Transition zones in railway tracks are locations with considerable changes in the rail-supporting structure. Typically, they are located near engineering structures, such as bridges, culverts and tunnels. In such locations, severe differential settlements often occur due to the different material properties and structure behavior. Without timely maintenance, the differential settlement may lead to the damage of track components and loss of passenger’s comfort. To ensure the safety of railway operations and reduce the maintenance costs, it is necessary to consecutively monitor the structural health condition of the transition zones in an economical manner and detect the changes at an early stage. However, using the current in situ monitoring of transition zones is hard to achieve this goal, because most in situ techniques (e.g., track-measuring coaches) are labor-consuming and usually not frequently performed (approximately twice a year in the Netherlands). To tackle the limitations of the in situ techniques, a Satellite Synthetic Aperture Radar (InSAR) system is presented in this paper, which provides a potential solution for a consecutive structural health monitoring of transition zones with bi-/tri-weekly data update and mm-level precision. To demonstrate the feasibility of the InSAR system for monitoring transition zones, a transition zone is tested. The results show that the differential settlement in the transition zone and the settlement rate can be observed and detected by the InSAR measurements. Moreover, the InSAR results are cross-validated against measurements obtained using a measuring coach and a Digital Image Correlation (DIC) device. The results of the three measuring techniques show a good correlation, which proves the applicability of InSAR for the structural health monitoring of transition zones in railway track.

Keeping railway tracks in good operational condition is one of the most important tasks for railway owners. As a result, railway companies have to conduct track inspections periodically, which is costly and time-consuming. Due to the rapid development in computer science, many prediction models using machine learning methods have been developed. It is possible to discover the degradation pattern and develop accurate prediction models. The paper reviews the existing prediction methods for railway track degradation, including traditional methods and prediction methods based on machine learning methods, including probabilistic methods, Artificial Neural Network (ANN), Support Vector Machine (SVM), and Grey Model (GM). The advantages, shortage, and applicability of methods are discussed, and recommendations for further research are provided.

Transition zones in railway tracks are the locations with considerable changes in the vertical support structures. Due to the differential stiffness and settlement in the open track and the engineering structure resulting in the dynamic amplification of the wheel forces, track settlement is usually observed in the approaching zones. The settlement in transition zones is detrimental to the track components and passenger comfort. This paper presents the results of the experimental analysis performed in three transition zones which were in various conditions. The dynamic displacements of rails due to passing trains were measured at multiple points (dynamic profile) in the approaching zones. The device employed is a contactless mobile device for measuring displacements, which is based on the digital image correlation technique. Because the operational parameters of the digital image correlation-based devices are important for measurement accuracy, prior to the in situ measurements, this device was tested in a laboratory to study the influence of the operational parameters, including the elevation/heading angles, the focal length of the cameras, and the measuring distance. After determining the optimal operational parameters for the railway field, multiple-point measurements were performed in the transition zones. The length of the approaching zone was studied first. Also, the dynamic profiles of the embankment–bridge and bridge–embankment transitions were analysed. Finally, by comparing the multiple-point displacements in the approaching zones in different conditions, it was found that the dynamic profile of the rail displacements has a good correlation with the track condition in the transition zone. The results are presented and discussed.