Improved long-span bridge modeling using data-driven identification of vehicle-induced vibrations
Journal article, Peer reviewed
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OriginalversjonStructural Control and Health Monitoring. 2020, 27 (9), e2574. https://doi.org/10.1002/stc.2574
The paper introduces a procedure to automatically identify key vehicle characteristics from vibrations data collected on a suspension bridge. The primary goal is to apply a model of the dynamic displacement response of a long‐span suspension bridge to traffic loading, suitable for automatic identification of the vehicle passage over the bridge. The second goal is to improve the estimation of the structural damping of the bridge deck by utilizing the free‐decay displacement response induced by the passing vehicles. The vehicles responsible for a significant bridge vertical response are first identified using an outlier analysis and a clustering algorithm. Utilizing a moving mass model, the equivalent mass and speed of each vehicle, as well as its arrival time, are assessed in a least‐squares sense. The computed vertical displacement response shows a remarkably good agreement with the full‐scale data in terms of peak values and root‐mean‐square values of the displacement histories. The data acquired on the Lysefjord Bridge (Norway) indicate that the contribution of heavy traffic loading to the combined effects of wind and traffic excitation may be significant even at mean wind speeds above 10 m s−1. The critical damping ratios of the most significant vibrational modes of the Lysefjord Bridge are studied for low wind velocities, using the time‐decomposition technique and the traffic‐induced free‐decay response of the bridge deck. The structural damping ratios estimated this way are found to be more accurate than those obtained with an automated covariance‐driven stochastic subspace identification algorithm applied to the same dataset.