Article Article
Rapid Bridge Deck Damage Identification from Unmanned Aerial System Imagery

The application of Unmanned Aerial Systems (UAS) will likely influence the civil engineering industry in the near future. UAS can quickly collect image data, which can be used to identify structural damage. In particular, bridge decks are of interest because they provide a driving surface for vehicles and protect bridges from the environment. Damage on a bridge deck can take the form of delaminations and surface cracks which can be a good indication of deck deterioration. Delaminations can be identified by their different heat transfer characteristics using an infrared camera and surface cracks can be observed with high resolution color images. Image processing techniques and computer vision algorithms can be applied to detect areas of interest from UAS imagery as well as to direct inspectors to targeted locations for further investigation using other contact techniques such as impact echo or chain drag. To demonstrate such capabilities, a simulated bridge deck with internal and external manufactured defects was used in this study. The locations of the defects were unknown to the authors before the flight. A post processing algorithm was developed to leverage both color and infrared aerial images and display global information of local simulated damage.

References

[1] N. Gucunski, S. Kee, H. La, B. Basily, A. Maher, and H. Ghasemi, "Implementation of a Fully Autonomous Platform for Assessment of Concrete Bridge Decks RABIT," in Structures Congress 2015, 2015.

[2] K. Vaghefi, R. C. Oats, D. K. Harris, T. M. Ahlborn, C. N. Brooks, K. A. Endsley, et al. , "Evaluation of commercially available remote sensors for highway bridge condition assessment," Journal of Bridge Engineering,  vol. 17, pp. 886-895, 2011.

[3] F. Khan, A. Ellenberg, M. Mazzotti, A. Kontsos, F. Moon, A. Pradhan, et al. , "Investigation on Bridge Assessment Using Unmanned Aerial Systems," in Structures Congress 2015 , 2015, pp. 404-413.

[4] F. Khan and I. Bartoli, "Detection of delamination in concrete slabs combining infrared thermography and impact echo techniques: A comparative experimental study," in SPIE Smart Structures and Materials+Nondestructive Evaluation and Health Monitoring , 2015, pp. 94370I-94370I-11.

[5] D. Lattanzi and A. Khaloo, "Extracting Structural Models through Computer Vision," 2015.

[6] B. Guldur, Y. Yan, and J. F. Hajjar, "Condition Assessment of Bridges Using Terrestrial Laser Scanners," in Structures Congress 2015 , 2015, pp. 355-366.

[7] T. Yamaguchi and S. Hashimoto, "Fast crack detection method for large-size concrete surface images using percolation-based image processing," Mach. Vision Appl.,  vol. 21, pp. 797-809, 2010.

[8] I. Abdel-Qader, S. Yohali, O. Abudayyeh, and S. Yehia, "Segmentation of thermal images for nondestructive evaluation of bridge decks," NDT & E International,  vol. 41, pp. 395-405, 2008.

 [9] C. Koch, K. Georgieva, V. Kasireddy, B. Akinci, and P. Fieguth, "A review on computer vision based defect detection and condition assessment of concrete and asphalt civil infrastructure," Adv. Eng. Inform.,  vol. 29, pp. 196-210, 2015.

[10] A. Ellenberg, L. Branco, A. Krick, I. Bartoli, and A. Kontsos, "Use of Unmanned Aerial Vehicle for Quantitative Infrastructure Evaluation," Journal of Infrastructure Systems,  p. 04014054, 2014.

[11] L. Barazzetti and M. Scaioni, "Development and implementation of image-based algorithms for measurement of deformations in material testing," Sensors,  vol. 10, pp. 7469-7495, 2010.

[12] B. Pan, K. M. Qian, H. M. Xie, and A. Asundi, "Two-dimensional digital image correlation for in-plane displacement and strain measurement: a review," Measurement Science & Technology,  vol. 20, Jun 2009.

[13] I. Abdel-Qader, O. Abudayyeh, and M. Kelly, "Analysis of Edge-Detection Techniques for Crack Identification in Bridges," Journal of Computing in Civil Engineering,  vol. 17, pp. 255-263, 2003.

[14] A. Ellenberg, A. Kontsos, F. Moon, and I. Bartoli, "Bridge related damage quantification using unmanned aerial vehicle imagery," Structural Control and Health Monitoring,  2016.

[15] A. Ellenberg, A. Kontsos, I. Bartoli, and A. Pradhan, "Masonry Crack Detection Application of an Unmanned Aerial Vehicle," Computing in Civil and Building Engineering,  2014.

[16] S.-E. Chen, C. Rice, C. Boyle, and E. Hauser, "Small-format aerial photography for highway-bridge monitoring," Journal of Performance of Constructed Facilities,  vol. 25, pp. 105-112, 2011.

[17] M. Matsumoto, K. Mitani, and F. N. Catbas, "NDE for Bridge Assessment using Image Processing and Infrared Thermography 2," 2014.

[18] A. Ellenberg, A. Kontsos, F. Moon, and I. Bartoli, "Low-cost, quantitative assessment of highway bridges through the use of unmanned aerial vehicles," in SPIE Smart Structures and Materials+ Nondestructive Evaluation and Health Monitoring , 2016, pp. 98052D-98052D-10.

[19] MATLAB2015b, ed. Natick, MA: The Mathworks Inc., 2015.

[20] S.-H. Kee, T. Oh, J. S. Popovics, R. W. Arndt, and J. Zhu, "Nondestructive bridge deck testing with aircoupled impact-echo and infrared thermography," Journal of Bridge Engineering,  vol. 17, pp. 928-939, 2011.

[21] R. C. Gonzalez and R. E. Woods, "Digital image processing 3rd edition," ed: Prentice Hall, 2007.

[22] E. H. Adelson, C. H. Anderson, J. R. Bergen, P. J. Burt, and J. M. Ogden, "Pyramid methods in image processing," RCA engineer,  vol. 29, pp. 33-41, 1984.

[23] C. Wu, "VisualSFM: A visual structure from motion system," 2011.

Metrics
Usage Shares
Total Views
171 Page Views
Total Shares
0 Tweets
171
0 PDF Downloads
0
0 Facebook Shares
Total Usage
171