Structural Engineering and Mechanics

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Output-only structural damage detection under multiple unknown white noise excitations

  • Ni, Pinghe (Key Laboratory of Urban Security and Disaster Engineering of Ministry of Education, Beijing University of Technology) ;
  • Wang, Xiaojuan (Key Laboratory of Urban Security and Disaster Engineering of Ministry of Education, Beijing University of Technology) ;
  • Zhou, Hongyuan (Key Laboratory of Urban Security and Disaster Engineering of Ministry of Education, Beijing University of Technology)
  • Received : 2020.03.09
  • Accepted : 2021.06.11
  • Published : 2021.08.10
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Abstract

Most of the existing output-only damage detection methods require the number of sensors should be larger than the number of unknown excitation force, and the force location should be available. This paper presents a novel output-only damage detection method without these requirements. The proposed method is based on the correlation function of acceleration responses. When the structure is under white noise excitations (or ambient excitations), the correlation function of acceleration responses can be treated as free vibration responses with unknown initial conditions. The unknown structural parameters and initial conditions can be simultaneously identified by minimizing the difference between the measured and calculated correlation functions. The unknown initial conditions are identified with state space method and the unknown structural parameters are updated with sensitivity method. Numerical studies of a 2D truss and a five-bay 3D frame structure are conducted to demonstrate the accuracy, effectiveness, and robustness of the proposed method. Experimental studies on an eight-floor steel frame are further carried out. Results show that the proposed method is not only insensitive to environmental noise but also applicable when the number of sensors is less than that of unknown excitations. Also, the proposed method can be used for damage detection when the force location is unknown.

Keywords

Acknowledgement

The presented work was supported Beijing Municipal Education Commission Projects (KM201810005019, KM202110005020), a research grant from the Niche Area Project Funding of the Hong Kong Polytechnic University (Project No.1-BB6F), and the research project of Beijing Natural Science Foundation (grant number 8184063).

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