Smart Structures and Systems

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Identification of structural systems and excitations using vision-based displacement measurements and substructure approach

  • Lei, Ying (School of Architecture and Civil Engineering, Xiamen University) ;
  • Qi, Chengkai (School of Architecture and Civil Engineering, Xiamen University)
  • Received : 2022.06.26
  • Accepted : 2022.07.10
  • Published : 2022.09.25
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Abstract

In recent years, vision-based monitoring has received great attention. However, structural identification using vision-based displacement measurements is far less established. Especially, simultaneous identification of structural systems and unknown excitation using vision-based displacement measurements is still a challenging task since the unknown excitations do not appear directly in the observation equations. Moreover, measurement accuracy deteriorates over a wider field of view by vision-based monitoring, so, only a portion of the structure is measured instead of targeting a whole structure when using monocular vision. In this paper, the identification of structural system and excitations using vision-based displacement measurements is investigated. It is based on substructure identification approach to treat of problem of limited field of view of vision-based monitoring. For the identification of a target substructure, substructure interaction forces are treated as unknown inputs. A smoothing extended Kalman filter with unknown inputs without direct feedthrough is proposed for the simultaneous identification of substructure and unknown inputs using vision-based displacement measurements. The smoothing makes the identification robust to measurement noises. The proposed algorithm is first validated by the identification of a three-span continuous beam bridge under an impact load. Then, it is investigated by the more difficult identification of a frame and unknown wind excitation. Both examples validate the good performances of the proposed method.

Keywords

Acknowledgement

The research in this paper is supported by the National Natural Science Foundation of China via the grant No. 52178304.

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