Target-free vision-based approach for modal identification of a simply-supported bridge

Abstract

Accurate and efficient vibration response measurement is one of the key tasks in structural health monitoring (SHM) of civil engineering structures. Normally, dynamic responses such as displacement responses, are acquired by physical sensors and data acquisition systems installed on the structures. However, the installation of sensors may be time-consuming and costly. In recent decades, computer vision-based methods have been introduced into civil engineering to provide alternatives for traditional displacement measurements. However, the camera specifications, such as limitations on field of view (FOV) and relatively low resolution of economic lens, have prevented the vision-based method from being widely used for vibration displacement measurement on relatively large-scale structures. This paper proposes a target-free vision-based approach for dynamic displacement measurement and modal identification of simply-supported bridges using a single camera. In this study, a series of videos are captured in separate experimental tests using a consumer-class GoPro on a long-span beam subjected to a moving load. The radial distortion of videos caused by the wide-angle lens is offset by distortion adaption, and the scale factor is fitted using a static video shooting experiment. The tracking process consists of three basic steps, namely, region of interest (ROI) selection, natural feature extraction and features tracking. The results are compared with those measured by linear variable differential transducer (LVDT), demonstrating a high accuracy by the proposed approach. Vibration characteristics, including natural frequencies and mode shapes of the bridge, are identified from the measured displacement. The overall mode shapes are obtained by combining the mode shapes identified from different segmental videos of the bridge obtained by roving the single camera along the bridge for measurements. Comparisons of the natural frequencies and mode shapes between the results from the proposed approach and LVDTs demonstrate the effectiveness and accuracy of the proposed approach for displacement measurement and modal identification of long bridges under moving loads.