Bridge deformation prediction based on SHM data using improved VMD and conditional KDE

Deformation is a paramount index of bridge health monitoring. Accurate prediction of bridge deformation is of great significance to evaluate bridge performance. However, owing to the complex mechanical mechanism of bridge performance evolution, it is arduous to obtain a satisfactory forecasting result by simple data-driven methods. To this end, this paper proposes an innovative data-driven method based on an improved variational mode decomposition (IVMD) and conditional kernel density estimation (CKDE). Specifically, the raw deformation data are firstly pretreated by IVMD. This newly developed technique could not only adaptively optimize the decomposition level number through Hilbert transform and empirical mode decomposition, but also hinder the disturbance of irrelevant components by Kullback-Leibler divergence and Gram-Schmidt orthogonal. Then, auto-regression integrated moving average model is established to excavate the linear feature hidden in the data. Finally, CKDE in conjunction with Gaussian distribution assumption is designed to describe the above modeling errors, by which both point and interval predictions are generated. Obviously, this method avoids exploring the complex internal mechanism of structural behavior evolution. Three case studies based on structural health monitoring data are used to systematically evaluate its effectiveness. The results manifest that this method possesses higher reliability in comparison with the other concerned models, and could lay a foundation for early warning purpose.