Autonomous Detection of Concrete Cracks on Bridge Decks and Fatigue Cracks on Steel Members
Conference: Publication Date: 26 June 2017Testing Method:
Crack detection and measurement are among the most common and most important tasks in Structural Health Monitoring (SHM). The current practice of crack detection relies mostly on visual inspection which is labor intensive, time-consuming, and biased by the inspector skill and experience. One growing field of SHM is inspecting infrastructure using only visual cameras and automated damage detection algorithms. In this paper, two image-processing techniques were developed to locate surface cracks in concrete bridge decks and steel structures using Laplacian of Gaussian (LoG) edge detector. One set of images from the surface of several bridge decks and pavements was collected and analyzed by the developed autonomous Concrete Crack Detection (CCD) algorithm. The accuracy of the CCD algorithm was 92%, with a run-time of 0.9-second per image. An autonomous Fatigue Crack Detection (FCD) algorithm was also developed and applied on images taken from two test-pieces with fatigue cracks. The FCD algorithm was able to locate the fatigue cracks by detecting 85% and 69% of the first and second crack’s length, respectively. The promising results of the CCD and the FCD algorithms indicate their potentials for SHM and condition assessment of infrastructure in a faster, less dangerous, less biased manner.
- Markow, M. J. (2011). Determining Highway Maintenance Costs (Vol. 688). Transportation Research Board.
- Bridges. (2017). ASCE's 2017 Infrastructure Report Card. Retrieved 26 April 2017, from http://www.infrastructurereportcard.org/cat-item/bridges/.
- FHWA (2013), Bridges by Year Built, Year Reconstructed and Material Type 2013 - Bridge Tables - National Bridge Inventory - Bridge Inspection - Safety - Bridges & Structures - Federal Highway Administration. (2017). Fhwa.dot.gov. Retrieved 26 April 2017, from https://www.fhwa.dot.gov/bridge/nbi/no10/yrblt_yrreconst13.cfm#a
- FHWA (2015) Bridge Replacement Unit Costs 2015 - Bridge Tables - National Bridge Inventory - Bridge Inspection - Safety - Bridges & Structures - Federal Highway Administration. (2017). Fhwa.dot.gov. Retrieved 26 April 2017, from https://www.fhwa.dot.gov/bridge/nbi/sd2015.cfm.
- Lee, S., and N. Kalos (2014). Non-destructive testing methods in the US for bridge inspection and maintenance. KSCE Journal of Civil Engineering, 18(5), 1322-1331.
- Dorafshan, S., Maguire, M., Hoffer, N., and Coopmans, C., Challenges in Bridge Inspection Using Small Unmanned Aerial Systems: Results and Lessons Learned, The 2017 International Conference on Unmanned Aircraft Systems (ICUAS’ 17), June 13-16, 2017, Miami, FL.
- Dorafshan S., Maguire, M., and Chang, M., (2017). Comparing Automated Image-Based Crack Detectio Techniques in Spatial and Frequency Domains, 26th ASNT Research Symposium, Jacksonville, Florida
- Pines, D., & Aktan, A. E. (2002). "Status of structural health monitoring of long-span bridges in the United States," Progress in Structural Engineering and materials, 4(4), 372-380.
- Dorafshan, S. Maguire, M., Qi, Xi., (2016). Automatic Surface Crack Detection in Concrete Structures Using OTSU Thresholding and Morphological Operations (Research Report, Utah State University).
- German, S., Brilakis, I., & DesRoches, R. (2012). Rapid entropy-based detection and properties measurement of concrete spalling with machine vision for post-earthquake safety assessments. Advanced Engineering Informatics, 26(4), 846-858.
- Sohn, H., Dutta, D., Yang, J. Y., DeSimio, M., Olson, S., & Swenson, E. (2011). Automated detection of delamination and disbond from wavefield images obtained using a scanning laser vibrometer. Smart Materials and Structures, 20(4), 045017.
