A Modified Fully Convolutional Network for Crack Damage Identification Compared with Conventional Methods
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 vi...
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Modelling and Simulation in Engineering |
| Online Access: | http://dx.doi.org/10.1155/2021/5298882 |
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| author | Meng Meng Kun Zhu Keqin Chen Hang Qu |
| author_facet | Meng Meng Kun Zhu Keqin Chen Hang Qu |
| author_sort | Meng Meng |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-d1dcb83a9cf44400ae6ab9deb7285889 |
| institution | Kabale University |
| issn | 1687-5605 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Modelling and Simulation in Engineering |
| spelling | doaj-art-d1dcb83a9cf44400ae6ab9deb72858892025-02-03T01:04:25ZengWileyModelling and Simulation in Engineering1687-56052021-01-01202110.1155/2021/5298882A Modified Fully Convolutional Network for Crack Damage Identification Compared with Conventional MethodsMeng Meng0Kun Zhu1Keqin Chen2Hang Qu3School of Civil EngineeringSchool of Computer ScienceDepartment of Big Data Management and ApplicationsMedical Imaging CenterLarge-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.http://dx.doi.org/10.1155/2021/5298882 |
| spellingShingle | Meng Meng Kun Zhu Keqin Chen Hang Qu A Modified Fully Convolutional Network for Crack Damage Identification Compared with Conventional Methods Modelling and Simulation in Engineering |
| title | A Modified Fully Convolutional Network for Crack Damage Identification Compared with Conventional Methods |
| title_full | A Modified Fully Convolutional Network for Crack Damage Identification Compared with Conventional Methods |
| title_fullStr | A Modified Fully Convolutional Network for Crack Damage Identification Compared with Conventional Methods |
| title_full_unstemmed | A Modified Fully Convolutional Network for Crack Damage Identification Compared with Conventional Methods |
| title_short | A Modified Fully Convolutional Network for Crack Damage Identification Compared with Conventional Methods |
| title_sort | modified fully convolutional network for crack damage identification compared with conventional methods |
| url | http://dx.doi.org/10.1155/2021/5298882 |
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