Crack Identification for Bridge Condition Monitoring Combining Graph Attention Networks and Convolutional Neural Networks
Orthotropic steel box girders and steel bridge decks are commonly applied to bridges. Because of the coupling of original defects and alternating forces, fatigue cracks are likely to appear in the structures. In order to ensure the life span of bridges, methods for automatic crack identification are...
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MDPI AG
2025-05-01
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| Series: | Applied Sciences |
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| author | Feiyu Chen Tong Tong Jiadong Hua Chun Cui |
| author_facet | Feiyu Chen Tong Tong Jiadong Hua Chun Cui |
| author_sort | Feiyu Chen |
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| description | Orthotropic steel box girders and steel bridge decks are commonly applied to bridges. Because of the coupling of original defects and alternating forces, fatigue cracks are likely to appear in the structures. In order to ensure the life span of bridges, methods for automatic crack identification are needed. In this paper, we present a novel approach for crack detection and bridge condition monitoring by integrating convolutional neural networks (CNNs) with graph attention networks (GATs). At first, the original large-sized images are divided into small-sized patches, and these patches are input into a CNN architecture to extract features by decreasing dimensions. Then, the output features of the CNN model are considered as nodes of the graph. Considering the spatial relationship among the patches in the original image, the node from the central patch is connected to the nodes from its neighboring patches to constitute a graph structure, which can be input into a GAT model to learn the relationship among the nodes and update the features. Finally, the output features of GAT can judge whether the central patch contains cracks. Forty original large-sized images are cropped into abundant patches for the training of the CNN-GAT model. With the use of a sliding window technique, the trained CNN-GAT model is capable of finding the patches containing cracks in the test images with large sizes. From the test results, the location and the size of the cracks are exhibited, which indicates that the proposed approach is effective for crack identification in bridge structures. |
| format | Article |
| id | doaj-art-0f54745b79a8447e87c22f2adfa36947 |
| institution | OA Journals |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-0f54745b79a8447e87c22f2adfa369472025-08-20T02:33:43ZengMDPI AGApplied Sciences2076-34172025-05-011510545210.3390/app15105452Crack Identification for Bridge Condition Monitoring Combining Graph Attention Networks and Convolutional Neural NetworksFeiyu Chen0Tong Tong1Jiadong Hua2Chun Cui3713 Research Institute of CSSC, Zhengzhou 450015, ChinaSchool of Reliability and Systems Engineering, Beihang University, Xueyuan Road No. 37, Haidian District, Beijing 100191, ChinaSchool of Reliability and Systems Engineering, Beihang University, Xueyuan Road No. 37, Haidian District, Beijing 100191, ChinaSchool of Reliability and Systems Engineering, Beihang University, Xueyuan Road No. 37, Haidian District, Beijing 100191, ChinaOrthotropic steel box girders and steel bridge decks are commonly applied to bridges. Because of the coupling of original defects and alternating forces, fatigue cracks are likely to appear in the structures. In order to ensure the life span of bridges, methods for automatic crack identification are needed. In this paper, we present a novel approach for crack detection and bridge condition monitoring by integrating convolutional neural networks (CNNs) with graph attention networks (GATs). At first, the original large-sized images are divided into small-sized patches, and these patches are input into a CNN architecture to extract features by decreasing dimensions. Then, the output features of the CNN model are considered as nodes of the graph. Considering the spatial relationship among the patches in the original image, the node from the central patch is connected to the nodes from its neighboring patches to constitute a graph structure, which can be input into a GAT model to learn the relationship among the nodes and update the features. Finally, the output features of GAT can judge whether the central patch contains cracks. Forty original large-sized images are cropped into abundant patches for the training of the CNN-GAT model. With the use of a sliding window technique, the trained CNN-GAT model is capable of finding the patches containing cracks in the test images with large sizes. From the test results, the location and the size of the cracks are exhibited, which indicates that the proposed approach is effective for crack identification in bridge structures.https://www.mdpi.com/2076-3417/15/10/5452crack identificationconvolutional neural networksgraph attention networksmachine visionbridge condition monitoring |
| spellingShingle | Feiyu Chen Tong Tong Jiadong Hua Chun Cui Crack Identification for Bridge Condition Monitoring Combining Graph Attention Networks and Convolutional Neural Networks Applied Sciences crack identification convolutional neural networks graph attention networks machine vision bridge condition monitoring |
| title | Crack Identification for Bridge Condition Monitoring Combining Graph Attention Networks and Convolutional Neural Networks |
| title_full | Crack Identification for Bridge Condition Monitoring Combining Graph Attention Networks and Convolutional Neural Networks |
| title_fullStr | Crack Identification for Bridge Condition Monitoring Combining Graph Attention Networks and Convolutional Neural Networks |
| title_full_unstemmed | Crack Identification for Bridge Condition Monitoring Combining Graph Attention Networks and Convolutional Neural Networks |
| title_short | Crack Identification for Bridge Condition Monitoring Combining Graph Attention Networks and Convolutional Neural Networks |
| title_sort | crack identification for bridge condition monitoring combining graph attention networks and convolutional neural networks |
| topic | crack identification convolutional neural networks graph attention networks machine vision bridge condition monitoring |
| url | https://www.mdpi.com/2076-3417/15/10/5452 |
| work_keys_str_mv | AT feiyuchen crackidentificationforbridgeconditionmonitoringcombininggraphattentionnetworksandconvolutionalneuralnetworks AT tongtong crackidentificationforbridgeconditionmonitoringcombininggraphattentionnetworksandconvolutionalneuralnetworks AT jiadonghua crackidentificationforbridgeconditionmonitoringcombininggraphattentionnetworksandconvolutionalneuralnetworks AT chuncui crackidentificationforbridgeconditionmonitoringcombininggraphattentionnetworksandconvolutionalneuralnetworks |