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2 Construction and Building Materials 28 (2012) Contents lists available at SciVerse ScienceDirect Construction and Building Materials journal homepage: Review Characterisation of concrete cracking during laboratorial tests using image processing J. Valença a,d,, D. Dias-da-Costa b,e, E.N.B.S. Júlio c,d a Department of Civil Engineering, Polytechnic Institute of Coimbra, Rua Pedro Nunes Quinta da Nora, Coimbra, Portugal b Department of Civil Engineering, University of Coimbra, Rua Luís Reis Santos, Coimbra, Portugal c Department of Civil Engineering, Instituto Superior Técnico, Technical University of Lisbon, Av. Rovisco Pais, Lisboa, Portugal d ICIST, Av. Rovisto Pais, Lisboa, Portugal e INESC Coimbra, Rua Antero de Quental 199, Coimbra, Portugal article info abstract Article history: Received 5 May 2011 Received in revised form 24 August 2011 Accepted 29 August 2011 Keywords: Concrete Cracking Laboratorial tests Image processing The evolution of the crack pattern during laboratorial tests is quite important to adequately characterise the specimen s response. Usually this is done by hand drawings. Hence, the development of an automatic tool capable of detecting, mapping and measuring crack patterns would be of upmost importance. The recent developments of digital optical equipment and methods widened their original field of application. Nowadays, laboratorial test monitoring is surely one of these potentially interesting new areas. In this paper, an innovative method named MCRACK is introduced, aiming to automatically characterise cracking using digital image processing. First, MCRACK is tested on a crack width ruler. Afterwards, the method is applied to push-off specimens tested until failure to study its advantages compared to traditional methods. It is concluded that MCRACK gives a considerable increase of data, with higher reliability, automatically processed and at a significantly reduced working time. Ó 2011 Elsevier Ltd. All rights reserved. Contents 1. Introduction Description of the method Case study Test preparation Image acquisition and precision Global Analysis Definition of LROIs and local characterisation Global Reconstitution Conclusions Acknowledgments References Introduction Concrete cracking observed during laboratorial tests is crucial to characterise the specimen s behaviour. Consequently, to accurately detect, map and quantify the evolution of cracking is mandatory. Corresponding author at: Department of Civil Engineering, Polytechnic Institute of Coimbra, Rua Pedro Nunes Quinta da Nora, Coimbra, Portugal. Tel.: ; fax: address: jonatas@dec.uc.pt (J. Valença). However, most existing approaches regarding this parameter are empirical. For instance, the crack pattern is frequently mapped by direct sketch based on visual observation and the cracks width is evaluated by means of a measuring magnifier or by using a crack width ruler. Therefore, most of the traditional methods to characterise and monitor cracking still remain: (i) time-consuming; (ii) subjective; and (iii) subjected to human error. Recently developed algorithms are capable of detecting discontinuities in images by analysing variations of the intensity of pixels. Since cracks localised on a concrete surface can be regarded as discontinuities in an image of the latter, some applications devoted to /$ - see front matter Ó 2011 Elsevier Ltd. All rights reserved. doi: /j.conbuildmat

3 608 J. Valença et al. / Construction and Building Materials 28 (2012) their characterisation can already be found [1 5]. Nevertheless, these only present good results under controlled conditions and, typically, only a single crack, on a single stage, is analysed. Therefore, this inhibits monitoring a complete surface of a specimen and a vast campaign of tests with different crack patterns. In this paper, an innovative method is introduced, MCRACK Image Processing of Cracking in Concrete Surfaces, to automatically detect, map and measure cracking using digital image processing in a fast and reliable way. Push-off specimens tested until failure were used to characterise this new tool and to compare its advantages and drawbacks relatively to traditional methods. This paper is organised as follows. In this first section, the motivation and main goals of the research study undertaken are given and the outline of the paper is presented. In Section 2, a detailed description of the proposed new method is presented. In Section 3, the experimental procedure concerning the push-off specimens (considered as case study) is described. Moreover, the full procedure and the calibration of MCRACK are described. Finally, in Section 4, the most relevant conclusions are drawn. 2. Description of the method An innovative method specially developed for monitoring cracking, combining digital image processing and mathematical morphology (MM) [6], is herein presented. Using a combined global local approach, crack detection and characterisation is significantly enhanced on an entire surface of the specimen. RMS precision (pixels) CWR line thickness (mm) Fig. 3. RMS precision of the measured thickness. The main steps of this new method, MCRACK, are summarised below: the first step includes the preparation of the surface of the specimen which is painted white in order to enhance the appearance of cracks and the placement of crack width rulers (CWR) in order to obtain the precision; images are acquired at step 2 with a low cost digital camera placed orthogonally to the surface of the specimen, in order to minimise the distortion of the image, using a single image approach [7,8]; step 3 uses a Global Region of Interest (GROI), which is a window defined by the user to monitor the specimen s surface. For each stage, a binary image is produced which enhances the discontinuities at the surface of the specimen; (a) (b) (c) (d) Fig. 1. Evaluation of the crack width at each point along the length of the discontinuity. (a) (b) (c) Fig. 2. Push-off specimen: (a) initial stage; (b) intermediate stage; and (c) failure of the specimen.

