Although displacement measurement is essential for many civil infrastructure applications, the precise estimation of structural displacement remains a challenge. In this study, a structural displacement estimation technique was developed by fusing asynchronous acceleration and computer vision measurements using a Kalman filter. First, the scale factor, which converts translation from vision measurements (in pixel units) into displacement (in length units), is automatically computed using a natural target (i.e., without any artificial target or any prior knowledge of the target size). Second, an improved feature matching algorithm was developed to better trace the natural target within the computer vision. Third, an adaptive multirate Kalman filter was formulated such that asynchronous computer vision and acceleration measurements with different sampling rates could be seamlessly combined to improve displacement estimation. The feasibility and effectiveness of the proposed displacement estimation technique were validated by performing shaking table, four‐story building model, and steel box girder pedestrian bridge tests. In all tests, the proposed technique was able to accurately estimate displacements with root mean square errors of less than 3 mm.

For large-span bridge monitoring, displacement measurement is essential.However, it remains challenging to accurately estimate bridge displacement. When displacement is calculated by the double integration of acceleration, a low-frequency drift appears in the estimated displacement. Displacement can also be estimated from strains based on the Euler-Bernoulli beam theory.However, prior knowledge of the mode shapes and the neutral axis location of the target bridge are required for strain-displacement transformation. In this study, we propose a finite impulse response filter-based displacement estimation technique by fusing strain and acceleration measurements. First, the relationship between displacement and strain is established, and the parameter associated with this strain-displacement transformation is estimated from strain and acceleration measurements using a recursive least squares algorithm. Next, the low-frequency displacement estimated from the strain measurements and the high-frequency displacement obtained from an acceleration measurement are combined for high-fidelity displacement estimation. The feasibility of the proposed technique was examined via a series of numerical simulations, a lab-scale experiment, and a field test. The uniqueness of this study lies in the fact that the displacement and the unknown parameter in strain-displacement transformation are estimated simultaneously and the accuracy of displacement estimation is improved in comparison with those of previous data fusion techniques.

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