Extraction of bridge fundamental frequency from estimated vehicle excitation through a particle filter approach. (18th August 2018)

• Record Type: Journal Article
• Authors: Wang, Haoqi; Nagayama, Tomonori; Nakasuka, Junki; Zhao, Boyu; Su, Di
• Is Part Of: Journal of sound and vibration. Volume 428(2018)
• Page Start: 44
• Page End: 58
• Publication Date: 2018-08-18
• Subjects: Bridge frequency extraction -- Indirect methods -- Vehicle–bridge interaction -- Pavement roughness -- Particle filter; Sound -- Periodicals; Vibration -- Periodicals; Son -- Périodiques; Vibration -- Périodiques; Sound; Vibration; Periodicals; Electronic journals; 620.205
• DOI: 10.1016/j.jsv.2018.04.030
• Languages: English
• ISSNs: 0022-460X

Abstract

Abstract: A bridge's natural frequencies are important dynamic properties reflecting the structural condition of the bridge. Numerous studies have been conducted in the field to extract a bridge's natural frequencies from responses of passing vehicles. The bridge frequency peaks are, however, not easily observed, because pavement roughness often influences the spectra of vehicle responses. In this research, a method that extracts the fundamental frequency of a bridge from the responses of an ordinary vehicle with its parameters calibrated in advance is proposed. The method is based on the idea that the vehicle passing across a bridge is excited by two sources, i.e., pavement roughness and bridge vibration. The excitation inputs to the vehicle, i.e., displacement inputs at the front and rear tire locations, are estimated from vehicle responses using a particle filter method. The estimated displacement inputs at the front and rear tires are then subtracted from each other after shifting by a wheel–base distance to eliminate the roughness influence, which commonly appears in both signals. The signal after the subtraction contains only the bridge vibration influence and is used to extract the fundamental frequency of the bridge. This indirect method of bridge frequency extraction is investigated through numerical simulations. A field measurement was also conducted, and it showed that the bridge's fundamental frequency was successfully extracted with a good accuracy for several driving-speed cases. Highlights: A particle filter estimates vehicle excitation at the axles from vehicle responses. The bridge fundamental frequency is extracted from the estimated excitation. Inadequate speeds and the speed dependency of the proposed method are clarified. Numerical example and field measurement validate the proposed method.