Available online at www.sciencedirect.com
ScienceDirect Procedia Engineering 122 (2015) 239 – 243
Operational Research in Sustainable Development and Civil Engineering - meeting of EURO working group and 15th German-Lithuanian-Polish colloquium (ORSDCE 2015)
Processing digital images for crack localization in reinforced concrete members Arvydas Rimkus, Askoldas Podviezko*, Viktor Gribniak Vilnius Gediminas technical university, Sauletekis av. 11, Vilnius 10223, Lithuania
Abstract Cracks are among the most frequent types of damage occurring in concrete structures. The structural inspection often requires application of non-destructive techniques for localization of damages, and for validation of the structural integrity. Traditionally, cracks are localized and measured using crack width templates or microscopes, and consequently the crack pattern is transferred to a drawing sheet manually. These operations imply a high level of imperfection, subjectivity of judgment, furthermore they are time-consuming. In the engineering practice, digital image analysis systems can be implemented for reliable detection of concrete surface cracking. In this paper, such procedure is proposed. Images obtained by Digital Image Correlation technique are used for the crack localization. The image processing is performed in two steps. First, the image is modified to achieve strictly horizontal position for the purpose of removing effect of perspective and shape deformation. Ambient noise is also reduced. Subsequently, the vertical shape of cracks is used in order to localize their position. The Agglomerative Hierarchical Clustering Technique is used at the second analysis step for identifying the “cracking pixels” (projections) that closely resemble one another. The proposed algorithm can be applied to datasets of the images generated at different loading levels for the purpose of producing a diagram that represents evolution of the crack distances with increasing load. It is illustrated using the experimental data obtained by the authors. © Published by Elsevier Ltd. This ©2015 2015The TheAuthors. Authors. Published by Elsevier Ltd. is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer-review under responsibility of the organizing committee of the Operational in Sustainable Development Peer-review under responsibility of the organizing committee of the Operational ResearchResearch in Sustainable Development and Civil and Civil Engineering- meeting - meeting of EURO working andGerman-Lithuanian-Polish 15th German-Lithuanian-Polish colloquium. Engineering of EURO working groupgroup and 15th colloquium Keywords: Reinforced concrete; digital image pocessing; cracking; clustering; computer-assisted
* Corresponding author. Tel.: +3-706-042-3820. E-mail address:
[email protected]
1877-7058 © 2015 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license
(http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer-review under responsibility of the organizing committee of the Operational Research in Sustainable Development and Civil Engineering - meeting of EURO working group and 15th German-Lithuanian-Polish colloquium
doi:10.1016/j.proeng.2015.10.031
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1. Introduction Cracking is one of the earliest indications of the degradation of concrete structures. To ensure safety and performance during the service life, detection of cracks is the most important part of structural maintenance. Furthermore, reinforced concrete structures are usually designed to allow cracking under service loads. As a result, inspections of the structural integrity becomes even more substantial for the lifetime predictions. Traditionally, crack detection is performed manually by human inspectors using visually applied tools, such as crack width templates or microscopes. Due to the subjective nature of the measurement process, the quantity of the results highly depends on the training, experience and the knowledge of the personnel performing the inspection [1]. Furthermore, manually performed crack detection methods are time-consuming and relevant expensive. Recently, computer-assisted systems for reliable detection of surface cracking of the concrete have been developed as an alternative for handcrafted methods [2], [3]. Most of the computer systems are based on the digital image analysis, which can be performed semi-automatically [4]. In order to detect cracks, users of the systems need to specify the focal part of the element for inspection. However, the cracks cannot be detected accurately when the images include noise. In this case, a manual interaction must be done. Consequently, such systems cannot be used in the fully-automated analysis. In attempting to realize an automated crack detection system, a more reliable digital image processing technique is essential. In the engineering practice, Digital Image Correlation (DIC) technique has been implemented for reliable detection of concrete surface cracking, e.g. [5], [6], and [7]. This paper provides a procedure for processing of digital images of gradually appearing and developing cracks in reinforced concrete members. In the current formulation, images obtained with a help of the DIC are used for the crack localization. The image processing is performed in two steps. First, the image is modified to achieve strictly horizontal position for the purpose of removing effect of perspective and shape deformation. Ambient noise is also reduced. Subsequently, the vertical shape of cracks is used in order to localize their position. The Agglomerative Hierarchical Clustering Technique is used at the second analysis step for identifying the “cracking pixels” (projections) that closely resemble one another. The proposed procedure can be applied to datasets of the images generated at different loading levels for the purpose of producing a diagram that represents evolution of the crack distances with increasing load. It is illustrated using the experimental data of reinforcement concrete beam tested by the authors. 