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Abstract Maintaining airport runways is crucial for safety and efficiency, yet traditional monitoring relies on manual inspections, prone to time consumption and inaccuracy. This study pioneers the utilization of low‐cost dashcam imagery for the detection and geolocation of airport runway pavement distresses, employing novel deep‐learning frameworks. A significant contribution of our work is the creation of the first public dataset specifically designed for this purpose, addressing a critical gap in the field. This dataset, enriched with diverse distress types under various environmental conditions, enables the development of an automated, cost‐effective method that substantially enhances airport maintenance operations. Leveraging low‐cost dashcam technology in this unique scenario, our approach demonstrates remarkable potential in improving the efficiency and safety of airport runway inspections, offering a scalable solution for infrastructure management. Our findings underscore the benefits of integrating advanced imaging and artificial intelligence technologies, paving the way for advancements in airport maintenance practices.
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Rigid pavement icing: misting tests on a model pavement column under simulated cold fronts inside a freezer
Meteorological and subsurface factors influence pavement’s response to cold fronts. Prediction of pavement temperature, particularly icing, is important to winter pavement maintenance which relies on an estimated time for the formation of ice. However, the prediction development is limited by test data on pavement icing. A model column consisting of soil samples and a concrete pavement slab retrieved from the Dallas Fort Worth airport was used to replicate the airport pavement structure, including subgrade. The soil was classified using USCS, tested for optimum moisture content and compacted in lifts in the column. Thermistors and moisture sensors were placed at different depths. The pavement slab was fitted with temperature sensors throughout. The system was installed in a freezer box,wrapped in insulation and plastic. Three cold front scenarios were selected from observed airport weather data and simulated in the freezer box using varying rates of temperature decrease and precipitation. The formation of ice on a pavement surface was observed at 10–20 min after the start of precipitation. This time frame is not affected by the freezer box cooling rate. The icing time found from this study is useful for the development of prediction models for icing on pavements.
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- PAR ID:
- 10405998
- Date Published:
- Journal Name:
- International Journal of Pavement Engineering
- ISSN:
- 1029-8436
- Page Range / eLocation ID:
- 1 to 11
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1080/10298436.2022.2044036
