Vision-based structural displacement methods allow convenient monitoring of civil structures such as bridges, though they are often limited due to the small number of measurement points, constrained spatial resolution, and inability to identify the acting forces of the measured displacement. To increase the number of measurement points in vision-based bridge displacement measurement, this study introduces a front-view tandem marker motion capture system with side-view traffic counting to identify the force-inducing passing vehicles on the bridge’s deck. The proposed system was able to measure structural displacement at submillimeter resolution on eight measurement points at once at a distance of 40.8–64.2 m from a front-view camera. The traffic counting system with a side-view camera recorded the passing vehicles from two opposing lanes. We conducted a 35-min experiment for a 25 m-span steel road bridge with hundreds of cars passing over it and confirmed dynamic displacement distributions with amplitudes of several millimeters when large vehicles passed.

This study proposes a novel vision-based measurement method to capture small dynamic displacements at many points on a large-scale structure. The measurement points are aligned in the depth direction so that all points are observable in a single field of view with a high power zoom lens. To cope with insufficient incident light and lens blur when capturing video in a limited depth of field with large magnification, our method used highly retroreflective cubes as markers, combined with a strong coaxial lighting device for measuring image displacements with a tandem-layout in images. We conducted experiments to measure dynamic displacements of a 4 m long truss bridge model, and 18 corner cubes were attached as retroreflective markers. 752×2048 images were captured with a coaxial lighting device at 240 fps. The experimental results show that the deformation of the bridge model, its resonant frequencies, and mode shapes at a frequency of dozens of Hz can be determined by analyzing images captured from a single camera view.

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