Today, the most commonly used civil infrastructure inspection method is based on a visual assessment conducted by certified inspectors following prescribed protocols. However, the increase in aggressive environmental and load conditions, coupled with the achievement of many structures of the life-cycle end, has highlighted the need to automate damage identification and satisfy the number of structures that need to be inspected. To overcome this challenge, this paper presents a method for automating concrete damage classification using a deep convolutional neural network. The convolutional neural network was designed after an experimental investigation of a wide number of pretrained networks, applying the transfer-learning technique. Training and validation were conducted using a database built with 1352 images balanced between “undamaged”, “cracked”, and “delaminated” concrete surfaces. To increase the network robustness compared to images in real-world situations, different image configurations have been collected from the Internet and on-field bridge inspections. The GoogLeNet model, with the highest validation accuracy of approximately 94%, was selected as the most suitable network for concrete damage classification. The results confirm that the proposed model can correctly classify images from real concrete surfaces of bridges, tunnels, and pavement, resulting in an effective alternative to the current visual inspection techniques.

如今，最常用的民用基础设施检查方法是基于经过认证的检查员按照规定的规程进行的视觉评估。但是，激进的环境和负载条件的增加，以及生命周期末期许多结构的实现，凸显了自动识别损坏并满足需要检查的结构数量的需求。为了克服这一挑战，本文提出了一种使用深度卷积神经网络自动进行混凝土损伤分类的方法。卷积神经网络是在对大量预训练网络进行实验研究之后，采用转移学习技术设计的。使用建立有1352张图片的数据库进行了培训和验证，这些图片在“未损坏”，“开裂的”和“分层的”混凝土表面。与现实情况下的图像相比，为了提高网络的健壮性，已经从Internet和现场桥检查中收集了不同的图像配置。GoogLeNet模型具有约94％的最高验证准确度，被选为最适合混凝土损伤分类的网络。结果证实了所提出的模型可以正确地分类来自桥梁，隧道和人行道的真实混凝土表面的图像，从而可以有效替代当前的视觉检查技术。具有约94％的最高验证准确度的模型被选为最适合混凝土损伤分类的网络。结果证实了所提出的模型可以正确地分类来自桥梁，隧道和人行道的真实混凝土表面的图像，从而可以有效替代当前的视觉检查技术。具有约94％的最高验证准确度的模型被选为最适合混凝土损伤分类的网络。结果证实了所提出的模型可以正确地分类来自桥梁，隧道和人行道的真实混凝土表面的图像，从而可以有效替代当前的视觉检查技术。