Search Results (44)

Runway friction is a critical factor for aircraft operational safety, yet the role of adhesion in wet friction remains insufficiently understood, especially in areas where tyre rubber contaminates the surface. This study evaluated approximate adhesive contribution for representative common runway surfaces, using contact angle measurements and British pendulum tester friction tests. The results show that approximate adhesion influence varies strongly with surface type: negligible on cement concrete, 16% to 19% on rubber-contaminated asphalt, and up to 49% on roughened rubber. A linear correlation between friction and contact angle confirmed that wetting behaviour governs adhesion-driven friction. Friction tests at different temperatures also confirmed the adhesive nature of the temperature influence on friction. The analysis further indicates that material properties and greater effective surface area correlate with stronger adhesive contributions, explaining material-specific differences in friction performance. These findings may provide a conceptual basis for interpreting variability in continuous friction measurements and suggest the importance of considering adhesion effects in runway surface characterisation and maintenance systems. Full article

Runway microtexture is a key parameter governing pavement friction. In recent years, several microtexture assessment methods have been developed; however, understanding of microtexture evolution under operational conditions, as well as the effects of maintenance techniques, remains limited. In this study, a runway at an Australian airport was investigated using laser profilometry. Measurements were conducted across multiple transverse sections, including aircraft touchdown and mid-runway zones. Microtexture deterioration rates were evaluated based on the estimated number of tire–pavement contacts, and aggregate polishing was assessed at different locations. Measurements were also performed after rubber contamination removal and rejuvenation treatments. The results indicate that approximately 25% of total microtexture reduction can be attributed to surface polishing, with a lower contribution in touchdown zones due to the protective effect of rubber deposits. A non-linear degradation trend was observed in touchdown zones, where approximately 1100 tire contacts reduced average microtexture roughness from 18 μm to 11 μm. Rubber removal effectively restored microtexture close to its original levels across the runway width. A rejuvenation treatment with a covering of fine sand initially improved microtexture; however, rapid deterioration occurred due to loss of the sand coating. These findings improve the understanding of microtexture evolution under operational runway conditions, albeit only at a case study level, and support more effective runway maintenance planning and intervention strategies. Full article

With the rapid expansion of airport construction projects in China, high-fill airports are frequently built under complex geological conditions, where the high risk of surface stability may significantly affect flight safety and operational costs. In this study, 17 high-fill airports and 11 non-high-fill airports across China, all characterized by high subsidence risks, were selected to investigate vertical ground deformation. Utilizing multi-temporal Sentinel-1A radar imagery spanning from 2017 to 2024, Small Baseline Subset InSAR (SBAS-InSAR) was employed to retrieve the annual average deformation velocities and time-series cumulative displacements. The results revealed that among the selected sites, only 25% were relatively stable, while the others exhibited significant deformation characteristics. Notably, high-fill airports demonstrated greater deformation magnitudes compared to those in plain areas, especially in the area of prevalent slope subsidence. In addition, significantly positive correlation was found between fill height and deformation magnitude, while differential settlement was widespread in runway zones. Furthermore, foundations involving special ground conditions manifested continuedly and distinct deformation patterns despite ground treatments. This study demonstrates the limitations of current engineering approaches in completely eliminating airport deformation, and offers valuable insights for the site selection, engineering design, and maintenance of high-fill airports. Full article

This paper presents an end-to-end framework for automated inspection and condition assessment of airport runway pavement using UAV-acquired imagery. The proposed approach integrates Unmanned Aerial Vehicle (UAV)-based data collection, deep learning-based pixel-level semantic segmentation of surface defects, and Geographic Information System (GIS)-based spatial aggregation to generate a georeferenced digital representation of airfield pavement condition. Multiple safety-critical defect types are detected and localized at pixel resolution, while spatially referenced processing enables a Pavement Condition Index (PCI)-inspired condition assessment based on defect density within predefined sampling units. The framework is validated through a real-world case study at Zadar Airport, where the entire runway was surveyed using high-resolution UAV imagery. The results demonstrate the system’s capability to identify and map multiple defect categories across the full runway extent and to produce a coherent, runway-scale condition map supporting maintenance prioritization and decision-making. Overall, the proposed solution provides a scalable, data-driven alternative to traditional manual runway inspection workflows and establishes a practical foundation for digital condition monitoring of airport pavement infrastructure. Full article

