Multi-modal Remote Sensing and in-situ Sensors Integration for Advanced Airport Asset Monitoring

Runways and taxiways constitute a vital component of an airport hub. They are engineered to endure for decades; however, there are reports indicating that they may deteriorate prior to the completion of their designated lifespan, as seen in cases where 30-year designs fail within the first decade. Such deterioration includes premature distress indicators such as rutting, mud pumping, concrete slab sinking, and reflective cracking, often linked to specific material factors such as 100% Ordinary Portland Cement (OPC) design mixes. These issues can be influenced by factors such as subgrade condition, drainage quality, seasonal moisture variations, and geotechnical stability. Furthermore, factors such as ambient temperature and construction workmanship can significantly reduce the design life; if the pavement is not cured properly or poured correctly, it becomes highly susceptible to early-stage failure.

Present monitoring practices are predominantly reliant on limited in-situ testing and visual surveys, which often fail to capture the rate of deterioration at a network-wide scale or the root cause of early pavement failure. The advancement of remote sensing technologies, including Synthetic Aperture Radar (SAR) time-series, provides a reliable instrument for the precise monitoring of millimetric-scale deformations across extensive airport areas [1]. However, a comprehensive linkage between SAR observations, pavement failure mechanics, surface distress evolution, and long-term asset management decision making is still at an early stage.

In this study, we propose a prototype multi-sensor framework designed for the early detection and characterisation of airport pavement failures. This framework integrates satellite InSAR technology for long-term and seasonal deformation time-series analysis, high-resolution optical imagery for surface distress mapping, and incorporates detailed in-situ pavement investigations. The methodology examines the correlation between seasonality of SAR displacement and ambient temperature, weather, or traffic records to separate consolidation, settlements, and thermoelastic responses. The framework evaluates the utility of these data streams to identify trend profiles that may help characterise the speed of deterioration in "difficult" sections, such as those experiencing mud pumping or sinking bays [2]. The distress metrics and their temporal evolution are extracted from co-registered optical products to track the progression of visible surface damage. In-situ observations serve to validate and provide structural and materials ground truth [3]. The combination of multi-source data facilitates deformation features relating to environmental drivers. These data sources are essential to better understand the pavement states and different scenarios of changes over time. This approach supports asset owners in moving toward data-driven pavement management and optimal budget allocation.

 

 

Keywords: InSAR Time-Series Analysis; Airport Asset Management; Pavement Deterioration Modelling; Multi-modal Remote Sensing; Infrastructure Resilience

 

References

[1] Ferretti, A., Prati, C., & Rocca, F. (2001). Permanent scatterers in SAR interferometry. IEEE Transactions on Geoscience and Remote Sensing, 39(1), 8–20.

[2] Gagliardi, V.; Bianchini Ciampoli, L.; Trevisani, S.; D’Amico, F.; Alani, A.M.; Benedetto, A.; Tosti, F. Testing Sentinel-1 SAR Interferometry Data for Airport Runway Monitoring: A Geostatistical Analysis. Sensors 2021, 21, 5769.

[3] Asadollahkhan Vali, A. (2022). Airport Pavement Management System: Assessing current condition and estimating remaining life from aircraft demand. Spectrum Research Repository.