- Ellenberg, A., Kontsos, A., Moon, F., & Bartoli, I. (2016). Bridge related damage quantification using unmanned aerial vehicle imagery. Structural Control and Health Monitoring.
- Jahanshahi, M. R., Kelly, J. S., Masri, S. F., & Sukhatme, G. S. (2009). A survey and evaluation of promising approaches for automatic image-based defect detection of bridge structures. Structure and Infrastructure Engineering, 5(6), 455-486.
- Jähne, B. (2004). Practical handbook on image processing for scientific and technical applications.
- Gonzalez, R. C., & Woods, R. E. (2007). Image processing. Digital image processing, 3.
- Hopper, T., Manafpour, A., Radlinska, A., Warn, G., Rajabipour, F., Morian, D., & Jahangirnejad, S. (2015). Bridge Deck Cracking: Effects on In-Service Performance, Prevention, and Remediation. Final Report, Aug. 5th.
- Abdel-Qader, I., Abudayyeh, O., & Kelly, M. E. (2003). Analysis of edge-detection techniques for crack identification in bridges. Journal of Computing in Civil Engineering, 17(4), 255-263.
- Abdel-Qader, I., Pashaie-Rad, S., Abudayyeh, O., & Yehia, S. (2006). PCA-based algorithm for unsupervised bridge crack detection. Advances in Engineering Software, 37(12), 771-778.
- Hutchinson, T. C., & Chen, Z. (2006). Improved image analysis for evaluating concrete damage. Journal of Computing in Civil Engineering, 20(3), 210-216.
- Fujita, Y., Mitani, Y., & Hamamoto, Y. (2006, August). A method for crack detection on a concrete structure. In Pattern Recognition, 2006. ICPR 2006. 18th International Conference on (Vol. 3, pp. 901-904). IEEE.
- Yamaguchi, T., Nakamura, S., Saegusa, R., & Hashimoto, S. (2008). Image-Based Crack Detection for Real Concrete Surfaces. IEEJ Transactions on Electrical and Electronic Engineering, 3(1), 128-135.
- Yamaguchi, T., & 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.
- Moon, H., & Kim, J. (2011). Intelligent crack detecting algorithm on the concrete crack image using neural network. Proceedings of the 28th ISARC, 1461-1467.
- Nishikawa, T., Yoshida, J., Sugiyama, T., & Fujino, Y. (2012). Concrete crack detection by multiple sequential image filtering. Computer-Aided Civil and Infrastructure Engineering, 27(1), 29-47.
- Kim, J. W., Kim, S. B., Park, J. C., & Nam, J. W. (2015). Development of Crack Detection System with Unmanned Aerial Vehicles and Digital Image Processing. Advances in structural engineering and mechanics (ASEM15).
- Rimkus, A., Podviezko, A., & Gribniak, V. (2015). Processing digital images for crack localization in reinforced concrete members. Procedia Engineering, 122, 239-243.
- 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.
- Bennett, J. A., & Mindlin, H. (1973). Metallurgical aspects of the failure of the Point Pleasant Bridge. Journal of Testing and Evaluation, 1(2), 152-161.
- Ryu, D. H., Choi, T. W., Kim, Y. I., & Nahm, S. H. (2000). Measurement of the fatigue-crack using image processing techniques. In Knowledge-Based Intelligent Engineering Systems and Allied Technologies, 2000. Proceedings. Fourth International Conference on (Vol. 1, pp. 121-124). IEEE.
- Gao, H. L., Shen, S. S., & Yun, Y. (2012). Fatigue Crack Length Real Time Measurement Method Based on Camera Automatically Tracking and Positioning. In Applied Mechanics and Materials (Vol. 130, pp. 3111-3118). Trans Tech Publications.
- Dorafshan, S., Maguire, M., (2017). UAV bridge inspection. Idaho Transportation Department, Final Report.
240 Page Views
0 PDF Downloads
0 Facebook Shares