Understanding aircraft taxiing behavior, including type, speed, and lateral deviation, is essential for optimizing runway design and enhancing airport management. This study introduces a novel monitoring system deploying three laser rangefinders, coupled with computational models and machine learning algorithms, to evaluate aircraft taxiing behavior automatically. Field tests at Chengdu Tianfu International Airport demonstrated the system’s effectiveness. Identifying aircraft types based on landing gear wheel span can be challenging due to overlapping measurements between certain classes. To address this, a classification model based on convolutional neural network model is developed and validated by onboard radar data, which achieved an 80% of accuracy rate in aircraft type identification. Analysis of taxiing speeds during take-off and landing revealed significant variations influenced by runway direction, with longer acceleration or deceleration distances leading to broader speed distributions. Lateral deviation analysis indicated a positive skew in wheel track distributions, suggesting a tendency for aircraft to drift towards one side of the runway. The proposed techniques provide a simple and reliable way for aircraft taxiing behavior monitoring, and the findings offer valuable insights for improving runway design and airport safety.

Pavement roughness is a critical airport pavement characteristic that has been linked to impacts such as safety and service life. A properly defined roughness evaluation method would reduce airport operational risk, prolong the life of aircraft landing gear, and optimize the decision-making process for pavement preservation, which together positively contribute to overall airport sustainability. In this study, we optimized the parameters of the International Roughness Index (IRI) model to resolve the current poor correlation between the IRI and aircraft vibration responses in order to adapt and extend the IRI’s use for airport runway roughness evaluation. We developed and validated a virtual prototype model based on ADAMS/Aircraft software for the Boeing 737–800 and then employed the model to predict the aircraft’s dynamic responses to runway pavement roughness. By developing a frequency response function for the standard 1/4 vehicle model, we obtained frequency response distribution curves for the IRI. Based on runway roughness data, we used fast Fourier transform to implement the frequency response distribution of the aircraft. We then utilized Particle Swarm Optimization to determine more appropriate IRI model parameters rather than modifying the model itself. Our case study results indicate that the correlation coefficient for the optimized IRI model and aircraft vibration response shows a qualitative leap from that of the original IRI model.

Runway roughness is one of the most critical performance factors for runway evaluation, which directly impacts airport operation safety and pavement preservation cost. Properly evaluated runway roughness could optimize the decision-making process for runway preservation and therefore reduce the life cycle cost of the runway pavement asset. In this paper, the excitation effect of runway roughness is analyzed using a coupled aircraft/runway system. The coupled system is composed of a two degrees-of-freedom (2-DOF) aircraft model and a typical asphalt runway structure model established under runway roughness random excitation in this work. The dynamic differential equations for the coupled system are derived based on D’Alembert’s principle. The system’s vibration responses are determined via the pseudo excitation method and three response laws, i.e., the center of gravity acceleration (CGA), the dynamic load coefficient (DLC) of the landing gear, and the runway structural displacement, which are investigated under different modes. The results show that the first-order mode of the runway structure, vertical deformation, is the most significant of the four modes. Moreover, uneven excitation has a significant effect on the distribution of the aircraft’s vibration response. Compared with a single aircraft system, the developed coupled aircraft/runway system has different dynamic responses, and the degree of difference depends on the taxiing speed. The coupled effect on the CGA increases significantly with an increase in speed, with up to a 7.3% percentage difference. The coupled effect on the DLC first increases and then decreases as the aircraft speed increases, reaching a maximum of about 6% percentage difference at 120 km/h.

Understanding the vibration response patterns of aircraft taxiing under runway roughness excitation is crucial for aircraft design and runway performance evaluation. In this paper, we establish pavement roughness models for both asphalt and concrete surfaces, taking into account their unique structural characteristics. We construct a six-degrees-of-freedom aircraft model using multi-rigid-body system dynamics theory and employ the pseudo-excitation method to examine the influence of pavement roughness types on the steady vibration response of aircraft taxiing at constant speeds. Furthermore, we analyze the non-stationary vibration response patterns of aircraft during takeoff and landing taxiing using the method of instantaneous frequency response function with space frequency. Lastly, we explore the effect of stochastic structural parameters on aircraft vibration response using the Monte Carlo method. Our findings reveal that the roughness power spectrum differs between asphalt and concrete pavements, and the established roughness models in this paper demonstrate a strong fit (R2 > 0.95). The type of pavement roughness has a relatively minor impact on the power spectral density distribution of the aircraft vibration response, suggesting that the same roughness model can be used for both asphalt and concrete pavements when high accuracy is not required. The power spectral density distribution of aircraft vibration response varies across different motion attitudes, with the vibration response during landing being significantly larger than that during takeoff. Among the aircraft structural parameters, the randomness of the sprung mass has the most substantial effect on the aircraft vibration response, potentially causing the variation coefficient of dynamic load on the front landing gear to exceed 0.11. Tire stiffness comes next, which can lead to the variation coefficient of dynamic load on the main landing gear reaching 0.07. The results have a guiding role in optimizing aircraft structure design and ensuring pavement performance.