Smartphones have become widely accessible and advanced, pocket-sized computers capable of sensing, sending, and processing an ever-increasing variety of data. Modern phones are, for example, equipped with sensors such as accelerometers and GPS-sensors. Meanwhile, an increase in rail traffic leads to an increase in the need of having a reliable and comfortable railway system, an objective which can be achieved through additional monitoring. Through mobile crowdsensing, the smartphone data from passengers could potentially provide an additional and almost continuous information source on the railway system health state.

This thesis explores to what extent smartphone accelerometer measurements on board of in-service passenger trains can be used to monitor railway track quality. The thesis contains a literature review and a survey with European infrastructure managers. They identify that repeatability, as a result of operational variance, is a key research gap and challenge in the implementation of this technology. The thesis assesses accelerometer characteristics from a set of contemporary smartphones through laboratory tests. The tests uncover limitations and heterogeneity in the sensing capabilities such as sampling frequencies, low-pass filters and eigenfrequencies of the devices. The thesis also makes use of a case study, in which different smartphones are placed in various positions on different in-service passenger trains, running over the same tracks in the Netherlands. The vertical acceleration signals are analysed and compared. The vehicle speed and the position of the smartphone within the car body are found to significantly influence both the frequency content and vertical acceleration amplitudes measured. Finally, the different signals are related back to a Track Quality Index based on the standard deviation of the Longitudinal Level D1 track parameter. A degree of consistency in identifying locations with a low relative track quality is displayed, albeit with a significant degree of variance due to the previously mentioned factors.

This thesis explores the feasibility of drive-by health monitoring (DBHM) for railway bridges using axlebox acceleration (ABA) measurements. Considering the non-stationary nature of train vibrations passing over a bridge, a signal analysis methodology is developed including filtering and time-frequency analysis using the Continuous Wavelet Transform. A vehicle-bridge interaction model combining multi-body dynamics and Euler-Bernoulli beam elements is developed to simulate the influence of local stiffness reduction damage on ABA. Results indicate that both quasi-static and dynamic components of the ABA signal are affected by local stiffness reduction, with the quasi-static influence being more pronounced and localized. A more detailed finite element model simulating a cracked beam is developed to more accurately determine the influence of the local structural damage on the quasi-static component of the ABA signal, revealing that the crack is expected to cause a transient, localized increase in the acceleration. To comprehensively assess the feasibility of DBHM, this research integrates the simulation results with field measurements. To characterize the repeatability of ABA field measurements, measurements from three different measurement campaigns are compared to one another. It is found that the simulated impact of the local damage is overshadowed by the frequency content of the ABA signals in the time-frequency domain, indicating the difficulty to achieve direct damage detection. Alternatively, taking the advantage of DBHM in frequent monitoring, it is concluded with probabilistic analysis that reference signals under healthy conditions combined with multiple passages are essential to confidently identify structural damage. Using a case study of a damaged railway bridge, this paper illustrates both the feasibility and the inherent challenges of implementing DBHM with ABA measurements. As such, this research underscores the need for further field measurements and ABA data analysis for more reliable and accurate structural health monitoring of bridges.

In this paper, various two-dimensional (2D) models of train-bridge dynamic interaction are established using multibody dynamics theory and beam theory. The models are solved using the Newmark-𝛽 method, a common implicit integration method for structural dynamics. The research gives a description of the effect of train parameters on the train-bridge dynamics using a full 2D train model. By clearly describing the train and bridge models used, an attempt will be made to accurately describe the physics behind the models. The result of the simulations describe the differences between different models, after which the effects of various train parameters on the midpoint displacements are illustrated.