Author
Listed:
- Yapeng Guo
(School of Transportation Science and Engineering, Harbin Institute of Technology, 73 Huanghe Road, Harbin 150090, China)
- Peng Zhong
(School of Transportation Science and Engineering, Harbin Institute of Technology, 73 Huanghe Road, Harbin 150090, China
Shanghai Weibuild Technology Co., Ltd., Shanghai 200949, China)
- Yi Zhuo
(China Railway Design Corporation, Tianjin 300142, China)
- Fanzeng Meng
(China Railway Design Corporation, Tianjin 300142, China)
- Hao Di
(China Railway Design Corporation, Tianjin 300142, China)
- Shunlong Li
(School of Transportation Science and Engineering, Harbin Institute of Technology, 73 Huanghe Road, Harbin 150090, China)
Abstract
In recent years, computer vision-based structural displacement acquisition technique has received wide attention and research due to the advantages of easy deployment, low-cost, and non-contact. However, the displacement field acquisition of large-scale structures is a challenging topic as a result of the contradiction of camera field-of-view and resolution. This paper presents a large-scale structural displacement field calculation framework with integrated computer vision and physical constraints using only one camera. First, the full-field image of the large-scale structure is obtained by processing the multi-view image using image stitching technique; second, the full-field image is meshed and the node displacements are calculated using an improved template matching method; and finally, the non-node displacements are described using shape functions considering physical constraints. The developed framework was validated using a scaled bridge model and evaluated by the proposed evaluation index for displacement field calculation accuracy. This paper can provide an effective way to obtain displacement fields of large-scale structures efficiently and cost-effectively.
Suggested Citation
Yapeng Guo & Peng Zhong & Yi Zhuo & Fanzeng Meng & Hao Di & Shunlong Li, 2023.
"Displacement Field Calculation of Large-Scale Structures Using Computer Vision with Physical Constraints: An Experimental Study,"
Sustainability, MDPI, vol. 15(11), pages 1-17, May.
Handle:
RePEc:gam:jsusta:v:15:y:2023:i:11:p:8683-:d:1157189
Download full text from publisher
Corrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:15:y:2023:i:11:p:8683-:d:1157189. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
We have no bibliographic references for this item. You can help adding them by using this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .
Please note that corrections may take a couple of weeks to filter through
the various RePEc services.