Runway friction is a critical factor in aircraft safety, affecting braking performance during landing and take-off. This study evaluates friction measurement variability and runway life-cycle dynamics at four typical Australian airports, using GripTester data from calibration strips and operational runways. The results show that friction measurements are influenced by seasonal effects, random errors, and testing equipment tire wear, with greater variability at lower speed (65 km/h) than at higher speed (95 km/h). Analysis of runway friction decay indicates that friction reduction rates are higher in touchdown zones and decelerating rate gradually decrease as friction declines, while regular rubber removal significantly restores friction, sometimes exceeding post-construction levels. Current internationally recommended friction testing intervals may not adequately ensure safety, with a sufficient probability of friction dropping below maintenance planning levels between tests. Based on observed reduction rates, updated intervals of approximately 3000 to 4000 landings are proposed to achieve 90% confidence in maintaining safe friction levels. The findings provide practical guidance for friction management and maintenance scheduling as part of an optimized airport pavement management system. Full article

The causal analysis of historical aviation accidents documented in investigation reports is important for the design, manufacture, operation, and maintenance of aircraft. However, given that most accident data are unstructured or semi-structured, identifying and extracting causal information remain labor intensive and inefficient. This gap is further deepened by tasks, such as system identification from component information, that require extensive domain-specific knowledge. In addition, there is a consequential demand for causation pattern analysis across multiple accidents and the extraction of critical causation chains. To bridge those gaps, this study proposes an aviation accident causation and relation analysis framework that integrates prompt engineering with a retrieval-augmented generation approach. A total of 343 real-world accident reports from the NTSB were analyzed to extract causation factors and their interrelations. An innovative causation classification schema was also developed to cluster the extracted causations. The clustering accuracy for the four main causation categories—Human, Aircraft, Environment, and Organization—reached 0.958, 0.865, 0.979, and 0.903, respectively. Based on the clustering results, a causation knowledge graph for aviation accidents was constructed, and by designing a set of safety evaluation indicators, “pilot—decision error” and “landing gear system malfunction” are identified as high-risk causations. For each high-risk causation, critical combinations of causation chains are identified and “Aircraft operator—policy or procedural deficiency/pilot—procedural violation/Runway contamination → pilot—decision error → pilot procedural violation/32 landing gear/57 wings” was identified as the critical causation combinations for “pilot—decision error”. Finally, safety recommendations for organizations and personnel were proposed based on the analysis results, which offer practical guidance for aviation risk prevention and mitigation. The proposed approach demonstrates the potential of combining AI techniques with domain knowledge to achieve scalable, data-driven causation analysis and strengthen proactive safety decision-making in aviation. Full article

This study evaluates the mechanical performance and sustainability potential of fiber-reinforced concrete incorporating mine tailings as the fine aggregate and waste tire wire as the reinforcing fiber. The concrete mixtures contained 0.2%, 0.4%, and 0.6% waste tire wire with the natural fine aggregate replaced entirely with Pb-Zn-Cu tailings. The mixtures were tested for porosity, water absorption, compressive strength, splitting tensile strength, flexural strength, toughness, fracture energy, and ductility to assess their mechanical performance and durability. The mine tailings improved the microstructure and reduced water absorption, particularly with tire wire. Using waste tire wire improved the compressive, tensile, and flexural performance; in particular, W-6 showed a 18.2% rise in compressive strength and a more than twofold increase in flexural strength relative to the control mix. The flexural toughness and fracture energy rose by up to 161%, while the ductility peaked at a fiber content of 0.2%. These gains were attributed to fiber crack-bridging and post-cracking energy absorption. The dual-waste system also reduced porosity, improved durability, and demonstrated strong potential for rigid pavement applications such as highways, industrial yards, and airport runways that require high fatigue resistance and a long service life. Beyond technical performance, this approach offers a sustainable solution that lowers maintenance, reduces life-cycle costs, and aligns with circular economy principles. Full article

