Visual inspection has been the most widely used technique for monitoring concrete structures in service. Inspectors visually evaluate defects based on experience, skill, and engineering judgment. However, this process is subjective, laborious, time-consuming, and hampered by demanding access to numerous parts of complex structures. Accordingly, the present study proposes a nearly automated inspection model based on image processing and deep learning for detecting defects in typically inaccessible areas of concrete structures. Results indicate that using the Keras classifier combined with Otsu image processing can achieve superior classification accuracy of 97.63%, 96.5%, and 96.17% for training, validation, and testing data, respectively, along with low quantification error of 1.5%, 5% and 2% for the crack length, width, and angle of orientation, respectively. The type of structural damage and its severity are identified based on the allowed range of concrete crack width for different structures, including buildings and bridges based on different international standards and codes. The proposed method can deploy unmanned aerial vehicle image acquisition to offer a nearly automated inspection platform for the colossal backlog of aging concrete structures.

外观检查已成为监视使用中的混凝土结构的最广泛使用的技术。检验人员根据经验，技能和工程判断来直观地评估缺陷。但是，该过程是主观的，费力的，耗时的，并且由于要求访问复杂结构的许多部分而受到阻碍。因此，本研究提出了一种基于图像处理和深度学习的近乎自动化的检测模型，用于检测混凝土结构通常无法进入的区域中的缺陷。结果表明，结合使用Keras分类器和Otsu图像处理，可以分别对训练，验证和测试数据实现97.63％，96.5％和96.17％的出色分类准确度，以及1.5％，5％和5％的低量化误差。裂纹长度，宽度和取向角为2％，分别。结构损伤的类型及其严重程度是根据不同结构的混凝土裂缝宽度的允许范围来确定的，包括基于不同国际标准和规范的建筑物和桥梁。所提出的方法可以部署无人机图像采集，以为老化混凝土结构的巨大积压提供几乎自动化的检查平台。