Once America’s proudest achievement, many of the nation’s infrastructure systems today are outdated and overwhelmed, requiring continuous investment for expansion, upgrade, and mainte-nance to meet increasing demands. In this context, a main challenge today is to rebuild, maintain, modernize, and expand infrastructure systems under constrained funding. A large part of this effort is the assessment of the current state of structures to determine the actions that are required for preservation or replacement. Visual inspection is the most reliable and widely used method for monitoring a structure; however, it is time-consuming, expensive, and produces qualita-tive results. In contrast, the research presented in this article provides evidence that rapid and remote sensing systems implemented on an unmanned aerial vehicle (UAV) platform could contribute to the next generation of civil infrastruc-ture assessment. These systems can provide access to difficult-to-reach areas of the structure and can carry multiple types of remote-sensing systems such as cameras. Furthermore, image processing and computer vision algorithms have been developed to identify locations of potential damage, such as cracks, delaminations, or corrosion. Within the scope of developing a general strategy for UAV infrastructure assessment, several activities are presented in this article. Specifically, aerial image data was captured with color and infrared cameras and was post processed to identify surface and subsurface damage on a simulated bridge deck with internal discontinuities. Lab scale tests with static images and images captured during flight are presented to explore automated damage identification. In addition, optical metrology was used to calculate deformation of a lab-scale structure under static loading. The information was then compared to a preexisting finite element model of the structure and validated with traditional displacement sensors.
ABAQUS, 6.13, 2013, Dassault Systemes, Velizy-Villacoublay, France.
Abdel-Qader, I., O. Abudayyeh, and M. Kelly, 2003, “Analysis of Edge-Detection Techniques for Crack Identification in Bridges,” Journal of Computing in Civil Engineering, Vol. 17, No. 4, pp. 255–263.
ASCE, 2013, “2013 ASCE’s Report Card for America’s Infrastructure,” ASCE. http://www.infrastructurereportcard.org/
Barazzetti, L., and M. Scaioni, 2010, “Development and Implementation of Image-Based Algorithms for Measurement of Deformations in Material Testing,” Sensors, Vol. 10, No. 8, pp. 7469–7495.
Blaber, J., 2014, “Ncorr Digital Image Correlation Software,” Georgia Institute of Technology.
Blaber, J., B. Adair, and A. Antoniou, 2015, “Ncorr: Open-Source 2D Digital Image Correlation Matlab Software,” Experimental Mechanics, Vol. 55, No. 6, pp. 1105–1122.
Blom, J.D., 2010, “Unmanned Aerial Systems: A Historical Perspective,” Vol. 45, Combat Studies Institute Press, Ft. Leavenworth, KS.
Bouguet, J.-Y., 2013, “Camera Calibration Toolbox for Matlab,” http://www.vision.caltech.edu/bouguetj/calib_doc/index.html #parameters
Dalton, P.A., 2008, “Physical Infrastructure: Challenges and Investment Options for the Nation’s Infrastructure,” Testimony Before the Committee on the Budget and the Committee on Transportation and Infastructure, US House of Representatives, US Government Accountability Office.
Ellenberg, A., A. Kontsos, F. Moon, and I. Bartoli, 2016, “Bridge Deck Delamination Identification from Unmanned Aerial Vehicle Infrared Imagery,” Automation in Construction, Vol. 72, No. 2, pp. 155–165.
Ellenberg, A., A. Kontsos, F. Moon, and I. Bartoli, 2016, “Bridge Related Damage Quantification Using Unmanned Aerial Vehicle Imagery,” Structural Control Health Monitoring, Vol. 23, No. 9, pp. 1168–1179.
Ellenberg, A., A. Kontsos, F. Moon, and I. Bartoli, 2016, “Low-Cost, Quantitative Assessment of Highway Bridges Through the Use of Unmanned Aerial Vehicles,” SPIE Smart Structures and Materials+Nonde-structive Evaluation and Health Monitoring, pp. 98052D–8052D–10.
