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.

In view of the fact that there is no suitable measurement method for tire driving resistance during the operation of a transporter on soil pavement, this paper proposes a simple measurement and estimation method for driving resistance on a soil runway based on the Bekker settlement model. In this paper, using the driving resistance equation based on the Bekker model, a simplified equation of driving resistance related to mass is proposed. Using the principle of kinematics, a simple test device for driving resistance was developed, and the test data of the test device were used to determine the parameters of the driving resistance estimation equation of the road surface. The driving resistance of the wheel running state was tested by the simulated aircraft load loading vehicle through the mechanical sensor, and the consistency between the theoretical value and the actual value of the driving resistance value under 8T, 10T, and 12T loads was verified. This research shows that the test method and estimation method have low cost and good accuracy and are suitable for wide promotion. The results of this paper provide a new idea and method for estimating the driving resistance of a wheel when a transporter runs on a soil road and also provide a reference for designing the length of a soil runway.

Aiming at the practical difficulties (e.g., high cost of full-scale tests) in testing the rolling resistance of aircraft wheels on unpaved runways, this study establishes a theoretical calculation formula for wheel rolling resistance based on the Bekker model, following an analysis of the development and application of wheel–soil interaction models. Global sensitivity analysis using the Sobol’ method was performed on the theoretical formula to derive a simplified calculation model. Aircraft load simulation tests under 80 kN, 100 kN, and 120 kN loading conditions were conducted using a specialized loading vehicle to determine parameters for the simplified prediction model. The resistance values obtained from this model were then applied to calculate aircraft takeoff roll distance. The accuracy of resistance estimation was verified by comparing the calculated results with takeoff distances reported in relevant literature. This research provides a novel approach for estimating wheel rolling resistance of transport aircraft on unpaved runways and offers valuable reference for determining the required length of unpaved runways.

The large-displacement shear behavior of crushable granular interfaces is critical to pile foundations in reef engineering, but the coupled evolution of strength, deformation, and particle breakage at coral sand–steel interfaces remains poorly understood, especially regarding shear rate effects and the transition from coral-sand-dominated to quartz-sand-dominated behavior. Ring shear tests were conducted on dry coral sand–steel interfaces under constant normal load (CNL) conditions, varying normal stress, relative density, shear rate, and quartz-sand content. Post-shear particle-size distributions were used to quantify breakage via Hardin’s relative breakage index. Results show that peak and residual strengths increase with normal stress, with relative density effects becoming more evident at higher normal stress. Dilatancy is suppressed as normal stress increases. Peak strength depends non-monotonically on shear rate, peaking near 6°/min, while residual strength is much less rate-sensitive. Increasing quartz-sand content reduces strength and breakage and promotes dilation. Transition ranges at approximately 30% and 85–90% quartz-sand content indicate a shift from coral-sand-dominated to quartz-sand-dominated behavior. Persistent contraction at large displacements, attributed to particle breakage and fines infilling, may cause additional settlement. These findings clarify the coupled roles of breakage and composition in interface behavior.

Runway excursion (RE) risk during aircraft landing emerges from the coupling of technical, environmental, human, information-related, and organizational factors, and is characterized by the coexistence of deterministic deviations and uncertain intermediate states. This study proposes an uncertainty-aware RE risk assessment framework by integrating the SHELLO model with Set Pair Analysis. A six-dimensional indicator system covering Software, Hardware, Environment, Liveware, Liveware–Organization, and Organization was established based on accident reports, regulatory and operational documents, 94 valid pilot questionnaires, and expert consultation. Indicator weights were determined through structured expert judgment, and grading thresholds were established by integrating industry standards, operational evidence, expert judgment, and simulation-supported information. A discrepancy-refined ternary SPA formulation was employed to characterize identity, discrepancy, and contrary relationships and to further resolve the discrepancy component into identity-side, residual, and contrary-side components. The principal value of connection degree and set pair potential were then used to characterize the separation of the evaluated state from the neutral position and the relative dominance between identity and contrary components. A case study of an A320-landing RE scenario indicated that the evaluated system fell within the Grade II intermediate diagnostic region. Hardware and Environment characterized the background risk conditions, whereas Software-related information deviations and Liveware–Organization coordination deficiencies showed the most prominent negative diagnostic contributions. Four additional anonymized A320-landing incident cases exhibited distinct dimensional risk profiles. Discrepancy refinement changed three-dimensional classifications relative to conventional SPA, illustrating its effect on boundary-sensitive risk diagnosis.

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.