Bridge response separation method based on recursive variational mode decomposition

Abstract Due to the influence of various factors on bridge sensors, the signals obtained often contain multiple signal components, including temperature and vehicle induced effect. It is necessary to separate and analyze individual signals in bridge health detection. In order to separate temperature and vehicle response components from complex signals, this article proposes an improved variational mode decomposition (VMD) algorithm based on recursive methods, which takes the mean value of each recursive block as the eigenvalue, fits the eigenvalues of each recursive block using the least squares method, and separates the first intrinsic mode function. The applicability of this method in the field of bridges was first verified through modal decomposition of simulated deflection and strain data. Then based on the health monitoring data of the Jingtai Expressway viaduct, the rapid separation of temperature response and vehicle response of the bridge has been achieved. The results indicate that the recursive method, in an online continuous decomposition environment, is approximately seven times faster than the traditional VMD algorithm. Moreover, when setting the same penalty factor, the mean square error obtained from separating finite element simulation data is smaller than that of VMD, and the separated actual measurement data has a higher correlation coefficient with temperature. This resolves the computational speed issue of the VMD algorithm in real-time bridge health monitoring, demonstrating the feasibility of the recursive algorithm, and effectively separates signals related to temperature and vehicles.