Investigating bridge vibrational modes under operational conditions using time-frequency analysis

Abstract

AbstractThis study aims to accurately and effectively investigate the instantaneous frequency (IF) of the monitored acceleration of the bridge, as well as the individual components of the monitored signal, using advanced time-frequency techniques. To achieve this, first, synthetic signals resembling the monitored acceleration are constructed and processed using four time-frequency techniques. The accuracy of IF tracking, signal reconstruction, and representation resolution are obtained and compared between these different time-frequency techniques. Subsequently, the monitored vertical and torsional accelerations from the Humber Bridge are analysed using the Fourier synchrosqueezed transform and the improved multisynchrosqueezing transform. This allows for the reconstruction of individual acceleration components along with the corresponding variation of the natural frequency. The magnitude of each component of the monitored acceleration is then obtained. The relative importance of modes in the monitored acceleration is evaluated based on the acceleration root mean square. This study presents a novel approach that involves reconstructing the monitored acceleration and assessing the magnitude of individual components of the monitored data of the bridge under operational conditions using the advanced time-frequency method. The method is particularly suitable for addressing multicomponent monitored data and is expected to benefit future practical research in the bridge health monitoring field.Keywords: Time-frequency analysisreconstruction of accelerationinstantaneous frequency trackingresolution of representationroot mean squareoperational conditionssynchrosqueezed transform AcknowledgementsThe author expresses his gratitude to Dr. Ki Young Koo at the University of Exeter for providing the monitored data and contributing to the code.Disclosure statementThe author reports there are no competing interests to declare.