Ellenberg, A., A. Kontsos, F. Moon, and I. Bartoli, 2016, “Rapid, Prelimi-nary Bridge Deck Damage Identification from Unmanned Aerial System Imagery,” NDE/NDT for Structural Materials Technology for Highways and Bridges, Portland, OR.
Ellenberg, A., L. Branco, A. Krick, I. Bartoli, and A. Kontsos, 2014, “Use of Unmanned Aerial Vehicle for Quantitative Infrastructure Evaluation,” Vol. 21, No. 3, Journal of Infrastructure Systems, p. 04014054.
Eschmann, C., C.-M. Kuo, C.-H. Kuo, and C. Boller, 2013, “High-Resolu-tion Multisensor Infrastructure Inspection with Unmanned Aircraft Systems,” ISPRS-International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Rostock, Germany, Vol. 1, pp. 125–129.
FHWA, 2015, “Deficient Bridges by Highway System 2015,” National Bridge Inventory (NBI), FWHA. http://www.fhwa.dot.gov/bridge/deficient.cfm
Gonzalez, R.C., and R.E. Woods, 2007, Digital Image Processing, 3rd Edition, Pearson Prentice Hall, Upper Saddle River, NJ.
Graybeal, B.A., B.M. Phares, D.D. Rolander, M. Moore, and G. Washer, 2002, “Visual Inspection of Highway Bridges,” Journal of Nondestructive Evaluation, Vol. 21, No. 3, pp. 67–83.
Greenleaf, A.R., 1950, Photographic Optics, Macmillan, New York, NY.
Gucunski, N., S. Kee, H. La, B. Basily, A. Maher, and H. Ghasemi, 2015, “Implementation of a Fully Autonomous Platform for Assessment of Concrete Bridge Decks RABIT,” Proceedings of Structures Congress 2015, Portland, OR.
Hartley, R., and A. Zisserman, 2003, Multiple View Geometry in Computer Vision, Cambridge University Press, New York, NY.
Hausamann, D., W. Zirnig, G. Schreier, and P. Strobl, 2005, “Monitoring of Gas Pipelines: A Civil UAV Application,” Aircraft Engineering and Aero-space Technology, Vol. 77, No. 5, pp. 352–360.
Keane, J.F., and S.S. Carr, 2013, “A Brief History of Early Unmanned Aircraft,” Johns Hopkins APL Technical Digest, Vol. 32, No. 3, pp. 558–571.
Khan, F., and I. Bartoli, 2015, “Detection of Delamination in Concrete Slabs Combining Infrared Thermography and Impact Echo Techniques: A Comparative Experimental Study,” SPIE Smart Structures and Materials+ Nondestructive Evaluation and Health Monitoring, San Diego, CA, pp. 94370I–94370I–11.
Khan, F., M. Bolhassani, A. Kontsos, A. Hamid, and I. Bartoli, 2015, “Modeling and Experimental Implementation of Infrared Thermography on Concrete Masonry Structures,” Infrared Physics & Technology, Vol. 69, pp. 228–237.
Khan, F., A. Ellenberg, M. Mazzotti, A. Kontsos, F. Moon, A. Pradhan, et al., 2015, “Investigation on Bridge Assessment Using Unmanned Aerial Systems,” Proceedings of Structures Congress 2015, Portland, OR, pp. 404–413.
Khan, F., A. Ellenberg, S. Ye, A. E. Aktan, F. Moon, A. Kontsos, et al., 2014, “Multispectral Aerial Imaging for Infrastructure Evaluation,” NDE/NDT for Structural Materials Technology for Highway & Bridges, Washington, DC, pp. 123–130.
Khan, F., S. Rajaram, P.A. Vanniamparambil, M. Bolhassani, A. Hamid, A. Kontsos, et al., 2015, “Multi-Sensing NDT for Damage Assessment of Concrete Masonry Walls,” Structural Control and Health Monitoring, Vol. 22, No. 3, pp. 395–593.
