To realize the efficient analysis of composite joints with novel side-plate reinforced connections, a macro model of beam-column-slab composite joint was proposed. The optimal realization method of the connection between floor and steel beam was determined. The accuracy and reliability of the macro model was verified. Furthermore, a beam-column joint frame model with traditional side-plate reinforced connections (TSP), a beam-column joint frame model with novel side-plate reinforced connections (FBSP), and a composite joint frame model with novel side-plate reinforced connections (CJ-FBSP) were established. Elastic-plastic time-history analysis was conducted on three frame models considering the joint performance. The top point lateral displacement, inter-story drift angle, plastic energy dissipation, and plastic hinge distribution of different frame models under seismic waves were obtained. The results show that the performance of TSP and FBSP joints can be well simulated according to the spring stiffness calculation method. At the same time, the connection method considering the shear slip and pull-out performance of studs can precisely simulate the mechanical performance of composite joints. The inter-story drift angle of the three joint frame models do not exceed the specification limit (1/50). The maximum inter-story drift angle of FBSP frame model is smaller than that of TSP frame model but larger than that of CJ-FBSP frame model, and the plastic energy dissipation capacity is the best. Due to the strengthening effect of the floor, the plastic hinge rate of CJ-FBSP frame model is the smallest but the plastic energy dissipation capacity is weak.

Oil and gas drilling and production wellsites are complex and have many types of potential safety hazards, in order to improve the accuracy of the identification of potential safety hazards in wellsites, an oil and gas drilling and production wellsite potential safety hazard identification method based on improved YOLOv5 was proposed. Firstly, in order to solve the problem that the background of the picture was complex and the recognition difficulty increased, the SimAM attention mechanism was introduced in the backbone network; secondly, in order to solve the problem that the scales of the types of hidden hazards were different and there were multiple scales in one picture, the original feature fusion was replaced by adaptive spatial fusion of features (ASFF). Lastly, the hidden hazard recognition effect of the improved model was validated by comparing the model with other models. The results show that the improved YOLOv5 model improves the average accuracy value of recognition by 10.4%, and has a better recognition effect on the safety hazards of oil and gas drilling and production well sites. In order to solve the limitation of video monitoring and identification of oil and gas drilling and production wellsite safety hazards, a set of intelligent wearable device was developed, which effectively improved the portability of the identification of wellsite safety hazards.

In the field of fingerprint recognition technology, ridge density, as one of the morphological features of fingerprints, has demonstrated increasing research value. Aiming at the problems of time-consuming and labor-intensive existing measurement methods, an algorithm based automated measurement method was proposed. The algorithm first preprocessed fingerprint images, including grayscale conversion, edge detection, noise reduction, and ridge enhancement, to improve image quality and clarity. Subsequently, it strengthened fingerprint features, performed array transformation, determined directional vectors, detects peaks, and finally plotted a grayscale fluctuation diagram to visually present the measurement results. Experimental results show that the automated measurement algorithm performs well in terms of efficiency and accuracy, exhibiting high consistency and significant statistical correlation with manual measurements. This further validates the scientific robustness and effectiveness of the automated measurement method, providing new perspectives and approaches for the automation and intelligence of fingerprint recognition.

In order to reduce the risk of logistics drones to the ground after failure or collision in the air, a model for logistics drones to the ground risk assessment was constructed, focusing on the risk analysis of drone failure and crash. Firstly, in the drone failure and crash risk model, the possible failure of drones in different flight stages was discussed in detail, including vertical fall and horizontal fall, as well as the casualties and economic losses that these falls may cause to ground personnel. Secondly, the collision and crash risk model was analyzed the dynamic behavior of drones after a collision in the air and its impact on ground safety, including collision momentum-energy conservation and collision consequence assessment. Finally, a comprehensive three-dimensional risk assessment matrix was constructed, combining the possibility of accidents, the number of deaths on the ground and the economic losses to evaluate the risk level in different scenarios. The case analysis shows that the number of deaths on the ground is within the order of 10^-6, and the risk level in different regions is obtained. It can be seen that this method provides important risk assessment tools and data support for logistics drone operators and relevant policy makers.

To address the high cost and low accuracy of manual inspection for steel surface defects, as well as the excessive computational resource requirements caused by complex traditional target detection models,a lightweight defect detection algorithm named YOLOv8n-MDC was proposed by integrating MobileNetv3 with YOLOv8.Firstly, based on YOLOv8n, the original intersection over union(IoU)-based bounding box loss function was replaced with weighted IoU(WIoU), enhancing model robustness through a non-monotonic focusing mechanism. Secondly, the backbone feature extraction network of YOLOv8n was substituted with MobileNetv3, utilizing its lightweight architecture to reduce network complexity and redundant computational overhead. Finally, during the feature fusion stage, depthwise separable convolution (DWConv) and C3Ghost modules replaced the original components, further minimizing model parameters and accelerating detection speed. Evaluated on the NEU-DET steel surface defect dataset, the YOLOv8n-MDC achieves an mAP of 81.3%, representing a 5% improvement over the baseline YOLOv8n, while its parameter count and computational complexity are reduced to 1.02 M and 2.1 GFLOPs (33.9% and 25.9% of the original model, respectively), meeting industrial requirements. This lightweight algorithm significantly reduces computational complexity and resource consumption while enhancing detection accuracy, offering an optimized solution for industrial steel surface defect inspection.

