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Crack Inspection and Feature Quantitative Identification of Hybrid-Fibers Engineered Cementitious Composites Based on Deep Learning
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Xiaodan TENG1, 2, 3, Jianming GUO4, Huihuang SUN1
Journal of the Chinese Ceramic Society | 2023, 51(5) : 1323 - 1331
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Journal of the Chinese Ceramic Society | 2023, 51(5): 1323-1331
• Research Articles •
Crack Inspection and Feature Quantitative Identification of Hybrid-Fibers Engineered Cementitious Composites Based on Deep Learning
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Xiaodan TENG1, 2, 3, Jianming GUO4, Huihuang SUN1
Affiliations
  • 1.School of Civil Engineering and Architecture, Guangxi University, Nanning 530004, China
  • 2.Key Laboratory of Disaster Prevention and Structural Safety of Ministry of Education, Guangxi University, Nanning 530004, China
  • 3.Guangxi Key Laboratory of Disaster Prevention and Engineering Safety, Guangxi University, Nanning 530004, China
  • 4.College of Film, Television and Media, Guangxi Arts University, Nanning 530004, China

    • TENG Xiaodan (1984-), female, Ph.D., Lecturer. E-mail:

About Author:

TENG Xiaodan (1984-), female, Ph.D., Lecturer. E-mail:

Published: 2023-05-20 doi: 10.14062/j.issn.0454-5648.20220573
Outline
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It is important for the accurate identification of cracks of engineered cementitious composites (ECC) to investigate the mechanical properties and durability of ECC. To solve the problems like the large number and density of ECC cracks and heavy noise interference, this study adopted the U-NET model suitable for biological image recognition, and optimized part of ResNet network layer structure based on the deep learning method. This study also used the neural network model and combined with the created data suitable for the ECC, and performed the semantic segmentation to obtain the crack pixels. For crack parameter extraction, this study used the bone extraction method and combined with digital image processing process, and used the CLAHE filter and half-peak full-width concept to obtain the crack width. The crack identification and parameter extraction method was applied to detect the cracks on hybrid-fiber ECC dog bone specimen and ECC link slab. The results show that the error range between the ECC crack identification and intelligent detection established by the deep learning method and the actual manual measurement is within 0.6 mm. The results of this study can provide an accurate, effective and high-throughput analysis for the ECC crack inspection and feature quantitative identification.

high ductility engineered cementitious composites  /  crack  /  deep learning  /  tensile  /  hybrid fiber
滕晓丹, 郭健鸣, 孙辉煌. 基于深度学习的高延性水泥基复合材料单轴拉伸裂缝检查与特征定量化识别. 硅酸盐学报, 2023 , 51 (5) : 1323 -1331 . DOI: 10.14062/j.issn.0454-5648.20220573
Xiaodan TENG, Jianming GUO, Huihuang SUN. Crack Inspection and Feature Quantitative Identification of Hybrid-Fibers Engineered Cementitious Composites Based on Deep Learning[J]. Journal of the Chinese Ceramic Society, 2023 , 51 (5) : 1323 -1331 . DOI: 10.14062/j.issn.0454-5648.20220573
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Year 2023 volume 51 Issue 5
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Article Info
doi: 10.14062/j.issn.0454-5648.20220573
  • Receive Date：2022-07-13
  • Online Date：2026-10-20
  • Published：2023-05-20
Article Data
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History
  • Received：2022-07-13
  • Revised：2022-09-28
Funding
Affiliations
    1.School of Civil Engineering and Architecture, Guangxi University, Nanning 530004, China
    2.Key Laboratory of Disaster Prevention and Structural Safety of Ministry of Education, Guangxi University, Nanning 530004, China
    3.Guangxi Key Laboratory of Disaster Prevention and Engineering Safety, Guangxi University, Nanning 530004, China
    4.College of Film, Television and Media, Guangxi Arts University, Nanning 530004, China

Corresponding:

GUO Jianming (1986-), male, Master, Lecturer. E-mail:
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表12种不同金属材料的力学参数
科
Family		 属数
Number of
genus		 种数
Number of
species		 占总种数比例
Percentage of
total species (%)		 属
Genus		 种数
Number of
species		 占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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