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 filte
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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.