Sparse accelerometer-aided computer vision technology for the accurate full-field displacement estimation of beam-type bridge structures

Computer vision-based sensors are considered effective means to monitor full-field displacements; nevertheless, several limitations prevent their applications in full-scale structures. First, the accuracy of computer vision-based sensors is affected by measured distances. Second, due to the limited frame rate of cameras, computer vision-based sensors cannot record high-frequency responses. To effectively address these issues, a novel full-field displacement estimation approach based on the data fusion of camera-based measurements and limited accelerations was proposed. Full-field pseudo-static displacements were extracted from camera-based measurements, and full-field dynamic displacements were expanded by accelerations at sparse locations based on the modal superposition method. Subsequently, high-fidelity full-field displacements were obtained by data fusion. The proposed approach was numerically and experimentally verified. Further, the full-field displacements of an experimental-scale beam model and a full-scale bridge were estimated by a consumer-grade camera and several accelerometers.