Airport operations significantly contribute to air pollution in their vicinity through various sources, including aircraft activities—particularly taxiing and take-off—as well as ground support equipment, service vehicles, and maintenance work. Since emissions from aircraft engines represent the primary pollution source at airports, it is essential to reduce emissions at every phase of the LTO (landing and take-off) cycle to improve local air quality and promote environmental sustainability. Given the research gap in emission analysis, a comprehensive LCA framework for airport pushback and taxi operations is proposed, integrating tow truck propulsion, a taxiing strategy, and fleet management. Given the complexity of the issue, the authors first decided to investigate emissions from taxiing operations using tow trucks with different powertrains. The analyses performed were considered preliminary and a starting point for exploring emissions during taxiing operations at airports. Typically, aircraft are pushed back from the apron and then taxi under their own power using both engines at approximately 7% of maximum thrust. To substantially reduce exhaust emissions, external towing vehicles can be employed to move aircrafts from the apron to the runway. This study evaluates the potential for emission reductions in CO₂ and other harmful compounds such as CO, HC, NO_x, and PM by using electric towing vehicles (ETVs). It also compares emissions from different taxiing methods: full-engine taxiing, single-engine taxiing, ETV-assisted taxiing, and taxiing using diesel and petrol-powered tow vehicles. The analysis was conducted for Warsaw and Poznań airports. Three aircraft types—the most commonly operating at these airports—were selected to assess emissions under various taxiing scenarios. The results show that using electric towing vehicles can reduce CO and NO_x emissions to nearly zero compared to other methods. Interestingly, CO emissions from full-engine taxiing were lower than those from petrol-powered towing, although the Embraer 195 showed the highest CO emissions among the selected aircrafts. HC emissions were lowest for the A321neo and also relatively low for the diesel towing vehicle. The use of electric tow trucks significantly reduces CO₂ emissions: only 2.8–4.4 kg compared to 380–450 kg when taxiing with engines. This research highlights the critical role of sustainable ground operations in reducing harmful emissions and underscores the importance of integrating sustainability into airport taxiing practices. Full article

Foreign Object Debris (FOD) on airport pavements poses a serious threat to aviation safety, making accurate detection and interpretable scene understanding crucial for operational risk management. This paper presents an integrated multi-modal framework that combines an enhanced YOLOv7-X detector, a cascaded YOLO-SAM segmentation module, and a structured prompt engineering mechanism to generate detailed semantic descriptions of detected FOD. Detection performance is improved through the integration of Coordinate Attention, Spatial–Depth Conversion (SPD-Conv), and a Gaussian Similarity IoU (GSIoU) loss, leading to a 3.9% gain in mAP@0.5 for small objects with only a 1.7% increase in inference latency. The YOLO-SAM cascade leverages high-quality masks to guide structured prompt generation, which incorporates spatial encoding, material attributes, and operational risk cues, resulting in a substantial improvement in description accuracy from 76.0% to 91.3%. Extensive experiments on a dataset of 12,000 real airport images demonstrate competitive detection and segmentation performance compared to recent CNN- and transformer-based baselines while achieving robust semantic generalization in challenging scenarios, such as complete darkness, low-light, high-glare nighttime conditions, and rainy weather. A runtime breakdown shows that the enhanced YOLOv7-X requires 40.2 ms per image, SAM segmentation takes 142.5 ms, structured prompt construction adds 23.5 ms, and BLIP-2 description generation requires 178.6 ms, resulting in an end-to-end latency of 384.8 ms per image. Although this does not meet strict real-time video requirements, it is suitable for semi-real-time or edge-assisted asynchronous deployment, where detection robustness and semantic interpretability are prioritized over ultra-low latency. The proposed framework offers a practical, deployable solution for airport FOD monitoring, combining high-precision detection with context-aware description generation to support intelligent runway inspection and maintenance decision-making. Full article

