KCF-Based Identification Approach for Vibration Displacement of Double-Column Bents under Various Earthquakes

Abstract

Vibration displacements are one of the most significant indicators in the health monitoring and condition assessment of bridges in the life cycle. The traditional monitoring means, such as contact sensors, have relatively high-cost and limited points for displacement measurement of bridges. This paper proposes a low-cost and non-contact monocular vision system based on the KCF algorithm to accurately and timely identify the vibration displacement of bridges. A conversion method associated with a scale ratio was established to cope with the loss of depth information in images when a monocular camera is used to monitor multiple targets in different depths of the field. A series of shaking table tests on a two-column pier with energy dissipation beams were conducted to verify the feasibility, accuracy, effectiveness, and robustness of the KCF-based identification approach. The results showed that the vibration displacements of the column identified by the monocular vision system based on the KCF algorithm are almost consistent with the measurement results obtained by the laser displacement sensors. The peak displacement discrepancies between both measurement methods are within 6% for all cases with different shaking amplitudes and earthquake waves. The RMSE of the displacement histories between both measurement methods is very low. The corresponding frequency spectra contents identified by the monocular vision system based on the KCF algorithm match well with the measurement counterparts recorded from the laser displacement sensors.