork support system joint from building site as an example，EMI-CNN model was verified and compared with EMI-BP model by the experiment. The research results show that EMI-CNN model reached convergence after 85 iterations. The prediction accuracy of the EMI-CNN model reached 100%，which is 29.63% better than EMI-BP model. This proposed method is distinguished by its real-time，accurate and non-destructive monitoring capabilities，providing an effective solution for health monitoring of joints in construction formwork support systems.

In order to solve the problem of false alarms and missed alarms in pipeline gas leakage detection using a single sensor，timely warning and feedback of leakage status，a multi-source data fusion pipeline leakage detection method based on cross-attention was proposed. Firstly，the pre-trained ShuffleNetV2 model was used to extract spatial features from thermal imaging data. Then，a 1DCNN BiGRU model was constructed by combining a one-dimensional CNN (1DCNN) and a bidirectional gated recurrent unit (BiGRU) to extract temporal features from gas sensors. Finally，cross-attention was used to capture the spatiotemporal correlation of the data and obtain the feature representations of the two data sources. The residual method was used to connect the features and input them into the classification layer to obtain the recognition results. The results show that the constructed SCGA model has a gas recognition accuracy of 99.22%，and the loss value fluctuates between 0-0.04. Compared with support vector machines (SVM)，1DCNN，and BiGRU models that only use gas sensor data，the accuracy is improved by at least 4.12%. Compared with MobileNetV3，ShuffleNetV2，and ResNet18 models that only use thermal image sensor data，the accuracy is improved by at least 1.14%. Compared with the multi-source data fusion model SCG，which simply connects temporal and spatial features，the accuracy is improved by 1%. It was verified that the SCGA model has high accuracy.