Smart Structures and Systems

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Extraction of quasi-static component from vehicle-induced dynamic response using improved variational mode decomposition

  • Zhiwei Chen (Department of Civil Engineering, Xiamen University) ;
  • Long Zhao (Department of Civil Engineering, Xiamen University) ;
  • Yigui Zhou (Department of Civil Engineering, Xiamen University) ;
  • Wen-Yu He (Department of Civil Engineering, Hefei University of Technology) ;
  • Wei-Xin Ren (College of Civil and Transportation Engineering, Shenzhen University)
  • Received : 2022.05.30
  • Accepted : 2022.11.07
  • Published : 2023.02.25
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Abstract

The quasi-static component of the moving vehicle-induced dynamic response is promising in damage detection as it is sensitive to bridge damage but insensitive to environmental changes. However, accurate extraction of quasi-static component from the dynamic response is challenging especially when the vehicle velocity is high. This paper proposes an adaptive quasi-static component extraction method based on the modified variational mode decomposition (VMD) algorithm. Firstly the analytical solutions of the frequency components caused by road surface roughness, high-frequency dynamic components controlled by bridge natural frequency and quasi-static components in the vehicle-induced bridge response are derived. Then a modified VMD algorithm based on particle swarm algorithm (PSO) and mutual information entropy (MIE) criterion is proposed to adaptively extract the quasi-static components from the vehicle-induced bridge dynamic response. Numerical simulations and real bridge tests are conducted to demonstrate the feasibility of the proposed extraction method. The results indicate that the improved VMD algorithm could extract the quasi-static component of the vehicle-induced bridge dynamic response with high accuracy in the presence of the road surface roughness and measurement noise.

Keywords

Acknowledgement

The authors wish to acknowledge the financial supports from the National Natural Science Foundation of China (52278319, 51878234 and 51778204), Natural Science Foundation for Distinguished Young Scholars of Anhui Province (2208085J20), and Shenzhen Science and Technology Program (No. KQTD20180412181337494). Any opinions and concluding remarks presented in this paper are entirely those of the authors.

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