This paper describes a novel method to determine a road profile through the analysis of accelerations in a passing vehicle. A direct integration algorithm is proposed to determine the profile from the measured vehicle acceleration response. A sprung mass model and a half-car model are used to represent the vehicles in separate analyses. Combining the direct integration algorithm with the Cross Entropy (CE) optimisation method, a vehicle fleet monitoring concept is proposed for the monitoring of roads and/or bridges. In this approach, the profile can be calculated using accelerations from multiple vehicles without prior knowledge of the vehicle properties. Numerical results show that calculated profiles are the same as the 'true' profiles which were used to generate the 'simulated measured' accelerations.

Roads and railway tracks are a major focus of interest in transport infrastructure monitoring. Settlement in a road or railway track profile changes the dynamic excitation applied to passing vehicles. This, in turn, results in a changed dynamic response in the original source of loading, such as a passing vehicle. These changes in dynamic excitation make it possible to detect damage in transport infrastructure from the vehicle response. In this paper, the profile is calculated using accelerations in a passing vehicle and used to monitor transport infrastructure.

This study introduces a novel method to determine apparent profile of the track and detect railway bridge condition using sensors on in-service trains. The concept uses a type of Inverse Newmark-β integration scheme on data from a batch of trains. In a self-calibration process, an optimization algorithm is used to find vehicle dynamic properties and speed. For bridge health monitoring, the apparent profile of the bridge is first determined, i.e., the true profile plus components of ballast and bridge deflection under the moving train. The apparent profile is used, in turn, to calculate the moving reference deflection influence line, i.e., the deflection due to a moving (static) unit load. The moving reference influence line is shown to be a good indicator of bridge stiffness. This numerical approach is assessed using an elaborate finite element model operated by an independent research group. The results show that the moving reference influence line can be found accurately and that it constitutes an effective indicator of the condition of a bridge.

This paper presents a new way to determine road profile and detect bridge damage using accelerations from a fleet of passing vehicles. Using off-bridge data, a Bayesian approach updates estimates of the road profile and vehicle properties. The profile elevations and vehicle properties are shown to be insensitive to random noise in acceleration measurements. On-bridge data, with recently updated vehicle properties, are used to estimate bridge damage. Bearing damage and local crack damage in a bridge are simulated. For bearing damage, the results show that this method can quantify the damage level of a bearing and infer other bridge properties. For local crack damage, the levels and the location of the damage are inferred from the simulated measurements.

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