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Long-term preventive maintenance planning using finite annual budgets is vital for maintaining the service performance of airport runway composite pavements. Using the pavement condition index (PCI) to quantify composite pavement performance, this study investigated the PCI deterioration tendencies of middle runways, terminal runways, and taxiways and developed prediction models related to structural thickness and air traffic. Performance jump (PJ) and deterioration rate reduction (DRR) were used to measure maintenance benefits. Based on 112 composite pavement sections in the Long-term Pavement Performance Program, this study analyzed the influences of five typical preventive maintenance technologies on PJ, DRR, and PCI deterioration rates. The logarithmic regression relationship between PJ and PCI was obtained. For sections treated with crack sealing and crack filling, the DRR was nearly 0. For sections treated with fog seal, thin HMA overlay, and hot-mix recycled AC, the DRR was 0.2, 0.7, and 0.8, respectively. To solve the multi-objective maintenance problem, this study proposed a decision-making optimization method based on dynamic programming, and the solution algorithm was optimized, which was applied in a five-year maintenance plan. Considering different PCI deterioration tendencies of airport regions, as well as PJ, DRR, and costs of maintenance technologies, the preventive maintenance decision-making optimization method meets performance and financial requirements sufficiently. [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]

Pavement evaluation is critical for the decision-making process of pavement preservation and rehabilitation. Roughness is a key airport pavement characteristic that has been linked to impacts such as safety and service life. The Boeing Bump is one of the few roughness evaluation methods that has been developed specifically for runways. Although it is superior to the widely used International Roughness Index (IRI), it does not take into account the superposition effect of continuous runway bumps. Based on the ADAMS/Aircraft virtual prototype platform, this paper establishes and verifies five typical models (B737, B747, B757, B777, and B787) and then analyzes the most unfavorable speed (in terms of aircraft vibration) of each model and the dynamic responses caused by multiple bumps. The original Boeing Bump is improved and optimized by determining dynamic response thresholds for the various aircraft types. The results show that the revised Boeing Bump is more realistic than the original version, especially with regard to medium and long wave bands. [ABSTRACT FROM AUTHOR]

• The method monitors rigid pavement strength without interference to airfield operations. • Over two years, the slab modulus stays constant; the subgrade reaction modulus decreases. • Merging instrumentation and simulation samples creates a diverse pavement database. • Neural network models effectively correlate instrumentation data with rigid airfield pavement properties. The pavement strength evaluation plays a vital role in the airfield operation and management. Traditionally, the evaluation has relied on the Heavy Weight Deflectometer (HWD) test. This method encounters challenges, including interruptions in airfield operations, limited coverage of inspection locations, extensive time required for data collection and analysis. This paper introduces a methodology for evaluating the structural strength of airfield rigid pavement using instrumentation data, aiming to enhance both the efficiency and accuracy of such evaluations. Strain responses, collected using the instrumentation system at Chengdu Tianfu International Airport under HWD loading, are extensively analyzed. A three-dimensional numerical simulation model encompassing nine slabs was formulated, leveraging finite element analysis to replicate pavement working conditions that were not covered by the instrumentation samples. The merging of the instrumentation and simulation samples resulted in a comprehensive database for diverse pavement working conditions. This facilitated the establishment of a data-driven framework for the fusion of mechanical models. From this database, a neural network mapping model was developed to correlate the instrumentation data with the pavement slab modulus and subgrade reaction modulus. These correlations were subsequently employed for the calculation of the pavement classification number (PCN). The primary advantages of the proposed method include real-time monitoring and evaluation of airfield rigid pavement strength without interference to airfield operations. The developed method has been deployed at seven hub airports in China, encompassing Shanghai Pudong International Airport (PVG), Beijing Capital International Airport (PEK), and Chengdu Tianfu International Airport (TFU), etc. [ABSTRACT FROM AUTHOR]

Under long-term tram loads, cracking occurs on the composite pavement surrounding an embedded tram track. However, in the current pavement design of the shared right-of-way section of a tram track, the trackside pavement fatigue characteristics are not considered. The investigation of the real dynamic strain response of the trackside composite pavement under tram loads is crucial for pavement system design. In this paper, a three-dimensional finite element model of tram-track- composite pavement was established. The rationality of this model was verified by a field pavement dynamic test. The influence of the track irregularity and tram speed on the dynamic characteristics of the pavement surrounding the track was explored. The trackside pavement strain distribution along the longitudinal and vertical directions of the track had been studied. The high-frequency strain components caused by the random shortwave track irregularity excitation were found in the strain frequency domain curves in different directions, and the track irregularity amplifies the influence of the tram speed on the surrounding pavement dynamic response. The loading frequency is an important input for determining the dynamic model and fatigue characteristics of asphalt materials. At the same time, the lab test loading frequency and fatigue life corresponding to the mixture were calculated by the mechanistic coupled model and empirical damage evolution equation. It provides an analytical base for the design of pavement surrounding embedded tram tracks. • Three-dimensional FEM of tram-track-asphalt pavement was established. • The trackside pavement stress distribution had been studied. • Reason for the cracking of the trackside pavement were explained. • Indoor loading frequency of trackside pavement was higher than that of municipal pavement. [ABSTRACT FROM AUTHOR]

• Extreme value distribution better represents aircraft wheel wander. • The damage caused by each load repetition is considered individually. • The load transfer effect is taken into account using finite element analysis. • The proposed method typically produces smaller CDF values. This paper proposes an approach to determine cumulative damage factors (CDFs) based on Miner's law to improve airport runway rigid pavement design. The approach uses measured aircraft wheel wander data and constructed influence surfaces. In this study, we developed an aircraft wheel wander measuring system and measured actual aircraft wheel loading locations at the Shanghai Hongqiao International Airport (SHA). We constructed influence surfaces using finite element analysis (FEA) and multivariate interpolation. We conducted FEA of a nine-slab model to simulate load transfer effects. Based on the measured wheel wander distribution and the constructed influence surface, a cumulative damage surface construction approach for CDF determination is proposed. The preliminary benefits of the proposed cumulative damage surface method are that (1) the damage caused by each load repetition is considered individually for the CDF calculation and (2) the load transfer effect is taken into account using FEA simulations. Also, the field data we collected at SHA show that, compared to normal (uniform) distribution, extreme value distribution better represents aircraft wheel wander. A comparison between the CDFs calculated using normal distribution and extreme value distribution indicates that the maximum CDF is increased by 210% using extreme value distribution. According to the several cases analyzed in this study, the proposed cumulative damage surface method typically produces smaller CDF values than those calculated using the traditional Civil Aviation Administration of China method. Traditional airport runway rigid pavement design methods could be improved and optimized in terms of accuracy and performance using the proposed cumulative damage surface method for CDF determination. [ABSTRACT FROM AUTHOR]