Currently, numerous conventional airport runways suffer from cracking distresses and cannot meet their structural and functional requirements. To address the urgent demand for rapid and durable maintenance of airport runways, this study investigates the material optimization and curing behavior of cold-mix epoxy asphalt (CEA) for non-disruptive overlays. Eight commercial CEAs were examined through tensile and overlay tests to evaluate their strength, toughness, and reflective cracking resistance. Two high-performing formulations (CEA 1 and CEA 8) were selected for further curing characterization using differential scanning calorimetry (DSC) tests, and the non-isothermal curing kinetics were analyzed with different contents of Component C. The results reveal that CEA 1 and CEA 8 were selected as promising formulations with superior toughness and reflective cracking resistance across a wide temperature range. DSC-based curing kinetic analysis shows that the curing reactions follow an autocatalytic mechanism, and activation energy decreases with conversion, confirming a self-accelerating process of CEA. The addition of Component C effectively modified the curing behavior, and CEA 8 with 30% Component C reduced curing time by 60%, enabling traffic reopening within half a day. The curing times were accurately predicted for each type of CEA using curing kinetic models based on autocatalytic and iso-conversional approaches. These findings will provide theoretical and practical guidance for high-performance airport runway overlays, supporting rapid repair, extended service life, and environmental sustainability. Full article

The accurate assessment of the structural condition of airfield pavements is of paramount importance to airport authorities as it determines the planning of maintenance activities. On this basis, Non-Destructive Testing (NDT) techniques provide a powerful tool to assess the mechanical properties of the individual layers of the pavement. However, information from laboratory testing of cores taken from the pavement is expected to provide a more accurate assessment of material properties. Against this background, the present research aims to investigate the accuracy of the mechanical properties of in-situ layers derived from NDT data and the associated back-calculation procedures for airfield pavements, where higher pavement thicknesses are usually required due to the high aircraft loads, while few similar studies have been conducted compared to road pavements. For this reason, the assessment of the structural condition of a flexible runway pavement is presented. The analysis shows that there is a strong correlation between the moduli estimated in the laboratory and the moduli estimated by back-calculation. Furthermore, the back-calculated moduli appear to lead to a conservative approach in assessing the structural condition of the pavement. This conservatism promotes a more proactive pavement management by airport authorities. Full article

The inspection of PAPI (precision approach path indicator), ALS (approach light system), and runway lights is the basis of maintenance actions that safeguard the safety of airport operations. These actions consist of measurements requiring the accurate positioning of reference viewing points. Commonly, GNSS-based methods are employed and augmentation procedures are applied to improve the positioning precision. Augmentation involves the use of additional sources of information such as reference stations and complicates the process of position calculations. An image processing-based method is proposed for triangulating the positions of reference viewing points during inspections of airfield lights using a UAV. The Structure-from-Motion (SfM) photogrammetric range imaging technique and airport geodesic data are the basis of the method. The measurement of PAPI transition angles is presented as an application example. The results are compared to GNSS-based measurements and prove the viability of the method. Full article

