A Eulerian Video Magnification Based Structural Damage Identification Method for Scaffold

Zhen-yu Liang (), Hao-long Chen (), Jia-hao Hua () and Yi-chuan Deng ()
Additional contact information
Zhen-yu Liang: South China University of Technology
Hao-long Chen: South China University of Technology
Jia-hao Hua: South China University of Technology
Yi-chuan Deng: South China University of Technology

A chapter in Proceedings of the 26th International Symposium on Advancement of Construction Management and Real Estate, 2022, pp 1122-1132 from Springer

Abstract: Abstract Scaffolds are temporary structure with many safety hazards in the construction site. Vibration information of scaffold can reflect its health state. Traditional vibration measurement methods are measured by contact sensors, which have the disadvantages of complicated operation, high cost and low efficiency. This paper proposes a structural damage identification method for scaffold based on phased-based Eulerian video magnification algorithm. The digital image of scaffold vibration collected by digital camera is firstly processed by phased-based Eulerian video magnification to acquire motion-magnified image sequence in the particular frequency bands. Then, canny edge detector is used to identify the edges in the image sequence and eliminate the noise resulted from the magnification. The edges in the image sequence are utilized to acquire time-history data of scaffold displacement based on the geometry centroid, from which we can obtain resonant frequencies after Fourier transformation and finally identify the damage states. The applicability of the proposed method is discussed in the context of the frame scaffold experiments with 10 kinds of damage states. By comparing the results between camera measurement and accelerometer measurement, the proposed method has satisfactory performance with average error of 0.95%.

Keywords: Scaffold; Structural damage identification; Eulerian video magnification; Computer vision; Canny edge detector (search for similar items in EconPapers)
Date: 2022
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:lnopch:978-981-19-5256-2_88

Ordering information: This item can be ordered from
http://www.springer.com/9789811952562

DOI: 10.1007/978-981-19-5256-2_88

Access Statistics for this chapter

More chapters in Lecture Notes in Operations Research from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().