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Recent improvements in remote sensing technologies have shown that techniques such as photogrammetry and laser scanning can resolve geometric details at the millimeter scale. This is significant because it has expanded the range of structural health monitoring scenarios where these techniques can be used. In this work, we explore how 3D geometric measurements extracted from photogrammetric point clouds can be used to evaluate the performance of a highway bridge during a static load test. Various point cloud registration and deformation tracking algorithms are explored. Included is an introduction to a novel deformation tracking algorithm that uses the interpolation technique of kriging as the basis for measuring the geometric changes. The challenging nature of 3D point cloud data means that statistical methods must be employed to adequately evaluate the deformation field of the bridge. The results demonstrate a pathway from the collection of digital photographs to a mechanical analysis with results that capture the bridge deformation within one standard deviation of the mean reported value. These results are promising given that the midspan bridge deformation for the load test is only a few millimeters. Ultimately, the approaches evaluated in this work yielded errors on the order of 1 mm or less for ground truth deflections as small as 3.5 mm. Future work for this method will investigate using these results for updating finite element models.
Graves et al. (Thu,) studied this question.