Buy article PDF
The purchased file will be sent to you
via email after the payment is completed.
US$ 35
|
Smart Structures and Systems Volume 31, Number 4, April 2023 , pages 393-407 DOI: https://doi.org/10.12989/sss.2023.31.4.393 |
|
|
|
A novel computer vision-based vibration measurement and coarse-to-fine damage assessment method for truss bridges |
||
Wen-Qiang Liu, En-Ze Rui, Lei Yuan, Si-Yi Chen, You-Liang Zheng and Yi-Qing Ni
|
||
| Abstract | ||
| To assess structural condition in a non-destructive manner, computer vision-based structural health monitoring (SHM) has become a focus. Compared to traditional contact-type sensors, the advantages of computer vision-based measurement systems include lower installation costs and broader measurement areas. In this study, we propose a novel computer vision-based vibration measurement and coarse-to-fine damage assessment method for truss bridges. First, a deep learning model FairMOT is introduced to track the regions of interest (ROIs) that include joints to enhance the automation performance compared with traditional target tracking algorithms. To calculate the displacement of the tracked ROIs accurately, a normalized cross-correlation method is adopted to fine-tune the offset, while the Harris corner matching is utilized to correct the vibration displacement errors caused by the non-parallel between the truss plane and the image plane. Then, based on the advantages of the stochastic damage locating vector (SDLV) and Bayesian inference-based stochastic model updating (BISMU), they are combined to achieve the coarse-to-fine localization of the truss bridge's damaged elements. Finally, the severity quantification of the damaged components is performed by the BI-SMU. The experiment results show that the proposed method can accurately recognize the vibration displacement and evaluate the structural damage. | ||
| Key Words | ||
| computer vision; damage assessment; deep learning; model updating; structural health monitoring; vibration measurement | ||
| Address | ||
| (1) Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong S.A.R.; (2) National Rail Transit Electrification and Automation Engineering Technology Research Center (Hong Kong Branch), Hung Hom, Kowloon, Hong Kong S.A.R. | ||