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Smart Structures and Systems
  Volume 30, Number 1, July 2022 , pages 107-119

Modal identification of time-varying vehicle-bridge system using a single sensor
Yilin Li, Wen-Yu He, Wei-Xin Ren, Zhiwei Chen and Junfei Li

    Modal parameters are widely used in bridge damage detection, finite element model (FEM) updating and design optimization. However, the conventional modal identification approaches require large number of sensors, enormous data processing workload, but normally result in mode shapes with low accuracy. This paper proposes a modal identification method of time-varying vehicle-bridge system using a single sensor. Firstly, the essential physical relationship between the instantaneous frequency of the vehicle-bridge system and the bridge mode shapes are derived. Subsequently, based on the synchroextracting transform, the instantaneous frequency of the system is tracked through the dynamic response collected by a single sensor, and further the modal parameters are estimated by using the derived physical relationship. Then numerical and experimental examples are conducted to examine the feasibility and effectiveness of the proposed method. Finally, the modal parameters identified by the proposed method are applied in bridge FEM updating. The results manifest that the proposed method identifies the modal parameters with high accuracy via a single sensor, and can provide reliable data for the FEM updating.
Key Words
    modal parameters; moving vehicle-bridge system; synchroextracting transform; time-frequency analysis; time-varying
(1) Yilin Li, Wen-Yu He, Junfei Li:
Department of Civil Engineering, Hefei University of Technology, Hefei, Anhui 230009, China;
(2) Wen-Yu He:
Anhui Engineering Laboratory for Infrastructural Safety Inspection and Monitoring, Hefei University of Technology, Hefei, Anhui 230009, China;
(3) Wei-Xin Ren:
College of Civil and Transportation Engineering, Shenzhen University, Shenzhen, Guangdong 518061, China;
(4) Zhiwei Chen:
Department of Civil Engineering, Xiamen University, Xiamen, 361005, China.

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