Thomas Rigoni’s scientific contributions

The health monitoring of railway networks offers a means to ensuring high-quality service, avoiding safety risks, and optimally planning maintenance actions to minimize life-cycle costs. Monitoring of the substructure is particularly linked to the tracking of degradation, which often stems from water infiltration, causing moisture accumulation in the underlying ballast layers. Such deterioration incurs substantial expenses in the order of several million per annum, while reducing the useful life of the track infrastructure. In optimizing management, it is important to improve detection schemes via affordable and non-invasive procedures. Such a solution is found in use of Ground Penetrating Radar (GPR) technology, which employs non-invasive radar pulses to map the subsurface, and possibly detect water infiltration. Train-based GPR systems thus offer tremendous potential for the development of preventive and automated monitoring of railway network infrastructure. Nevertheless, despite previous efforts in this direction, the technology remains relatively under-explored and lacks comprehensive studies to establish its suitability for this task. Moreover, there is no consensus on a standardized procedure for automatic inference of reliable indicators of railway health from GPR observations. In this work, we report on an extensive experimental analysis conducted on a controlled railway track, built by the Swiss Federal Railways, for this campaign. The humidity condition of the railway track was artificially altered to reach different levels of water content and GPR measurements were gathered under the varying conditions, with ground truth assessed through lab tests on collected samples. GPR data with complete ground truth labels deliver a rare benchmark, which can enhance understanding of this technology. Our findings show that GPR systems can effectively detect moisture infiltration in railway tracks, although several challenges are to be addressed for the development of accurate, automated procedures.