Railway Bridge Condition Monitoring Using Numerically Calculated Responses from Batches of Trains

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-beta 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.

Automated summary

This study introduces a novel method to determine the apparent profile of the track and detect the condition of railway bridges using sensors on in-service trains. The method involves using data from a batch of trains to find the dynamic properties and speed of the vehicles, and then calculating the moving reference deflection influence line based on the apparent profile to evaluate the bridge's condition.