A hybrid time-frequency method for robust drive-by modal identification of bridges

Abstract

Indirect bridge health monitoring (iBHM) has gained significant attention in recent years as an alternative to direct bridge monitoring techniques. iBHM leverages the use of a vehicle traveling over a bridge as a sensing device as well as an actuator. The resulting data acquired from the vehicle contains dynamic information about the bridge, vehicle suspension, engine noise, ambient traffic noise, road roughness, etc. Due to the presence of sensor-vehicle-bridge interactions in vehicle measurement, a powerful decoupling framework of system identification is required to extract useful information about the bridge and delineate the effect of vehicle and driving frequencies. This paper proposes a hybrid time-frequency method capable of decoupling vehicle-bridge interactions of vehicle measurement and performing a robust bridge modal identification under various operational challenges. In the proposed research, wavelet packet transformation (WPT) is first used to decompose the vehicle measurement into various WPT coefficients. Then, the modal frequencies of the bridge are extracted from the resulting WPT coefficients using Synchro-Extracting Transform (SET) and are separated from the vehicle and driving frequencies. The proposed method is validated using numerical and laboratory studies as well as a full-scale study comprising of a 220 m long box-girder bridge. The results provide strong evidence that the proposed hybrid time-frequency method can serve as a promising iBHM method.