Integrative approaches in railway health monitoring: InSAR, GPR, and machine learning synergy

Railway track health monitoring and maintenance are crucial stages in railway asset management, aiming to enhance the train operation quality and service life. For this aim, various inspection means (using diverse nondestructive testing techniques) have been applied; however, these means are mostly not able to monitor whole railway track network or track underlying layers (e.g., ballast and subgrade). The use of remote sensing techniques, such as interferometric synthetic aperture radar (InSAR), can expedite the defect diagnosis process for railway tracks, elevating the scope of health monitoring to a network-wide level. The ground-penetrating radar (GPR) has emerged as a particularly reliable method, especially for detecting structural deficiencies in underlying layers. As a result, combining the two distinct nondestructive testing techniques—GPR and InSAR—presents a promising strategy for efficient railway asset management. Recognizing the significance of embracing newer and more advanced monitoring strategies, this chapter reviews the fusion of GPR and InSAR methodologies and explores the potential integration of machine learning models to develop a predictive health monitoring and condition-based maintenance approach for railway tracks.