A Computer Vision Application for Measuring the Deflection in a Two-dimensional View of Reinforced Concrete Beams

Authors

DOI:

https://doi.org/10.32871/rmrj2109.01.02

Keywords:

beam deflection, computer vision, dial gauge, structural member, reinforced concrete, reaction framework

Abstract

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.

Author Biography

Eduardo Jr. Piedad, University of San Jose-Recoletos, Cebu City, Philippines

Instructor I, Department of Electrical Engineering

References

American Society for Testing and Materials, International. (2001). Standard test method
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.

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Published

2021-05-28

How to Cite

Piedad, E. J., Carpio, B. R., Sanchez, K., & Jabian, M. (2021). A Computer Vision Application for Measuring the Deflection in a Two-dimensional View of Reinforced Concrete Beams. Recoletos Multidisciplinary Research Journal, 9(1), 13–21. https://doi.org/10.32871/rmrj2109.01.02

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