A Modified Fully Convolutional Network for Crack Damage Identification Compared with Conventional Methods

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.

Computer Vision and Image Processing in Structural Health Monitoring: Overview of Recent Applications

“…Siriborvornratanakul et al [93] Deep Learning X Sajedi et al [96] Deep Learning X X Meng et al [99] Deep Learning X Benkhoui et al [ Regarding deep learning approaches, most of the selected studies addressed only binary classification problems, including [88,89,92,93,99,100,102,105,110]. Only a few studies also addressed multi-class classification problems in addition to binary classification, such as [86] (type of damage: four classes), [96] (bridge components: seven classes), [87] (several multi-class subsets), [104] (type of damage: three classes), [107] (several multi-class subsets), and [113] (type of damage: five classes).…”

Computer Vision Based Inspection on Post-Earthquake With UAV Synthetic Dataset

“…In the detection of defects, methods employing, among others, dynamic response of the structure [15] or laser scanning are used [16], but for a long time there has also been a significant increase in the number of works devoted to the use of computer vision, also with regard to earthquakes [17]. In this sub-field, classical methods of computer vision [18] are currently being replaced by methods that derive from machine learning, using convolutional [19] and fully convolutional [20] neural networks, LSTM [21] networks or other techniques combining [22] or improving [23] upon these methods. However, it should be noted that only a few systems have been dedicated to detecting more than a single type of defect.…”

Structural Damage Identification Using Ensemble Deep Convolutional Neural Network Models

“…Wang et al [21] developed a structural damage identification framework based on time-frequency graphs and the marginal spectrum of the signals using CNNs and particle swarm optimization algorithm. Meng et al [22] introduced a modified CNN for long-term structural monitoring using both forward convolution and reverse convolution. Quqa et al [23] utilized image processing techniques and CNNs for crack identification in steel bridges using an image dataset of the welded joints of steel bridges.…”