Koch, C., K. Georgieva, V. Kasireddy, B. Akinci, and P. Fieguth, 2015, “A Review on Computer Vision-Based Defect Detection and Condition Assessment of Concrete and Asphalt Civil Infrastructure,” Advanced Engineering Informatics, Vol. 29, No. 2, pp. 196–210.
Luque-Vega, L.F., B. Castillo-Toledo, A. Loukianov, and L.E. Gonzalez-Jimenez, 2014, “Power Line Inspection via An Unmanned Aerial System Based on the Quadrotor Helicopter,” MELECON 2014 17th IEEE Mediterranean Electrotechnical Conference, Beirut, Lebanon, pp. 393–397.
MathWorks, 2016, Release 2016a, The Mathworks Inc., Natick, MA.
Metni, N., and T. Hamel, 2007, “A UAV for Bridge Inspection: Visual Servoing Control Law With Orientation Limits,” Automation in Construc-tion, Vol. 17, No. 1, pp. 3–10.
Oh, T., S.-H. Kee, R. W. Arndt, J. S. Popovics, and J. Zhu, 2012, “Compar-ison of NDT Methods for Assessment of a Concrete Bridge Deck,” Journal of Engineering Mechanics, Vol. 139, pp. 305–314.
Phares, B.M., G.A. Washer, D.D. Rolander, B.A. Graybeal, and M. Moore, 2004, “Routine Highway Bridge Inspection Condition Documentation Accuracy and Reliability,” Journal of Bridge Engineering, Vol. 9, No. 4, pp. 403–413.
Shoup, L., N. Donohue, M. Lang, T. Mejia, S. Barry, D. Goldberg, et al., 2011, “The Fix We’re In For: The State of Our Nation’s Bridges,” Transportation 4 America, Washington, DC.
Vaghefi, K., R.C. Oats, D.K. Harris, T.M. Ahlborn, C.N. Brooks, K.A. Endsley, et al., 2012, “Evaluation of Commercially Available Remote Sensors for Highway Bridge Condition Assessment,” Journal of Bridge Engineering, Vol. 17, No. 6, pp. 886–895.
Vanniamparambil, P.A., R. Carmi, F. Khan, J. Cuadra, I. Bartoli, and A. Kontsos, 2015, “An Active–Passive Acoustics Approach for Bond-Line Condition Monitoring in Aerospace Skin Stiffener Panels,” Aerospace Science and Technology, Vol. 43, pp. 289–300.
Vanniamparambil, P.A., M. Bolhassani, R. Carmi, F. Khan, I. Bartoli, F. L. Moon, et al., 2013, “A Data Fusion Approach for Progressive Damage Quantification in Reinforced Concrete Masonry Walls,” Smart Materials and Structures, Vol. 23, No. 1, p. 015007.
Wu, C., 2011, “VisualSFM: A Visual Structure From Motion System.” http://ccwu.me/vsfm/index.html
Yahyanejad, S., J. Misiorny, and B. Rinner, 2011, “Lens Distortion Correc-tion for Thermal Cameras to Improve Aerial Imaging with Small-Scale UAVs,” 2011 IEEE International Symposium on Robotic and Sensors Environments (ROSE), Montreal, QC, Canada, pp. 231–236.
Yamaguchi, T., and S. Hashimoto, 2010, “Fast Crack Detection Method for Large-Size Concrete Surface Images Using Percolation-based Image Processing,” Machine Vision and Applications, Vol. 21, No. 5, pp. 797–809.
Yukihiro Ito, P.S., 2015, SMARTSENSYS. http://smartsensys.com/
Zou, Q., Y. Cao, Q. Li, Q. Mao, and S. Wang, 2012, “CrackTree: Auto-matic Crack Detection From Pavement Images,” Pattern Recognition Letters, Vol. 33, No. 3, pp. 227–238.
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