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Smart Structures and Systems Volume 30, Number 3, September 2022 , pages 273-286 DOI: https://doi.org/10.12989/sss.2022.30.3.273 |
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Identification of structural systems and excitations using vision-based displacement measurements and substructure approach |
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Ying Lei and Chengkai Qi
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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. | ||
| Key Words | ||
| displacement measurement; EKF-UI-WDF; force identification; smoothing; structural identification; substructure identification; system without direct feedthrough; vision sensor | ||
| Address | ||
| School of Architecture and Civil Engineering, Xiamen University, Xiamen 361005, China. | ||