Early Detection and Assessment of Railway Substructure Moisture Problems at National to
Local Scale Using Spaceborne, Airborne and Train-based Remote Sensing Systems
(REASSESS)

To ensure a highly available and affordable infrastructure SBB needs innovative solutions in the domain of infrastructure monitoring. The use of remote sensing systems operating from satellites, drones, and mobile mapping platforms offers tremendous potential for preventive and automated monitoring of the SBB railway network infrastructure. One of the most common causes for deterioration of the railway track infrastructure is water intrusion leading to subsurface ballast moisture/wetness. This type of damage results in significant costs for mitigation and repair. REASSESS proposes a remote-sensing-based approach to efficiently and timely detect and verify hot spots affected by subsurface ballast moisture. We investigate the synergistic use of three remote sensing systems 1) spaceborne synthetic aperture radar (SAR) , 2) (airborne) laser scanning, and 3) train-based ground penetrating radar (GPR) to rapidly and efficiently detect such hot spots. The envisioned monitoring concept implements a 2-tier detection scheme, in which spaceborne radar sensors with their day-and-night and all-weather imaging capability are used for a first-level detection of problematic hot spots on the scale of the entire SBB railway network, followed by a more localized inspection using airborne laser scanning and train-based ground penetrating radar systems. A successful implementation of such a remote-sensing monitoring concept, which ensures a timely detection of railway track sections affected by subsurface ballast moisture, offers the potential to extend the lifetime of the affected infrastructure by 5 to 10 years. This can result in annual savings in the order of 35 Mio per year, as calculated by the SBB partners supporting this project.
 

Point of contact

Dr. Othmar Frey
Institute of Environmental Engineering

 

Start | duration

01.01.2021 (36 months)

 

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