A Computer Vision Application for Measuring the Deflection in a Two-dimensional View of Reinforced Concrete Beams
DOI:
https://doi.org/10.32871/rmrj2109.01.02Keywords:
beam deflection, computer vision, dial gauge, structural member, reinforced concrete, reaction frameworkAbstract
A novel computer vision application is developed to measure the deflection of two-dimensional (2D) reinforced concrete structural members. Eight beam samples, with dimensions of 160 mm x 150 mm x 1400 mm are loaded and simulated under a four-point loading test until failure using a reaction framework machine. A camera is fixed at the center front view of the concrete beams to capture the deflection of the samples while testing. In each test, a dial indicator is installed and the maximum deflection is manually recorded. Based on the results, the maximum deflection values recorded by the proposed application obtained an average error of 18.38 % when compared to the manual measured results. This indicates that computer vision-based application can provide a beam-wide scale deflection performance, compared to the traditional point-based deflection reading. This study paves a new possibility of aiding manual measurements of concrete beams and all other structural studies.
References
for density, relative density (specific gravity), and absorption of coarse aggregate, C127-15.
ASTM International. 10.1520/C0127-15
American Society for Testing and Materials, International. (2002). Standard practice
for making and curing test specimens in the laboratory, C192-C192M-02. ASTM
International. 10.1520/C0192_C0192M-02
American Society for Testing and Materials, International. (2003). Standard test method
for density, relative density and absorption of fine aggregate, C128-01. ASTM International.
10.1520/C0128-01E01
Bradski, G.,&Kaehler, A. (2008). Learning OpenCV: Computer vision with the OpenCV library.
O’Reilly Media, Inc.
Chaczko, Z., Yeoh, L. A., & Mahadevan, V. (2010, February 9-11). A preliminary investigation
on computer vision for telemedicine systems using OpenCV [Conference session]. Second
International Conference on Machine Learning and Computing, Bangalore, India.
10.1109/ICMLC.2010.70
Florczyk, S. (2005). Robot vision: Video-based indoor exploration with autonomous and
mobile robots. WILEY-VCH Verlag GmbH & Co. KGaA.
Lü, C., Wang, X., & Shen, Y. (2013, December 16-18). A stereo vision measurement system
based on OpenCV [Conference session]. 6th International Congress on Image and Signal
Processing, Hangzhou, China. 10.1109/CISP.2013.6745259
Maas, H. G., & Hampel, U. (2006). Photogrammetric techniques in civil engineering material testing
and structural monitoring. Photogrammetric Engineering & Remote Sensing, 72(1),39-45.
https://www.asprs.org/wp-content/uploads/
Piedad, E. J. (2015). Civil-ivision. https://github.com/epiedadjr/civil-ivision.git
Piedad, E.D. Jr., & Villeta, R. B. (2016). Displacement and illumination levels effect on shortdistance
measurement errors of using a camera. Recoletos Multidisciplinary Research Journal, 4(1). https://doi.org/10.32871/rmrj1604.01.06
Piedad, E. J., Le, T. T., Aying, K., Pama, F. K., & Tabale, I. (2019, October 17-20). Vehicle count system
based on time interval image capture method and deep learning mask R-CNN [Conference session].
TENCON 2019-2019 IEEE Region 10 Conference (TENCON), Kochi, India. 10.1109/TENCON.2019.8929426
Vernon, D. (1991). Machine vision: Automated visual inspection and robot vision.
Prentice-Hall International.
Downloads
Published
How to Cite
Issue
Section
License
Copyright of the Journal belongs to the University of San Jose-Recoletos