Processing Digital Image for Measurement of Crack Dimensions in Concrete

Document Type: Research Papers


1 Research Scholar, Department of Civil Engineering, National Institute of Technology

2 Professor, Department of Civil Engineering, National Institute of Technology-Tiruchirappalli


The elements of the concrete structure are most frequently affected by cracking. Crack detection is essential to ensure safety and performance during its service life. Cracks do not have a regular shape, in order to achieve the exact dimensions of the crack; the general mathematical formulae are by no means applicable. The authors have proposed a new method which aims to measure the crack dimensions of the concrete by utilizing digital image processing technique. A new algorithm has been defined in MATLAB. The acquired data has been analyzed to obtain the most precise results. Here both the length and width of the crack are obtained from image processing by removing background noise for the accuracy of measurement. A semi-automatic methodology is adapted to measure the crack length and crack width. The applicability of the program is verified with the past literature works.


Main Subjects

Abdel-Qader, I., Abudayyeh, O. and Kelly, M.E. (2003). "Analysis of edge-detection techniques for crack identification in bridges", Journal of Computing in Civil Engineering, 17(4), 255-263.

ACI 224R-90. (1990). "Control of Cracking in Concrete Structures", America Concrete Institute: Farmington Hills, MI, USA.

Firdousi, R. and Parveen, S. (2014). "Local thresholding techniques in image binarization", International Journal of Engineering and Computer Science, 3(3), 4062-4065.

Kabir, S. and Rivard, P. (2007). "Damage classification of concrete structures based on grey level co-occurrence matrix using Haar's discrete wavelet transform", Computers and Concrete, 4(3), 243-257.

Khalili, K. and Vahidnia, M. (2014). "Improving the accuracy of crack length measurement using machine vision", 8th International Conference Inter-Disciplinarily in Engineering., Procedia Technology, 19, 48-55.

Koch, C., Georgieva, K., Kasireddy, V., Akinci, B. and Fieguth, P. (2015). "A review on computer vision based defect detection and condition assessment of concrete and asphalt civil infrastructure", Advanced Engineering Informatics, 29(2), 196-210.

Laefer, D.F., Gannon, J. and Deely, E. (2010). "Reliability of crack detection methods for baseline condition assessments", Journal of Infrastructure Systems, 16(2), 129-137.

Lattanzi, D. and Miller, G.R. (2014). "Robust automated concrete damage detection algorithms for field applications", Journal of Computing in Civil Engineering, 28(2), 253-262.

Li, S. and An, X. (2014). "Method for estimating workability of self-compacting concrete using mixing process images", Computers and Concrete, 13(6), 781-798.

Martins, A.P., Pizolato Junior, J.C. and Belini, V.L. (2013). "Image-based method for monitoring of crack opening on masonry and concrete using mobile platform", Revista IBRACON de Estruturas e Materiais, 6(3), 414-435.

Mohammadizadeh, M. (2018). "Identification of Structural Defects Using Computer Algorithms", Civil Engineering Infrastructures Journal, 51(1), 55-86.

Önal, O., Özden, G. and Felekoglu, B. (2008). "A methodology for spatial distribution of grain and voids in self-compacting concrete using digital image processing methods", Computers and Concrete, 5(1), 61-74.

Prasanna, P., Dana, K., Gucunski, N. and Basily, B. (2012). "Computer-vision based crack detection and analysis, in SPIE smart structures and materials + nondestructive evaluation and health monitoring", In Sensors and Smart Structures Technologies for Civil, Mechanical and Aerospace Systems, Vol. 8345, pp. 834542, International Society for Optics and Photonics.

Yamaguchi, T. and Hashimoto, S. (2010). "Fast crack detection method for large-size concrete surface images using percolation-based image processing", Machine Vision and Applications, 21(5), 797-809.

Yu, S.N., Jang, J.H. and Han, C.S. (2007). "Auto inspection system using a mobile robot for detecting concrete cracks in a tunnel", Automation in Construction, 16(3), 255-261.