Abstract
Large-scale structural health monitoring and damage detection of concealed underwater structures are always the urgent and state-of-art problems to be solved in the field of civil engineering. With the development of artificial intelligence especially the combination of deep learning and computer vision, greater advantages have been brought to the concrete crack detection based on convolutional neural network (CNN) over the traditional methods. However, these machine learning (ML) methods still have some defects, such as it being inaccurate or not strong, having poor generalization ability, or the accuracy still needs to be improved, and the running speed is slow. In this article, a modified fully convolutional network (FCN) with more robustness and more effectiveness is proposed, which makes it convenient and low cost for long-term structural monitoring and inspection compared with other methods. Meanwhile, to improve the accuracy of recognition and prediction, innovations were conducted in this study as follows. Moreover, differed from the common simple deconvolution, it also includes a subpixel convolution layer, which can greatly reduce the sampling time. Then, the proposed method was verified its practicability with the overall recognition accuracy reaching up to 97.92% and 12% efficiency improvement.
Highlights
With the development of construction industry and building technology, more and more traditional concrete structures have experienced a long service life with the deterioration of material properties, so the structural performance will be influenced by the fatigue damage, which will often affect the normal use of these buildings or structures
The convolutional neural network (CNN) model was selected for the task of interest despite other machine/deep learning methods including the support vector machine (SVM), deep belief network (DBN), and stacked denoising autoencoder (SDAE); the reasons are as follows: SVM is a typical shallow classifier, which
This research concentrates on the method applying convolutional network as well as the computer vision technology to identify or detect the concrete cracks in the architecture
Summary
With the development of construction industry and building technology, more and more traditional concrete structures have experienced a long service life with the deterioration of material properties, so the structural performance will be influenced by the fatigue damage, which will often affect the normal use of these buildings or structures (including bridges, dams, and tunnels). Compared with the classic CNN (sliding window convolution method) proposed by Cha et al [7], which is similar to the improved convolution neural network proposed by Chen et al [8] and U-net method proposed by Liu et al [9], a new modified FCN method is proposed Differed from the former, the latter has some advantages in recognition accuracy and efficiency, besides the data loss during training process is lower than the former. The modified fully convolutional network method proposed in this paper weakens the output size of the spatial kernel through the downsample in the special hourglass structure and increases the output size by using upsample. The performance of the proposed method was validated by a combined dataset composed of published one and the images collected by the authors using the drone with satisfactory results
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