4 J. Valença et al. / Construction and Building Materials 28 (2012) CWR line thickness (pixels) max min mean Linear (mean) y = x R² = CWR line thickness (mm) % % % % CWR line thickness (mm) -45% -59% Accuracy (mm) Accuracy (%) -73% -86% % Fig. 4. (a) Spatial resolution; and (b) accuracy. Fig. 5. Binary images for initial stage: (a) Canny 1; (b) Canny 2; (c) Otsu; and (d) manual threshold. Fig. 6. Binary images for intermediate stage: (a) Canny 1; (b) Canny 2; (c) Otsu; and (d) manual threshold. Fig. 7. Binary images for final stage: (a) Canny 1; (b) Canny 2; (c) Otsu; and (d) manual threshold.

5 610 J. Valença et al. / Construction and Building Materials 28 (2012) selected algorithm is applied to enhance cracks. This step can be performed using different algorithms (for instance, in Section 3 both Canny and Otsu s algorithms are compared). The post-processing procedures include MM operations to clean/ link discontinuity areas and to fill closed regions [9]. First, groups containing less than ten pixels are removed. Secondly, the gaps inside the discontinuities are filled. When using Canny algorithm, additional operations can be applied to circumvent the high sensibility of the algorithm (constituting what is designated ahead by Canny 2 ). These operations were optimised for the failure stage and are the following: (i) pixels touching a structural element composed by a straight line of 3 pixels, with a progressive varying angle (0, 45, 90 and 135 ), are connected; (ii) pixels which do not fulfil the first condition are removed; and (iii) groups containing less than 500 elements are also removed. Additionally, at step 5, the operations related to crack characterisation are applied. A sequence of three main steps allows evaluating cracks length, width and area (see Fig. 1): (1) localisation of the crack at the surface of the specimen; (2) definition of the boundary edges; and (3) assessment of the cracks length and corresponding opening at each point of the boundary. This procedure automatically characterises the cracks profile (length, width and area) of any selected boundary edges. The length of a given crack is defined by the average length of both edges, whereas the crack s width at each point is defined by the minimum distance from each pixel to the opposite edge (see Fig. 1d). In the next section all steps are thoroughly illustrated by means of the selected case study. 3. Case study Fig. 8. Definition of the LROIs. at step 4, Local Regions of Interest (LROI) are selected by the user after analysing the output of step 2 for the failure stage (this stage is expected to contain the most relevant cracks); step 5 includes a detailed analysis of each LROI according to the user request, allowing to characterise the cracks (length, width, area and profile); finally, at step 6, all used LROIs are reconstituted into a global image containing the complete crack map. Push-off specimens tested until failure (Fig. 2) were selected as case study aiming to validate MCRACK. Sixty-six stages have been acquired during a typical complete test, lasting nearly 30 min. From these, seven stages were considered relevant for monitoring and are herein addressed. First, in Section 3.1, image acquisition conditions and camera calibration are briefly described. In Section 3.2, the precision obtained with MCRACK in evaluating the crack opening is addressed. Then, in the subsequent sections, the different steps of the method presented in Section 2 are applied and examined. At both Global and Local Analysis, respectively steps 3 and 5, different procedures allow to enhance the results. These include: (1) pre-processing; (2) processing; and (3) post-processing. During pre-processing, the contrast between discontinuities and background of the image is improved. This was achieved using a high-pass filter and a filter to adjust the contrast. In the processing phase a 3.1. Test preparation The specimens surface was painted in white to enhance crack identification. Furthermore, two CWR were placed at both sides Fig. 9. Local Analysis of LROI #1.

6 J. Valença et al. / Construction and Building Materials 28 (2012) Fig. 10. Local Analysis of LROI #2. Fig. 11. Local Analysis of LROI #3. of the specimen (Fig. 2a). Each ruler is composed by a set of lines with increased thickness, ranging from 0.1 to 4.0 mm, obtained using a plotter capable of printing lines with minimum thickness of mm and a precision of ±0.2% Image acquisition and precision All images of the specimen were acquired with a Canon 400D at maximum resolution ( pixels), using an 18 mm focal

