Analysis of steel corrosion-induced surface damage evolution of magnesium oxychloride cement concrete through gray-level co-occurrence matrices

doi:10.1002/suco.202000182

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	Analysis of steel corrosion-induced surface damage evolution of magnesium oxychloride cement concrete through gray-level co-occurrence matrices
	Wang, Penghui; Qiao, Hongxia; Li, Yuanke; Feng, Qiong; Chen, Kefan
	2020-10-01
发表期刊	Structural Concrete
ISSN	14644177
卷号	21 期号:5 页码:1905-1918
摘要	In order to get the characteristics of crack formation on a concrete surface, a coupled method of industrial cameras and gray-level co-occurrence matrices (GLCM) is used to analyze the formation of magnesium oxychloride cement concrete surface cracks. This method requires fewer samples than the previous edge detection method, typing analysis, and artificial neural network method and can provide more damage parameter information without being limited to the geometric information of local features. According to the GLCM analysis of the surface crack formation of concrete under the effect of a rust expansion force, it is concluded that the larger the concrete protective layer, the longer the energization time required. When the concrete surface crack reaches 0.2 mm, the test pieces with protective layers of 14, 24, 34, and 44 mm need to be energized for 270, 465, 673, and 792 hr, respectively. With the increase of cracks on the concrete surface, the contrast keeps increasing, while the correlation, energy, and homogeneity gradually decrease. In color heat maps, the number of matrix peaks gradually increases with the formation of cracks, and the bandwidth of the horizontal projection of matrix peaks gradually decreases, and there is a tendency to extend along the main diagonal of the matrix. The selection of the size of the region of interest region will have a certain impact on the size of the four eigenvalues in GLCM, but it will not change its development law. © 2020 fib. International Federation for Structural Concrete
关键词	Cements Damage detection Eigenvalues and eigenfunctions Image segmentation Magnesium compounds Neural networks Steel corrosion Surface defectsArtificial neural network methods Edge detection methods Geometric information Gray-level co-occurrence matrix Horizontal projections Magnesium oxychloride cement Protective layers Region of interest
DOI	10.1002/suco.202000182
收录类别	EI
语种	英语
出版者	Wiley-Blackwell
EI入藏号	20202708897023
EI主题词	Concretes
来源库	Compendex
分类代码	412 Concrete - 412.1 Cement - 539.1 Metals Corrosion - 951 Materials Science
引用统计	被引频次：2[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	https://ir.lut.edu.cn/handle/2XXMBERH/132345
专题	土木工程学院
作者单位	School of Civil Engineering, Lanzhou University of Technology, Lanzhou, China
第一作者单位	土木工程学院
第一作者的第一单位	土木工程学院
推荐引用方式 GB/T 7714	Wang, Penghui,Qiao, Hongxia,Li, Yuanke,et al. Analysis of steel corrosion-induced surface damage evolution of magnesium oxychloride cement concrete through gray-level co-occurrence matrices[J]. Structural Concrete,2020,21(5):1905-1918.
APA	Wang, Penghui,Qiao, Hongxia,Li, Yuanke,Feng, Qiong,&Chen, Kefan.(2020).Analysis of steel corrosion-induced surface damage evolution of magnesium oxychloride cement concrete through gray-level co-occurrence matrices.Structural Concrete,21(5),1905-1918.
MLA	Wang, Penghui,et al."Analysis of steel corrosion-induced surface damage evolution of magnesium oxychloride cement concrete through gray-level co-occurrence matrices".Structural Concrete 21.5(2020):1905-1918.