2. Digital image processing procedure Cracking behavior in the pure bending zone of a reinforced concrete beam is examined in this section as a case study. Application of the proposed procedure for processing of digital images is illustrated using the cracking data from the experimental program [8]. Simply supported reinforced concrete beam with a nominal length of 1500 mm tested under a four-point bending scheme with constant (400 mm) spans was selected for the analysis. To assess the crack propagation in the pure bending zone (area of 400×200 mm, or approx. 800×400 pixels in Fig. 1), the surface deformations of the beam were monitored using two digital cameras. Localization of the “cracked pixels” is the target of the initial analysis of the digital images. Using the idea proposed and developed by the second author, the “cracked pixels” were localized automatically. 2.1. Determination of “cracked pixels” The developed algorithm for the digital image processing is aimed to obtain coordinates of pixels, which correspond to cracks. In the present example, images obtained with the help of the DIC (DaVis 8.1.6 software by La vision) were used for automatic localization of the cracks. At a particular loading level the surface strain distribution in the analyzed specimen is determined using the DIC technique: information from the digital cameras is transformed to the deformation field, where levels of strain are represented by various colors. Scale of the colors shows matching levels of strain at the surface of an analyzed concrete member. The cracking strains (represented by different colors in accordance with corresponding strain levels) are recognized with the help of the proposed algorithm. It was realized in MATLAB programming language.
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At the first step of the analysis, images were modified to achieve strictly horizontal position for the purpose of removing effect of perspective and shape deformation. Ambient noise was also reduced. Subsequently, the vertical shape of cracks was used in order to localize their position. Effects of the background were nullified by assigning zero to the corresponding color of the background; the image was transformed into a black-and-white image; the pixels of a lower brightness than the threshold (maximal brightness divided by two and rounded to the closest integer) was assigned black color or zero. The resulting image contained a lower noise and a higher contrast. At the second step, the image segmentation method was used for crack localization process [9], [10]. Columns of the resulting matrix containing color codification of pixels of the image were summed producing a vector V of the same dimension as the horizontal size of the analyzed image (in pixels). Maximal value within vector V was determined. The half-maximum was set as a threshold. All such elements of V with values lower than the threshold were assigned 0; remaining values were assigned 1 thus making an effect of further reduction of noise. The resulting sequence of consequent elements of value 1 was considered as belonging to a crack. Geometric centers of such sequences were considered as coordinates of the cracks. Finally, coordinates of the cracks were transformed from pixels to millimeters. 2.2. Assessment of the crack distance Crack distance was determined by clustering the crack projections on the longitudinal axis of the beam. The cracks were obtained using the technique described in Section 2.1 and plotted on a horizontal grid with the spacing of 10 pixels. Origin of the longitudinal axis x was set at the boundary of the pure bending zone. A collection of the gathered projections of the cracking points produced a dataset for the clustering. 1. Experimental crack pattern Pure bending zone
2. Digitally recognized surface strains (DIC results) Loading level n+1 Final loading level Loading level n