7 612 J. Valença et al. / Construction and Building Materials 28 (2012) Fig. 12. Local Analysis of LROI #4. Fig. 13. Local Analysis of LROI #5. Width (mm) crack 1 crack 2 crack 3 crack Time (min) (a) (b) Fig. 14. Monitoring of crack mouth opening: (a) selected areas; (b) CMOD. length, placed at 2.50 m orthogonally to the specimen s surface. Protection boards covered natural light sources and assured a homogeneous and diffuse light pattern which was kept unchanged during the test. A tripod was used to guarantee the stability of the camera. At each CWR, a LROI window of pixels was defined to evaluate the average of 100 consecutive longitudinal pixel intensity profiles for the binary image. The thickness of each line is given by the average length of the profile containing zero values (black pixels). It should be highlighted that, due to the resolution of the images, the four thinnest lines (from 0.1 to 0.4 mm) cannot be detected. A total of two readings for each width are obtained, one for each CWR, per analysed stage. Since seven stages are considered, the 14

8 J. Valença et al. / Construction and Building Materials 28 (2012) Area / Length area length width 2 1 Width Time (min) (a) (b) Fig. 15. (a) Monitored area, length and CMOD of crack 1; (b) Profile of crack 1 at each stage. Fig. 16. Crack map depicted from the Global Reconstitution: (a) initial stage; (b) intermediate stage; (c) final stage. values obtained constitute a measure of precision. Therefore, the average and the total RMS precision are, respectively, 0.25 and 0.33 pixel (Fig. 3). Precision was kept unchanged regardless considering only the left or the right placed CWR. The widths represented on the ruler have known values and could therefore be used to evaluate the accuracy of the method. The corresponding spatial resolution of the project is mm/ pixel (0.231 mm/pixel on average with a standard deviation of mm/pixel). Fig. 4a contains the measured widths vs. known values, whereas Fig. 4b demonstrates that the accuracy is always less than 10%, decreasing to 4% with increasing thickness. Nevertheless, the method exhibits difficulties in separating the thickness of consecutive lines (Fig. 4a). Since the linear regression of Fig. 4a has a coefficient of correlation near 1.0, denoting the good correlation between measured and known values, the spatial resolution is hereafter evaluated by this trend line. Considering the known values for the CWR lines as ground truth, an absolute average and a total RMS accuracy of mm and mm, respectively, is achieved Global Analysis The Global Analysis step, presented in Section 2, is applied to the complete surface of the specimen, identified as the GROI. A reduced number of stages can be considered since the LROI can be almost exclusively defined by the final stage, where the entire crack map is depicted. Nevertheless, three stages are chosen in order to additionally compare the capabilities of the processing algorithms: (1) the initial stage, just before applying the load; (2) an intermediate stage, 11 min after starting the test; and (3) the failure stage, corresponding to the end of the test, immediately before unloading. Three independent processing algorithms are selected for comparison: (1) Canny edge detector [10, 11]; (2) image binarisation applying Otsu s threshold method [12]; and (3) a manual threshold defined to remove circle marks on the specimen s surface. These were used for a photogrammetric study which is out of the scope of this manuscript [13]. The algorithms are identically applied to the GROI, followed by post-processing composed by cleaning and filling bounded regions (MM procedure). The resulting cracks maps are depicted in Figs. 5 7.

9 614 J. Valença et al. / Construction and Building Materials 28 (2012) From the visual analysis of results, the following main statements can be made: Canny 1 algorithm detects almost all discontinuities at the surface of the specimen. This is a drawback since surface irregularities or even a simple colour fluctuation can be mistaken with cracks (see Figs. 5a 7a); the MM operations which compose Canny 2 algorithm, described in Section 2, were optimised for the failure stage. Therefore, results for other stages may not be adequate (compare Figs. 5b and 6b with Fig. 7b). Thus, the algorithm cannot be easily automatized; the Otsu s algorithm is the most stable one, performing well at all stages (see Figs. 5c 7c); the manually defined threshold is able to eliminate the circular targets but detail is lost concerning mapping of cracks (compare noncircular discontinuities in Fig. 7c d) Definition of LROIs and local characterisation Due to the conclusions drawn in the previous section, Canny 1 is removed from the subsequent analysis. Therefore, only the other three algorithms are applied to identify the LROIs at the failure stage by the user. The five selected LROIs containing the most relevant cracks are represented in Fig. 8. Fig. 17. Crack map: (a) Global crack map; (b) detail of LROI#1 from Global Analysis; (c) detail of LROI#1 from Local Analysis; (d) detail of LROI#2 from Global Analysis; (e) detail of LROI#1 from Local Analysis. Fig. 18. Crack maps defined from a sketch by two different users.

