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This study was undertaken to develop a new index, the Airport Runway Roughness Index (ARRI), that considers the effect of runway space when evaluating airport runway roughness. The difference between roadway and runway roughness evaluations is analysed in detail and the spatial effect is considered for a proposed runway roughness evaluation model. The evaluation of runway roughness should fully account for the dynamic responses of different typical aircraft during taxiing at the most unfavourable speed for the full cross-section of the runway. ARRI is defined according to the normalised weighted average of the vibration responses of typical aircraft models at the most unfavourable excitation waveband, the most unfavourable taxiing speed, and the most unfavourable lateral offset position within the longitudinal distance of the runway. Using runway three-dimensional full-section unevenness data collection equipment, a runway roughness evaluation procedure that is based on ARRI is proposed. The ARRI system provides qualitative improvement in terms of its representation ability, the rationality of the evaluation criteria, and the correlation of the indicators. [ABSTRACT FROM AUTHOR]

Dynamic soil stress in the ground induced by aircraft loads can lead to excessive and differential settlements. The effect of aircraft speeds on the dynamic stress is complicated due to runway roughness, lift force and wave propagation. In this paper, a virtual prototype model of B777-300 was initially developed to investigate the aircraft loads impacted by runway roughness and lift force. Subsequently, a finite element model of the runway was established for the dynamic stress in the ground subjected to moving aircraft loads. Results show that as aircraft speed increases, the aircraft-load-induced dynamic stress in the ground first increases and then decreases. The critical speed of dynamic stress accompanied with a peak stress varies with soil depth. In the shallow soil layers, dynamic stress in the ground is mainly affected by runway roughness and lift force. The critical speed of dynamic stress is close to the sensitive speed corresponding to peak aircraft loads. With the increase of soil depth and aircraft speed, the effect of dynamic amplification due to wave propagation becomes more pronounced. In the deep soil layers, the critical speed of dynamic stress approaches the Rayleigh wave velocity of soils, especially when soft soil layers exist. [ABSTRACT FROM AUTHOR]

Fast and accurate detection of airfield pavement damage is crucial to airport flight safety and airfield pavement maintenance. An efficient and lightweight detection algorithm that can be embedded into the mobile detection device has been in urgent demand. However, traditional Convolutional Neural Networks (CNNs) usually generate redundant feature maps during feature extraction or use extra operations during feature fusion to gain better performance, which greatly challenges the efficiency of the algorithm. We approached this issue by proposing an accurate and efficient detection algorithm, the YOLOv5-APD. The algorithm improves the model performance in two ways: Speeding up training and inferencing by using cheaper operations during feature extraction; Reducing the model complexity by removing redundant nodes during feature fusion. We verified the detection performance of YOLOv5-APD on a self-made dataset and compared it with the other state-of-the-art (SOTA) models. Then ablation experiments were carried out to investigate the effects of the proposed model design and the impact of image augmentation. Results showed that the proposed YOLOv5-APD model outperformed the SOTA algorithms in model performance and efficiency, which attained the optimal performance mean average precision (mAP) of 0.924. The proposed model also achieved the fastest inference speed of 142 frame-per-second (FPS), with a model footprint of 8.3 G FLOPs and 8 MB Parameters. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Airport runway roughness can be characterised by power spectral density (PSD) and two spectrum parameters, namely, the unevenness index and waviness. In order to find suitable spectrum parameters for airport runway roughness, seven runway profiles in China were measured to expand the current database of runway profiles. The statistical distributions of the spectrum parameters for runway roughness then were calculated, and the effects of the spectrum parameters on aircraft vibrations were analysed. The results show that the mode of the unevenness index is 0.13, which is a little lower than that of an A-level road, and the mode of waviness is 2.2, which is higher than the waviness recommended in ISO road classification. Moreover, as the unevenness index and the waviness increase, the aircraft vibrations become more severe. The aircraft vibration responses can be represented as a function of waviness, unevenness index, and speed, with a high correlation. At a constant speed, the relationship between the threshold values of the unevenness index and waviness can be shown as a straight line in a log plot. Finally, taking 200 km/h as the representative speed, threshold curves of the spectrum parameters, which are based on ride comfort levels and statistical analysis are proposed for the the simulation of aircraft vibration. [ABSTRACT FROM AUTHOR]

This study was undertaken to develop a new index, the Main Landing Gear Cumulative Stroke (MLGCS) index, to evaluate airport runway roughness. Using ADAMS/Aircraft software, we first developed and validated a virtual prototype model of the Boeing B737-800 aircraft and then employed the model to predict the aircraft's dynamic responses with regard to roughness. Using this prototype model and comparing it to the International Roughness Index (IRI) that is designed to evaluate roadway roughness, we established a landing gear cumulative stroke (LGCS) model to represent runway roughness. The results show that taxiing speed is an important factor using the LGCS model. According to the most unfavorable scenario, we determined the LGCS of the nose landing gear to be 100 km/h and that of the main landing gear to be 60 km/h. These results underscore that, based on correlation analysis, using the cumulative stroke of the main landing gear is more reasonable than using that of the nose landing gear to evaluate runway roughness. Furthermore, based on measured data for 37 runways, a comparison of commonly used roughness indices indicates that the MLGCS index proposed in this paper is superior to both the Boeing Bump Index and the IRI. [ABSTRACT FROM AUTHOR]