Runway surface friction is critically important to safe aircraft operations and mostly depends on the surface texture, which provides grip in the presence of contamination and directly affects the friction coefficient in general. Microtexture assessment is the most challenging part of texture assessment since there is no standardised pavement microtexture control method in runway maintenance and management practice. The purpose of this study was to develop a simple laser profilometer and analysis model and subsequent validation for use in runway friction surveys. To that end, a simple laser profilometer was developed, and a profile picture analysis and macrotexture filtration method were designed. Test results were compared to the stylus-based roughness tester and the British Pendulum Tester. The proposed profile picture analysis and profile smothering and filtration methodology, based on linear approximation, is simpler and more effective for the case of macrotexture filtration for the friction survey. The laser profilometer model results were highly correlated with the stylus-based roughness tester results (R² = 0.99). The average roughness of the microtexture profile, after smothering and macrotexture filtration, also showed good correlation with the British Pendulum results (R² = 0.78). The results from this study confirm the possibility of texture assessment for routine runway friction surveys using a simple and economical laser profilometer, which is not routinely available in current airport surface friction management. Full article

The “bare runway” principle aims to ensure passenger and employee safety by making runways more usable during winter conditions, allowing for easier removal of contaminants like snow and ice. Maintaining runway operations in winter is essential, but it involves considerable cost and environmental impacts. Greater knowledge about the de-icing and anti-icing performance of runway de-icing products (RDPs) optimizes operations. The ice melting test, as per the AS6170 standard, gauges the rate at which an RDP dissolves an ice mass to determine RDP effectiveness. Here, we introduce a novel integrated methodology for assessing RDP-related ice melting. We combine laboratory-based procedures with infrared thermography and Raman spectroscopy to monitor the condition of RDPs placed on ice. The plateau of maximum efficiency, marked by the most significant Raman peak intensity, corresponds to the peak minimum temperature, indicating optimal RDP performance. Beyond this point, RDP efficacy declines, and the system temperature, including melted contaminants and RDP, approaches ambient temperature. Effective RDP performance persists when the ambient temperature exceeds the mixture’s freezing point; otherwise, a freezing risk remains. The initial phases of RDP–ice contact involve exothermic reactions that generate brine, followed by heat exchange with surrounding ice to encourage melting. The final phase is complete ice melt, leaving only brine with reduced heat exchange on the surface. By quantifying these thermal and chemical changes, we gain a deeper understanding of RDP-related ice melting, and a more robust assessment can be provided to airports using RDPs. Full article

Aircraft failures can result in the leakage of fuel, hydraulic oil, or other lubricants onto the runway during landing or taxiing. Damage to fuel tanks or oil lines during hard landings or accidents can also contribute to these spills. Further, improper maintenance or operational errors may leave oil traces on the runway before take-off or after landing. Identifying oil spills in airport runway videos is crucial to flight safety and accident investigation. Advanced image processing techniques can overcome the limitations of conventional RGB-based detection, which struggles to differentiate between oil spills and sewage due to similar coloration; given that oil and sewage have distinct spectral absorption patterns, precise detection can be performed based on multispectral images. In this study, we developed a method for spectrally enhancing RGB images of oil spills on airport runways to generate HSI images, facilitating oil spill detection in conventional RGB imagery. To this end, we employed the MST++ spectral reconstruction network model to effectively reconstruct RGB images into multispectral images, yielding improved accuracy in oil detection compared with other models. Additionally, we utilized the Fast R-CNN oil spill detection model, resulting in a 5% increase in Intersection over Union (IOU) for HSI images. Moreover, compared with RGB images, this approach significantly enhanced detection accuracy and completeness by 25.3% and 26.5%, respectively. These findings clearly demonstrate the superior precision and accuracy of HSI images based on spectral reconstruction in oil spill detection compared with traditional RGB images. With the spectral reconstruction technique, we can effectively make use of the spectral information inherent in oil spills, thereby enhancing detection accuracy. Future research could delve deeper into optimization techniques and conduct extensive validation in real airport environments. In conclusion, this spectral reconstruction-based technique for detecting oil spills on airport runways offers a novel and efficient approach that upholds both efficacy and accuracy. Its wide-scale implementation in airport operations holds great potential for improving aviation safety and environmental protection. Full article