In the engineering community, nondestructive imaging has been widely used for damage identification by capturing anomalies on the surface or inside of structural elements. In this paper, we focus on one of the most common damage types observed in civil engineering, namely, concrete surface cracks. To identify this type of damage, we propose an image-based framework, whereby optical cameras provide the source images. The framework involves several advanced image processing methods, including: (i) the determination of damage occurrence using time-series images, (ii) the localization of damage at each image frame, and (iii) the geometric quantification of damage. Challenges that may arise when images are obtained in the laboratory or field environment are addressed. Two application examples are provided to demonstrate the use and effectiveness of the proposed approach. 1. Introduction In the communities of structural health monitoring (SHM) and nondestructive evaluation (NDE), development of an automated structural damage identification solution has been a key objective. Most SHM methodologies rely on sensing of one-dimensional vibration signals, which are used to extract global modal features as signatures of structural integrity by using system identification-based or statistical pattern recognition-based methods [1, 2]; therefore, the awareness of structural damage is usually based on classifying these indirect signatures. Different from these SHM methodologies, NDE methods, especially when 2- or higher-dimensional imaging methods are employed, are able to provide a direct characterization of local structural damage. Depending on the nature of images, spatial extent and spectral variation of damage usually manifest themselves in the captured images. Subsequently, one can employ photogrammetric or image analysis methods to extract these damage characteristics quantitatively. A variety of imaging technologies have been developed in the NDE community in an effort to detect local structural damage to civil/mechanical systems or components. Widely used imaging devices include infrared thermography, microwave imaging, acoustic imaging, X-ray imaging, and other radiography-based methods . Common types of structural damage encountered in practice include external or internal cracks, voids, delamination, and ablation, to name a few. To detect these different types of damage, the imaging device must often be customized to capture the characteristics associated with the physical damage; hence the resulting equipment may be expensive. In civil engineering,
I. Abdel-Qader, O. Abudayyeh, and M. E. Kelly, “Analysis of edge-detection techniques for crack identification in bridges,” Journal of Computing in Civil Engineering, vol. 17, no. 4, pp. 255–263, 2003.
T. C. Hutchinson, F. Kuester, K.-U. Doerr, and D. Lim, “Optimal hardware and software design of an image-based system for capturing dynamic movements,” IEEE Transactions on Instrumentation and Measurement, vol. 55, no. 1, pp. 164–175, 2006.
T. Zhang and G. Nagy, “Surface tortuosity and its application to analyzing cracks in concrete,” in Proceedings of the 17th International Conference on Pattern Recognition (ICPR '04), vol. 2, pp. 851–854, Cambridge, Mass, USA, August 2004.
Z. Zhu and I. Brilakis, “Detecting air pockets for architectural concrete quality assessment using visual sensing,” Electronic Journal of Information Technology in Construction, vol. 13, pp. 86–102, 2008.
M. Moore, D. Rolander, B. Graybeal, B. Phares, and G. Washer, “Highway bridge inspection: state-of-the-practice survey,” Tech. Rep. FHWA-RD-01-033, Federal Highway Administration, McLean, Va, USA, 2001, http://www.tfhrc.gov/hnr20/nde/pdfs/01033.pdf.
Y. Fujita, Y. Mitani, and Y. Hamamoto, “A method for crack detection on a concrete structure,” in Proceedings of the 18th International Conference on Pattern Recognition (ICPR '06), vol. 3, pp. 901–904, Hong Kong, August 2006.
A. Ito, Y. Aoki, and S. Hashimoto, “Accurate extraction and measurement of fine cracks from concrete block surface image,” in Proceedings of the 28th Annual Conference of the IEEE Industrial Electronics Society, vol. 3, pp. 2202–2207, Seville, Spain, November 2002.
T. Lindeberg, “Edge detection and ridge detection with automatic scale selection,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR '96), pp. 465–470, San Francisco, Calif, USA, June 1996.
T. Soppe and T. Hutchinson, “Experimental damage-gas flow correlations for cyclically loaded reinforced concrete walls,” Tech. Rep. SSRP 09/07, Department of Structural Engineering, University of California, San Diego, Calif, USA, 2009.
Z. Chen, B. Chang, and T. C. Hutchinson, “Image-based monitoring of structural damage: concrete surface cracks,” in Smart Sensor Phenomena, Technology, Networks, and Systems, vol. 6933 of Proceedings of SPIE, San Diego, Calif, USA, March 2008.
P. W. Hallinan, “A low-dimensional representation of human faces for arbitrary lighting conditions,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR '94), pp. 995–999, Seattle, Wash, USA, June 1994.
P. Y. Simard, Y. A. Le Cun, J. S. Denker, and B. Victorri, “Transformation invariance in pattern recognition: tangent distance and propagation,” International Journal of Imaging Systems and Technology, vol. 11, no. 3, pp. 181–197, 2000.
M. S. Hassouna and A. A. Farag, “Robust centerline extraction framework using level sets,” in Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR '05), vol. 1, pp. 458–467, San Diego, Calif, USA, June 2005.
Y. Yitzhaky and E. Peli, “A method for objective edge detection evaluation and detector parameter selection,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 25, no. 8, pp. 1027–1033, 2003.