In order to improve the security performance of civil aviation security personnel, the relationship between security personnel's hazard perception ability and security performance under different factors was explored. Based on the fuzzy signal detection theory(FSDT), the discriminability and judgment criterion were used as indicators to measure the hazard perception ability of security personnel. A mixed experimental design of 2 (experience: novice, veteran) ×2 (time pressure: no time limit, time limit) ×2 (probability of occurrence of contraband: high, low) was employed to examine the impact of experience, time pressure, and probability of contraband on the hazard perception ability of security personnel. The results indicate that experience and time pressure significantly impact security performance, while the main effect of contraband probability on security performance is not significant. Experience has a notable effect on fuzzy discriminability and fuzzy judgment criterion, with veterans demonstrating higher overall hazard perception ability than novices. Additionally, a time limit reduces the discriminating power of security personnel under low contraband probability but improves fuzzy judgment criterion standards under high contraband probability. Attaching importance to security skills training, setting reasonable search time and conveyor speed can effectively improve security personnel's hazard perception ability and ensure aviation safety.

In construction safety inspections, visual obstructions often lead to missing features, resulting in dangerous misjudgments. To improve the efficiency of risk identification in construction, a method for occluded feature inference based on amodal completion technology was proposed, using construction fence detection as a case study. First, a dataset of fence detection images with visual obstructions was created using image synthesis techniques. Then, a combination of YOLOv8 instance segmentation and the amodal segmenter based on boundary uncertainty estimation (ASBU) feature completion network was used to infer the visual features of the occluded parts of the fence. The completed features of the occluded construction fences can be applied to various construction safety monitoring tasks, such as closed-loop detection. The approach was validated using fence images from multiple construction sites, achieving precise feature completion for occluded fences (average intersection ratio mIoU>95.5%). The research results provide a framework for feature inference in occluded construction scenes, which enhances the efficiency of intelligent construction safety supervision.

To achieve rapid automatic detection and identification of void damage in high-rise composite structures, a bridge tower full-scale model was tested for damage using Zhangjinggao Yangtze River Bridge's composite structure tower. Through numerical simulation of sound field spatial distribution, time-frequency response characteristics comparison analysis, and convolutional neural network(CNN) model training and visualization. An automatic device for void detection of high-rise composite structures and a deep learning detection method based on acoustic signals were proposed. The results demonstrate that the acoustic signal analysis method based on automatic device acquisition can be used as a new approach for automatic detection and identification of void damage in high-rise composite structures. The constructed CNN model can achieve high-precision classification of structural void state, and the recognition accuracy is 96.8%. The automatic device and intelligent detection method enable automatic real-time detection and classification of high-rise composite structures, improving automation and reducing safety risks.

To investigate the psychological load characteristics of drivers on interchange ramp curves, an naturalistic driving test was conducted with 38 participants in a high-density interchange group in Chongqing. The PhysioLAB physiological monitoring system was used to collect electrocardiogram (ECG) data from drivers navigating the ramp curves. Three typical ramps were taken as research objects, a factor analysis model was established based on heart rate (HR), heart rate increment (HRI), and heart rate variability[root mean square of the difference between adjacent normal cardiac cycles(RMSSD) and standard deviation of RR intervals for all sinus heartbeats(SDNN)] to analyze the variations in psychological load and influencing factors for drivers on different ramp curves. The results indicate that the average heart rate for drivers on various ramp scenarios is found to range from 60 to 120 beats per minute, with heart rate increments between 3% and 15%. Psychological load on right-turn ramps is more dispersed, while on left-turn and circular ramps, it is more concentrated, with higher load observed on circular ramps. Differences in psychological load are noted among drivers of different styles and genders. Left-turn ramps impact female drivers more, and right-turn ramps impact aggressive drivers more. Psychological load decreases with increased familiarity and increases with larger turning angles. More ramp lanes lead to more vehicle weaving, increasing psychological load. A negative correlation is observed between psychological load and the radius of right-turn ramps, with no linear relationship for left-turn and circular ramps. Driving on consecutive circular ramps increases psychological load.

Highway maintenance operations occupy existing road infrastructure and have a significant impact on vehicle traffic safety and efficiency. In order to analyze the traffic flow characteristics during maintenance operations on a four-lane highway when the inner lane is closed, a traffic flow modeling and simulation method based on an improved cellular automaton was proposed. According to the Highway Maintenance Safety Operation Regulations (JTG H30—2015), maintenance operation control zones were established, dividing the mainline highway facilities into six traffic scenarios: warning zone, upstream transition zone, buffer zone, work zone, downstream transition zone, and termination zone. By introducing a longitudinal safety distance model and optimizing the lateral lane-changing safety condition determination rule, the NaSch car-following model and symmetric two-lane cellular automata (STCA) lane-changing model were improved. The model was calibrated using field data, and the traffic flow operating conditions in the maintenance work zone were simulated using MATLAB. The results show that when the traffic flow density reaches 1 550 pcu/h per lane, setting the merging start point 1 000 meters downstream of the warning zone, with a speed limit of 60 km/h and an upstream transition zone length of 160 meters, the traffic flow safety and efficiency indices in the maintenance zone are optimized.