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The Agglomerative Hierarchical Clustering Technique was used for identifying coordinates of the cracking points (projections) that closely resemble one another. The clusters, related with coordinates of the cracks, were formed on the basis of having a shortest Euclidean distance between the elements using a corresponding linkage function. The 20 mm distance was chosen as a threshold for comprising the data into the clusters. Figure 1 shows a layout of the proposed technique for determination of the crack distances. Application of this technique to the datasets generated at the different loading levels resulted in a set of coordinates of cracks, which help tracking evolution of distances between the cracks gradually in accordance with increasing load. Figure 2 shows an example of such a diagram. 3. Discussion of the results The cracking depends on material composition, geometry and structural properties. Thus, real cracking behavior of reinforced concrete elements is more complicated than it was observed in the analyzed experimental data. Recent analysis of the experimental data [11] revealed ability of the proposed procedure to be effective for determining the crack distances in the cases a rather complex crack topology. Example of such analysis is presented in Fig. 3. However, determination of the crack distances might be extremely complicated or even hardly possible due to existence of inclined and/or multiple cracks. Furthermore, the DIC technique, requiring multiple-step images for the crack localization, might be significantly limited for a real application. Development of proper filters (increased contrast and reduced noise of the raw images) is of vital importance for reaching versatility of the proposed numerical procedure. Avoiding application of the DIC, such a filtering would allow direct analysis of digital images. Additional analysis must also undertake the crack localization issue. In the current formulation, the crack is associated to the center pixel of the determined “cracked” area. In fact, the crack position should be determined with the help of more elaborate analysis of high resolution images. Adequacy of this procedure must be statistically investigated employing fully-automated processing tools, e.g. [12]. P
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Fig. 3. Test setup and final crack pattern of the beam (left) and variation of the maximal crack distance, sr, max, in the pure bending zone (right)
Arvydas Rimkus et al. / Procedia Engineering 122 (2015) 239 – 243
4. Conclusions The manuscript presents a procedure for crack identification using digital images of reinforced concrete elements. In an automatic manner, the proposed algorithm allows to determine coordinates of cracks and forward resulting data through a numerical procedure of crack spacing determination. The main contribution of this algorithm was realization of an automated crack detection system, which allows eliminating subjective judgment characteristics of the traditional expertise. Analysis of experimental data obtained by the authors reveals ability of the proposed procedure to be effective for processing digital images with a rather complex crack topology. Seeking reliability and versatility of the proposed procedure, further studies must undertake the following means: x Development of proper numerical filters (increased contrast and reduced noise of the raw images) reaching versatility of the proposed numerical procedure by avoiding application of the DIC technique. x Statistical validation of the crack localization procedure processing high resolution images. Acknowledgements The authors gratefully acknowledge the financial support provided by the Research Council of Lithuania (Research Project MIP–050/2014). References [1] H.G. Sohn, Y.M. Lim, K.H. Yun, G.H. Kim, Monitoring crack changes in concrete structures, ComputerǦAided Civil and Infrastructure Engineering 20 (2005) 52ˀ61. [2] T. Yamaguchi, S. Hashimoto, Fast crack detection method for large-size concrete surface images using percolation-based image processing, Machine Vision and Applications 21 (2010) 797–809. [3] O.Kapliński, Information technology in the development of the Polish construction industry, Technological and Economic Development of Economy 15 (2009) 437–452 [4] J.P. Rivera, G. Josipovic, E. Lejeune, B.N. Luna, A.S. Whittaker, Automated Detection and Measurement of Cracks in Reinforced Concrete Components, ACI Structural Journal 112 (2015) 397–406. [5] D. Corr, M. Accardi, L. Graham-Brady, S. Shah, Digital image correlation analysis of interfacial debonding properties and fracture behavior in concrete, Engineering Fracture Mechanics 74 (2007) 109–121. [6] N.A. Hoult, W. Andy Take, C. Lee, M. Dutton, Experimental accuracy of two dimensional strain measurements using Digital Image Correlation, Engineering Structures 46 (2013) 718–726. [7] T.M. Fayyad, J.M. Lees, Application of Digital Image Correlation to Reinforced Concrete Fracture, Procedia Materials Science 3 (2014) 1585–1590. [8] D. Rumšys, D. Bačinskas, V. Gribniak, G. Kaklauskas, R. Ramanauskas, J. Augutis, Short-term deformation analysis of reinforced beams made of lightweight concrete, in: Proc. of the 20th International Conference Mechanika 2015, Kaunas, Lithuania 23-24 April 2015, Technologija, Kaunas, pp. 219–223. [9] L.G. Shapiro, G.C. Stockman, Computer vision, Prentice Hall, Upper Saddle River, NJ, 2001. [10] M. Sonka, V. Hlavac, R. Boyle, Image processing, analysis, and machine vision, fourth ed., Cengage Learning, Stamford, CT, 2015. [11] V. Gribniak, A.P. Caldentey, G. Kaklauskas, A. Rimkus, A. Sokolov, Effect of arrangement of tensile reinforcement on flexural stiffness and cracking, Engineering Structures (in press). [12] V. Gribniak, H.A. Mang, R. Kupliauskas, G. Kaklauskas, Stochastic tension-stiffening approach for the solution of serviceability problems in reinforced concrete: Constitutive modeling, Computer-Aided Civil and Infrastructure Engineering 00 (2015) 1–19, DOI: 10.1111/mice.12133.
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