The 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.

中文翻译：

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钢筋混凝土构件可见地震损伤的像素级多类别检测

可见损伤（即开裂，混凝土剥落和压碎，钢筋暴露，屈曲和断裂）的检测在钢筋混凝土（RC）建筑结构的地震后安全评估中起着关键作用。在这项研究中，开发了一种基于计算机视觉技术的新颖方法，用于对RC组件的可见地震破坏进行像素级多类别检测。从钢筋混凝土结构构件的测试照片构建了语义分割数据库。通过在样本和像素级别应用图像转换和数据平衡技术，从构建的数据库生成一系列数据集。设计了一种深度卷积网络体系结构，以对可见损伤进行像素级检测。分别优化了两组参数，一种用于检测裂纹，另一种用于检测所有其他类型的损坏。开发了一种用于裂纹检测的后处理技术，以细化裂纹边界，从而提高了裂纹表征的准确性。最后，将所提出的基于视觉的方法应用于梁到墙的联合试样的测试照片。结果证明了基于视觉的方法来检测损伤的准确性，以及其潜在的估计RC组件地震损伤状态的潜力。