Variational mode decomposition-based multirate data-fusion framework for estimating structural dynamic displacement by integrating vision- and acceleration-based measurements

Abstract

Dynamic displacement is a crucial parameter in structural health monitoring (SHM) for assessing the safety, dependability, and suitability of structures under various types of excitations. Computer vision-based methods for dynamic displacement estimation have attracted much interest owing to their cost-effectiveness and convenience. However, these methods are limited by their low sampling rates and high data sensitivity. To compensate for these limitations, methods for combining data obtained from other sensors have been proposed. In this study, an experimental data-fusion framework for displacement estimation based on variational mode decomposition (VMD) was developed to leverage the advantages of vision- and acceleration-based measurements. The measurements were decomposed into ensembles of modes and recomposed to reconstruct the displacement with a higher accuracy and over a wider frequency range. An optimal mode recomposition method was proposed to achieve optimal mode combinations. Furthermore, this study introduced an improved vision-based displacement measurement method and a VMD-based indirect acceleration measurement method. The proposed framework was validated through four-story RC structure tests, which demonstrated that the method could enhance the accuracy of displacement estimation and extend the feasible frequency range compared with single-source displacement measurements. The method provides a promising solution for more effective health monitoring of modern structures subjected to a wide variety of dynamic loads.