10 J. Valença et al. / Construction and Building Materials 28 (2012) Since the Otsu s algorithm proved to be stable for all stages studied in Section 3.3, keeping simultaneously an adequate sensibility for detecting cracks, this was selected to perform the Local Analysis at the LROIs, for the seven defined stages. The corresponding results are presented in Figs. 9 13, where RGB image, binary image and boundaries are represented. The latter can be used to measure opening, length and area of the discontinuity, according to the procedure introduced in the second to last paragraph of Section 2 and Fig. 1. From the LROIs represented, it is possible to monitor the evolution of the width of any identified crack. Fig. 14a shows the location of five selected cracks. For each one, the crack mouth opening displacement (CMOD) is monitored (Fig. 14b). Relevant information is provided by comparison between Figs. 14b and 9 13: (i) crack 1 is the main crack until stage 3 is reached; (ii) afterwards, crack 2 becomes the active crack, presenting a significant opening after this stage, whereas crack 1 slightly closes at stage 5; (iii) the CMOD of the remaining cracks increases gradually until failure, although at a smaller rate than crack 2; (iv) finally, at the last stage, the CMOD of all cracks decreases due to unloading. The profile (length, area and CMOD) of any selected crack can also be automatically monitored. Fig. 15 presents, as an example, the profile of crack 1. It is observed that all monitored parameters reach their maximum value approximately 17 min after starting the test, followed by a gradual decrease. This is again justified by the presence of crack 2, which becomes the active crack, leading crack 1 to close Global Reconstitution Fig. 16 contains the crack map, for three stages, obtained from the Global Reconstitution. This is depicted by merging each LROI (see Section 3.4) with the remaining area of the GROI (see Section 3.3). Comparison between boundaries defined in red in the Global Reconstitution, with black edges defined in the Global Analysis (see Section 3.3) is presented in Fig. 17. The detail presented in Fig. 17b e, allow to conclude that crack map obtained from Global Reconstitution is sharper. This is an advantage for crack characterisation. Nevertheless, it is stressed out that the crack map obtained from the Global Analysis is already satisfactory. Therefore, Local Analysis and Global Reconstitution steps can be disregarded if only a crack map is needed and not crack characterisation. Fig. 18 contains the crack pattern obtained by two different users in the three considered stages of the test. Comparing Fig. 16 with Fig. 18 leads to the following conclusions: (i) MCRACK and both users obtained similar results in most parts of the crack map; (ii) users tend to assume cracks close to each other as a single crack; this does not happen using MCRACK ; (iii) small differences between results obtained by different users tend to appear in areas where the presence of a crack is not obvious; regarding this issue, MCRACK presents the advantage of omitting dubious discontinuities; (iv) each user needs nearly one hour to perform a detailed sketch for the three stages, whereas MCRACK needs a couple of seconds to perform the same task; and (v) MCRACK can monitor the width of any crack at a given point whereas this task is not feasible with the traditional measuring methods. 4. Conclusions Traditional characterisation methods for surface cracks are still: (i) time-consuming; and (ii) subjected to human error. Usually, crack characterisation is mostly done by close visual observation, being the crack width evaluated by means of magnifiers or crack width rulers. In this paper, a new method is proposed for cracking characterisation and monitoring, named MCRACK, aiming to eliminate the drawbacks of traditional methods. The method was developed to detect, map and measure cracks, being also possible to easily monitor the evolution of cracking with time. The global cracking pattern can be recorded at an unlimited number of pre-defined stages. First, the critical areas are defined by means of a Global Analysis. Afterwards, a localised detailed analysis is performed, followed by a reconstitution of the global surface, which allows obtaining the entire crack pattern and to characterise the evolution of any crack. Relatively to traditional methods, namely sketching the crack pattern, by means of crack magnifiers or crack width rulers, the main advantages of MCRACK are a significant increase in: speed, efficiency, comprehensive information, and reliability, since automatic image processing is applied. The main drawback of the method is related to the selection of regions that has to be performed by the user. MCRACK is a non-contact cost-effective measurement method, since it does not require professional cameras or technical expertise for data processing. At this moment MCRACK is only suitable for laboratorial test monitoring since: (i) the spatial resolution required for large structures, such as bridges and dams, does not allow using low cost commercial cameras; and (ii) surface heterogeneities are often wrongly identified as cracks. Acknowledgments The authors acknowledge the financial support of both the Portuguese Science and Technology Foundation (FCT) and EC+A Projectos Lda, Ph.D. Grant number SFRH/BDE/15660/2007. References [1] Lange J, Benning W, Siering K. Crack detection at concrete construction units from photogrammetric data using image processing procedures. ISPRS commission VII mid-term symposium remote sensing: from pixels to processes, Enschede, Netherlands p [2] Dare P, Hanley H, Fraser C, Ridel B, Niemeier W. An operational application of the automatic feature extraction: The measurement of cracks in concrete structures. Photogramm Rec 2002;17(99): [3] Sinha S, Fieguth P. Segmentation of buried concrete pipe images. Automat Constr 2006;15(1): [4] Yamaguchi T, Nakamura S, Saegusa R, Hashimoto S. Image-based crack detection for real concrete surfaces. IEEJ Trans Electr Electron Eng 2008;3(1): [5] Barazzetti L, Scaioni M. 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