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.

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.

In deep foundation pit engineering, the support form of “bored pile + internal support” is often used for foundation pit support, but the support effect of different support arrangements on the foundation pit is different. Relying on a foundation pit project in a fast-track reconstruction and expansion project in Changzhi City, ABAQUS was used to numerically simulate the whole process of excavation of different steel support layout schemes, and the influence of different internal support layout schemes on the settlement of the surface soil around the excavation of the foundation pit, the horizontal displacement of the supporting structure and the uplift of the bottom of the foundation pit were analyzed, and then the reliability of the model was verified by combining the field monitoring results. The results show that the numerical simulation results can accurately reflect the deformation law and characteristics of the foundation pit in the process of excavation and support. When the depth of the internal support arrangement is small, it has a good control effect on the maximum horizontal displacement of the supporting pile and the surface settlement outside the pit. When the depth of the internal support arrangement is large, the maximum uplift at the bottom of the pit has a certain control effect. Increasing the number of internal supports has different effects on reducing the maximum horizontal displacement of the supporting pile and the maximum uplift at the bottom of the pit.

The accumulated water in the cavity behind the tunnel lining in cold regions may freeze under low-temperature conditions, resulting in local frost heave pressure. Assuming that the cavity behind the tunnel lining was a semi-elliptical space, the interaction between the surrounding rock, ice body, and lining during the frost heave process was simplified as springs in series, and an analytical solution for the local frost heave pressure in the semi-elliptical water-accumulated space was proposed. A three-dimensional numerical model was developed to verify the effectiveness of the analytical solution. Further, the relationships between local water-accumulated frost heave pressure and surrounding rock grade, lining stiffness, water-accumulated depth, and frost heave level were studied, and the influences of frost heave position on the mechanical characteristics of lining structure were analyzed. The results show that the local water-accumulated frost heave pressure of the cold region tunnel is negatively correlated with the surrounding rock grade and positively correlated with the lining stiffness, water-accumulated depth, and frost heave level. The influence degree of various factors on frost heave pressure is as follows: water-accumulated depth > lining stiffness > surrounding rock grade > frost heave level. The impact of water-accumulated frost heave on the lining structure mainly occurs in the contact area between the ice body and the lining, leading to the convergence of the tunnel towards the inner side. The principal stress of the lining is maximum when the water-accumulated space is located at the inverted arch, and the principal stress of the lining is minimum when it is located at the arch foot. Under the action of local frost heave, there is a sudden change in the bending moment and axial force of the lining structure, manifested as the bending moment increase and axial force decrease of the lining under tension on the air side, as well as the bending moment decrease and axial force decrease of the lining under tension on the surrounding rock side. The stiffness differences in different zones of the lining lead to different impacts of frost heave pressure on structure safety. The influence degree of frost heave position on structure safety is as follows: vault > inverted arch > arch shoulder > arch foot > wall foot.

Gas risk assessment of tunnel construction is one of the key issues to ensure the safe construction of tunnels passing through gas sections. Aiming at the three stages of investigation, design and construction in the process of tunnel construction, the interpretative structural model was used to reveal the hierarchical key of gas risk influencing factors in the above three stages. The hierarchical model of risk assessment was established, and the weight calculation method of risk assessment index was defined. Combined with data collection, literature collation and engineering investigation, the assignment standard of risk assessment index was proposed based on membership degree theory, and the gas risk assessment method in tunnel construction process was constructed. Combined with engineering examples, the rationality of the proposed method was verified. The results show that the pregnancy risk environment in the survey stage is the precondition, and the survey disturbance is the inducing factor. The pregnancy risk environment in the design stage is the precondition, and the design factor is the inducing factor. In the construction stage, the pregnancy risk environment is the precondition, the construction disturbance is the inducing factor, and the site management is the root cause. The risk is revealed in the survey stage, the risk is reduced in the design stage, and the safety is ensured in the construction stage. The proposed method is consistent with the on-site disclosure, which verifies the rationality of the proposed method. Through the above research, it can provide a theoretical basis for the risk determination of the tunnel crossing the gas area, and provide a reference for the selection of subsequent engineering measures.

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.

The smart skin of an aircraft is realized by integrating distributed sensors, actuators, and controllers into the composite skin, thereby enabling it to monitor its own state and detect damages. The physical field inversion algorithm plays a key role in the signal processing of the smart skin. However, due to factors such as the low sensor density, traditional inversion algorithms exhibit limited accuracy. In order to enhance the monitoring precision of the smart skin, a back propagation(BP) neural network-improved grey wolf optimizer(IGWO) inversion algorithm, which combined a BP neural network with an IGWO-optimized Kriging model, was proposed. A prototype of the smart skin was subsequently fabricated, and wind tunnel tests were conducted to validate the proposed algorithm. The results demonstrate that the BP-IGWO inversion algorithm achieves higher accuracy and superior detail representation compared to traditional inversion algorithms, and can better monitor the state of smart skin.

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.

A systematic study was conducted on the issue of gate assignment, with the goal of minimizing the number of remote gate assignments and the idle time of near gates. A multi-objective mathematical model was proposed to address the multi-objective and multi-constraint characteristics of the problem. The model was designed to minimize the number of remote gate assignments and the idle time of near gates while taking into account parameters such as actual flight arrival and departure times, aircraft types, and the interrelationships among gates. The gate assignment process was optimized using the deep reinforcement learning method, specifically the deep deterministic policy gradient(DDPG) algorithm. To enhance the optimization ability and performance of the algorithm, an improved DDPG algorithm was developed by incorporating prioritized experience replay and multi-strategy exploration mechanisms. Comparative experiments were conducted, and the results show that the improved algorithm significantly reduces the number of remote gate assignments and optimized time utilization. The algorithm also achieves faster convergence and stronger global optimization capabilities, confirming its effectiveness.