Detection and Measurements of Cracks in Axially Loaded Tension RC Members by Image Processing Technique

Md. Mahfuzur Rahman^1, , Ismail Saifullah¹ and Sunthonu Kumar Ghosh¹

¹Department of Civil Engineering, Khulna University of Engineering & Technology (KUET), Bangladesh

Cite this paper:
Md. Mahfuzur Rahman, Ismail Saifullah and Sunthonu Kumar Ghosh. Detection and Measurements of Cracks in Axially Loaded Tension RC Members by Image Processing Technique. American Journal of Civil Engineering and Architecture. 2019; 7(2):115-120. doi: 10.12691/ajcea-7-2-5

Abstract

Concrete cracking is very common and appeared in all types of concrete structures. Detection of concrete crack initiation and measurement of their characteristics has been a part of structural monitoring and maintenance program. Though manual inspection of cracks in the concrete structure is the oldest and still an accepted method of crack measurements, a more accurate technique for crack detection and measurement, applicable for all kind of concrete structures, is required. Automatic crack identification and crack width measurement by the image analysis method has been gaining popularity due to its versatile applicability, more accuracy and safety standard in difficult situations. In this context, the image analysis method is adopted in laboratory testing of the concrete prism with reinforcement under axial tension to identify and measure cracks in concrete prism samples. In this research work, a crack quantification method based on 2D image analysis is utilized. Raw colour images (RGB images) were obtained by means of a camera for cracks formed due to axial tension in reinforcement. Processing of RGB images with sufficient information will provide the dimension of crack. For all the image post-processing, an open source ImageJ software was utilized and characteristics of cracks were automatically determined by Ridge Detection Plugin. The results of image processing were also verified with the results from microscopic measurement of cracks in reinforced concrete prism samples.

Keywords:
axial tension concrete crack width image processing microscopic ImageJ

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References:

[1]	Wu, X., Jiang, Y., Masaya, K., Taniguchi, T., & Yamato, T. (2017). Study on the correlation of vibration properties and crack index in the health assessment of tunnel lining. Shock and Vibration, 2017.
[2]	Adhikari, R. S., Moselhi, O., & Bagchi, A. (2014). Image-based retrieval of concrete crack properties for bridge inspection. Automation in construction, 39, 180-194.
[3]	Anil, E. B., Akinci, B., Garrett, J. H., & Kurc, O. (2016). Information requirements for earthquake damage assessment of structural walls. Advanced Engineering Informatics, 30(1), 54-64.
[4]	Torok, M. M., Golparvar-Fard, M., & Kochersberger, K. B. (2013). Image-based automated 3D crack detection for post-disaster building assessment. Journal of Computing in Civil Engineering, 28(5), A4014004.
[5]	Thatoi, D., Guru, P., Jena, P. K., Choudhury, S., & Das, H. C. (2014). Comparison of CFBP, FFBP, and RBF networks in the field of crack detection. Modelling and Simulation in Engineering, 2014, 3.
[6]	Koch, C., Georgieva, K., Kasireddy, V., Akinci, B., & 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.
[7]	Wang, M. L., Lynch, J. P., & Sohn, H. (Eds.). (2014). Sensor Technologies for Civil Infrastructures, Volume 1: Sensing Hardware and Data Collection Methods for Performance Assessment. Elsevier.
[8]	Iyer, S., & Sinha, S. K. (2005). A robust approach for automatic detection and segmentation of cracks in underground pipeline images. Image and Vision Computing, 23(10), 921-933.
[9]	Salman, M., Mathavan, S., Kamal, K., & Rahman, M. (2013, October). Pavement crack detection using the Gabor filter. In 16th international IEEE conference on intelligent transportation systems (ITSC 2013) (pp. 2039-2044). IEEE.
[10]	Sinha, S. K., & Fieguth, P. W. (2006). Automated detection of cracks in buried concrete pipe images. Automation in Construction, 15(1), 58-72.
[11]	Talab, A. M. A., Huang, Z., Xi, F., & HaiMing, L. (2016). Detection crack in image using Otsu method and multiple filtering in image processing techniques. Optik-International Journal for Light and Electron Optics, 127(3), 1030-1033.
[12]	Rimkus, A., Podviezko, A., & Gribniak, V. (2015). Processing digital images for crack localization in reinforced concrete members. Procedia Engineering, 122, 239-243.
[13]	Alam, S. Y., Loukili, A., Grondin, F., & Rozière, E. (2015). Use of the digital image correlation and acoustic emission technique to study the effect of structural size on cracking of reinforced concrete. Engineering Fracture Mechanics, 143, 17-31.
[14]	Ebrahimkhanlou, A., Farhidzadeh, A., & Salamone, S. (2016). Multifractal analysis of crack patterns in reinforced concrete shear walls. Structural Health Monitoring, 15(1), 81-92.
[15]	Yu, T., Zhu, A., & Chen, Y. (2016). Efficient crack detection method for tunnel lining surface cracks based on infrared images. Journal of Computing in Civil Engineering, 31(3), 04016067.
[16]	Bose, K., & Bandyopadhyay, S. K. (2016). Crack detection and classification in concrete structure. Journal for Research, 2(4), 29-38.
[17]	Nguyen, H. N., Kam, T. Y., & Cheng, P. Y. (2014). An automatic approach for accurate edge detection of concrete crack utilizing 2D geometric features of crack. Journal of Signal Processing Systems, 77(3), 221-240.
[18]	Fujita, Y., & Hamamoto, Y. (2011). A robust automatic crack detection method from noisy concrete surfaces. Machine Vision and Applications, 22(2), 245-254.
[19]	Glud, J. A., Dulieu-Barton, J. M., Thomsen, O. T., & Overgaard, L. C. T. (2016). Automated counting of off-axis tunnelling cracks using digital image processing. Composites Science and Technology, 125, 80-89.
[20]	Ebrahimkhanlou, A., Farhidzadeh, A., & Salamone, S. (2016). Multifractal analysis of crack patterns in reinforced concrete shear walls. Structural Health Monitoring, 15(1), 81-92.
[21]	Hamrat, M., Boulekbache, B., Chemrouk, M., & Amziane, S. (2016). Flexural cracking behavior of normal strength, high strength and high strength fiber concrete beams, using digital image correlation technique. Construction and Building Materials, 106, 678-692.