Autonomous Detection of Concrete Cracks on Bridge Decks and Fatigue Cracks on Steel Members

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.

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