Crack Identification Method of Steel Fiber Reinforced Concrete Based on Deep Learning: A Comparative Study and Shared Crack Database
As a common disease of concrete structure in engineering, cracks mainly lead to durability problems such as steel corrosion, rain erosion, and protection layer peeling, and then the building gets destroyed. In order to detect the cracks of concrete structure in time, the bending test of steel fiber...
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| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Advances in Materials Science and Engineering |
| Online Access: | http://dx.doi.org/10.1155/2021/9934250 |
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| author | Yang Ding Shuang-Xi Zhou Hai-Qiang Yuan Yuan Pan Jing-Liang Dong Zhong-Ping Wang Tong-Lin Yang An-Ming She |
| author_facet | Yang Ding Shuang-Xi Zhou Hai-Qiang Yuan Yuan Pan Jing-Liang Dong Zhong-Ping Wang Tong-Lin Yang An-Ming She |
| author_sort | Yang Ding |
| collection | DOAJ |
| description | As a common disease of concrete structure in engineering, cracks mainly lead to durability problems such as steel corrosion, rain erosion, and protection layer peeling, and then the building gets destroyed. In order to detect the cracks of concrete structure in time, the bending test of steel fiber reinforced concrete is carried out, and the pictures of concrete cracks are obtained. Furthermore, the crack database is expanded by the migration learning method and the crack database is shared on the Baidu online disk. Finally, a concrete crack identification model based on YOLOv4 and Mask R-CNN is established. In addition, the improved Mask R-CNN method is proposed in order to improve the prediction accuracy based on the Mask R-CNN. The results show that the average prediction accuracy of concrete crack identification is 82.60% based on the YOLO v4 method. The average prediction accuracy of concrete crack identification is 90.44% based on the Mask R-CNN method. The average prediction accuracy of concrete crack identification is 96.09% based on the improved Mask R-CNN method. |
| format | Article |
| id | doaj-art-1ade0327391345a9a0e5d7b618c752a4 |
| institution | Kabale University |
| issn | 1687-8434 1687-8442 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Materials Science and Engineering |
| spelling | doaj-art-1ade0327391345a9a0e5d7b618c752a42025-02-03T01:24:46ZengWileyAdvances in Materials Science and Engineering1687-84341687-84422021-01-01202110.1155/2021/99342509934250Crack Identification Method of Steel Fiber Reinforced Concrete Based on Deep Learning: A Comparative Study and Shared Crack DatabaseYang Ding0Shuang-Xi Zhou1Hai-Qiang Yuan2Yuan Pan3Jing-Liang Dong4Zhong-Ping Wang5Tong-Lin Yang6An-Ming She7Key Laboratory of Advanced Civil Engineering Materials of Ministry of Education, Tongji University, Shanghai 201804, ChinaSchool of Civil Engineering and Architecture, East China Jiao Tong University, Nanchang 330013, ChinaSchool of Civil Engineering and Architecture, East China Jiao Tong University, Nanchang 330013, ChinaSchool of Civil Engineering and Architecture, East China Jiao Tong University, Nanchang 330013, ChinaSchool of Civil Engineering and Architecture, East China Jiao Tong University, Nanchang 330013, ChinaKey Laboratory of Advanced Civil Engineering Materials of Ministry of Education, Tongji University, Shanghai 201804, ChinaCollege of Chemistry and Chemical Engineering, Hunan University, Changsha 410082, ChinaKey Laboratory of Advanced Civil Engineering Materials of Ministry of Education, Tongji University, Shanghai 201804, ChinaAs a common disease of concrete structure in engineering, cracks mainly lead to durability problems such as steel corrosion, rain erosion, and protection layer peeling, and then the building gets destroyed. In order to detect the cracks of concrete structure in time, the bending test of steel fiber reinforced concrete is carried out, and the pictures of concrete cracks are obtained. Furthermore, the crack database is expanded by the migration learning method and the crack database is shared on the Baidu online disk. Finally, a concrete crack identification model based on YOLOv4 and Mask R-CNN is established. In addition, the improved Mask R-CNN method is proposed in order to improve the prediction accuracy based on the Mask R-CNN. The results show that the average prediction accuracy of concrete crack identification is 82.60% based on the YOLO v4 method. The average prediction accuracy of concrete crack identification is 90.44% based on the Mask R-CNN method. The average prediction accuracy of concrete crack identification is 96.09% based on the improved Mask R-CNN method.http://dx.doi.org/10.1155/2021/9934250 |
| spellingShingle | Yang Ding Shuang-Xi Zhou Hai-Qiang Yuan Yuan Pan Jing-Liang Dong Zhong-Ping Wang Tong-Lin Yang An-Ming She Crack Identification Method of Steel Fiber Reinforced Concrete Based on Deep Learning: A Comparative Study and Shared Crack Database Advances in Materials Science and Engineering |
| title | Crack Identification Method of Steel Fiber Reinforced Concrete Based on Deep Learning: A Comparative Study and Shared Crack Database |
| title_full | Crack Identification Method of Steel Fiber Reinforced Concrete Based on Deep Learning: A Comparative Study and Shared Crack Database |
| title_fullStr | Crack Identification Method of Steel Fiber Reinforced Concrete Based on Deep Learning: A Comparative Study and Shared Crack Database |
| title_full_unstemmed | Crack Identification Method of Steel Fiber Reinforced Concrete Based on Deep Learning: A Comparative Study and Shared Crack Database |
| title_short | Crack Identification Method of Steel Fiber Reinforced Concrete Based on Deep Learning: A Comparative Study and Shared Crack Database |
| title_sort | crack identification method of steel fiber reinforced concrete based on deep learning a comparative study and shared crack database |
| url | http://dx.doi.org/10.1155/2021/9934250 |
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