Abstract
AbstractThe detection of visible damage (i.e., cracking, concrete spalling and crushing, reinforcement exposure, buckling and fracture) plays a key role in postearthquake safety assessment of reinforced concrete (RC) building structures. In this study, a novel approach based on computer‐vision techniques was developed for pixel‐level multicategory detection of visible seismic damage of RC components. A semantic segmentation database was constructed from test photos of RC structural components. Series of datasets were generated from the constructed database by applying image transformations and data‐balancing techniques at the sample and pixel levels. A deep convolutional network architecture was designed for pixel‐level detection of visible damage. Two sets of parameters were optimized separately, one to detect cracks and the other to detect all other types of damage. A postprocessing technique for crack detection was developed to refine crack boundaries, and thus improve the accuracy of crack characterization. Finally, the proposed vision‐based approach was applied to test photos of a beam‐to‐wall joint specimen. The results demonstrate the accuracy of the vision‐based approach to detect damage, and its high potential to estimate seismic damage states of RC components.
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