Long landings can reduce runway utilization and increase the probability of runway incursions and excursions. Previous studies on long landings often lacked support from actual operational data and primarily relied on event-triggering logic established by airlines for parameter exceedance detection and retrospective analysis. In response, a comprehensive risk prediction framework for aircraft long landings, supported by Quick Access Recorder (QAR) data, was constructed. The framework includes a data analysis pipeline, a sequence prediction model, and performance evaluation metrics for accident warning efficiency. Specifically, approximately 3 million rows of real QAR data were collected, and reasonable landing intervals were extracted based on pilots’ correct landing sightlines, attention allocation, and actual visual scenarios at departure heights. Gradient Boosting Decision Trees (GBDT) were employed to develop a method for extracting landing interval feature data, based on monitored parameters and ranges of landing distance. Additionally, the GBDT-Informer long-sequence time series prediction model was developed to forecast landing distance, accompanied by the construction of effective metrics for evaluating prediction performance. The results indicate that the GBDT-Informer model effectively models the temporal dimensions of landing intervals, accurately predicting ground speed (GS), radio altitude (RALT), and landing distance sequences. Compared to other prediction models, the GBDT-Informer model consistently achieved the smallest RMSE, MAE, and MAPE values, demonstrating high prediction accuracy. This predictive framework allows for the analysis of the coupling relationships among multiple parameters in flight data and their interrelations with exceedance anomalies. The findings can be applied in actual flight landings to promptly assess whether landing distances exceed limits, providing quick references for flight crews during landing or go-around decisions, thereby enhancing operational safety margins during the landing phase.

As a key indicator of airport concrete pavement, the durability of concrete is seriously affected by the jet flow generated by aircraft engines. In order to clarify the temperature field distribution of aircraft jet flow acting on the pavement surface, this paper obtained the temperature field distribution of pavement surface under the action of three types of aircraft jet flow by combining field test experiment and CFD simulation. CFD simulation adopts the shear stress transfer model which is often used in engineering problem calculation, and the field test experiment adopts the method of embedding temperature sensor in precast concrete slab. The paired two sample t test was used to test the difference between the experimental results and the simulation results. The experimental results show that the pavement temperature increases first and then decreases with the distance from the engine, and reaches the maximum at the intersection point of the jet flow and the airport pavement. The simulation curve is in good agreement with the experimental results. When the level of confidence is 0.01, there is no significant difference between the simulation value and the experimental value, which verifies the accuracy and feasibility of the simulation calculation model. The simulation results of the three aircraft showed that when type C aircraft is in the state of full afterforce, the maximum pavement temperature is the highest which is 22m away from the nozzle, and can reach 533.81K. The current work provides a new method for measuring the temperature distribution of airfield pavement under the action of jet flow and a basis for the application of fiber concrete in airfield pavement.

The scale of modern airport continues to expand, considering the needs of future use of multiple aircraft types and models, in order to improve the relevant design and planning of taxiways in the current military airport, it is necessary to consider the structure of airport taxiways and Bridges. According to the experience of the civil airport taxi road and bridge construction, the paper analyzes the military airfield taxiing bridge structural characteristics and load characteristic, proposed for use in military airfield taxiing bridge structure form, the slide to the width of the bridge, span layout, section forms, high beam and the size, the details for the military airport taxi provides the reference for the simulation modeling and the analysis of internal force of bridge.

The layout design of an airfield area plays a crucial role in ensuring the efficiency of aircraft ground operations. In order to minimize delays caused by insufficient capacity and prevent resource wastage due to excessive capacity during the operational phase, this paper developed a prediction model for operational efficiency leveraging queuing network theory and machine-learning models. Our approach involves four key steps: (1) establish a theoretical framework for analyzing the operational efficiency of airfield area layouts based on queuing network theory, (2) employ a combination of discrete modeling and multi-agent modeling to construct a simulation model for ground operations in the airfield area, (3) develop a prediction model, known as PSO-ANN, for forecasting the operational efficiency of the airfield area using the simulation results, (4) conduct computer-based simulation experiments to assess the sensitivity of airfield area parameters, observe traffic-flow phase transitions, and investigate the factors influencing operational efficiency. This methodology enables the rapid assessment of operational efficiency for small- and medium-sized airports, as well as regional multi-airport systems. It is particularly useful for program evaluation during the strategic planning phase.

In view of the lack of soil bins for studying the surface interaction between aircraft wheels and soil, this study designed an indoor test bench for aircraft wheels and soil, including a soil container, loading vehicle, and intelligent measurement and control system, to test key parameters such as tire speed and wheel frictional resistance. The test system is capable of achieving speed regulation ranging from 0 to 30 km/h. The vertical load adjustment range with an adjustment interval of 10 kg spans from 90 to 140 kg. The soil type, compaction degree, and other conditions can be modified as per requirements to vary multiple test conditions, thereby enabling us to explore their influence on the driving resistance of the wheels. Moreover, the test data can be collected and processed in real time. A performance test of a wheel–soil table was carried out. The results show that the wheel–soil table test system is stable and reliable and can determine the relationship between the tire and soil, and the structural design of the test system meets the use requirements. In addition, it achieves the target test speed, data acquisition frequency, and stability. In terms of functionality and operational difficulty, the data acquisition of the entire test process is automated, and the test system achieves better informationization than previous methods. The overall operation of the wheel–soil platform is stable and powerful; thus, the model test platform design goal is achieved, and the testing requirements are met.

Airport agglomeration coupling coordination is a key indicator of healthy regional aviation development. This study constructs an evaluation index system from three dimensions—airport production, infrastructure construction, and network support—and assesses the coupling coordination capability of China’s four major airport agglomerations using the entropy weight method and a coupling coordination model. Furthermore, an Airport Consistency Index is innovatively introduced as the reciprocal of the coefficient of variation, and an overall coordination degree is developed under the framework of “balanced average level + consistency correction.” By incorporating the inverse coefficient of variation, the proposed index explicitly assesses airport agglomeration dispersion in coordination performance, thereby mitigating the risk that a strong performance at leading airports masks structural imbalances within the system. This refinement enhances the diagnostic precision of the overall coordination assessment by integrating both average development level and internal convergence. Based on calculations for 2020–2024, the overall coordination ranking is Beijing–Tianjin–Hebei, Guangdong–Hong Kong–Macao Greater Bay Area, Yangtze River Delta, and Chengdu–Chongqing. The Beijing–Tianjin–Hebei agglomeration shows strong and stable coordination with limited sensitivity to external conditions, whereas the Yangtze River Delta is more environmentally sensitive due to its large number of airports. The Greater Bay Area demonstrates solid coordination with substantial synergy potential, while Chengdu–Chongqing exhibits relatively weak coordination and considerable room for improvement. The proposed model effectively evaluates the overall coordination degree of airport agglomerations and supports targeted development recommendations.