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Texture features analysis on micro-structure of paste backfill based on image analysis technology

基于图像识别技术的膏体微观结构纹理特征分析

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Abstract

The strength of cement-based materials, such as mortar, concrete and cement paste backfill (CPB), depends on its microstructures (e.g. pore structure and arrangement of particles and skeleton). Numerous studies on the relationship between strength and pore structure (e.g., pore size and its distribution) were performed, but the micro-morphology characteristics have been rarely concerned. Texture describing the surface properties of the sample is a global feature, which is an effective way to quantify the micro-morphological properties. In statistical analysis, GLCM features and Tamura texture are the most representative methods for characterizing the texture features. The mechanical strength and section image of the backfill sample prepared from three different solid concentrations of paste were obtained by uniaxial compressive strength test and scanning electron microscope, respectively. The texture features of different SEM images were calculated based on image analysis technology, and then the correlation between these parameters and the strength was analyzed. It was proved that the method is effective in the quantitative analysis on the micro-morphology characteristics of CPB. There is a significant correlation between the texture features and the unconfined compressive strength, and the prediction of strength is feasible using texture parameters of the CPB microstructure.

摘要

胶结充填膏体 (CPB)、 砂浆及混凝土等水泥基材料的力学强度取决于其微观结构, 如孔隙数量、 孔径及结构, 颗粒及骨架的排列形态等。 对于该类材料的力学强度与其孔隙结构 (如孔径及其分布) 的关系研究目前已有很多, 但与微观形态特征或纹理特性的相关性研究较少。 纹理是一种反映图像中同质现象的视觉特征, 体现了物体表面共有的内在属性, 包含了物体表面结构组织排列以及它们与周围环境的联系, 是量化微观形态特性的有效方法。 在统计分析中, 灰度共生矩阵 (GLCM) 纹理和 Tamura 纹理是表征纹理特征的最具代表性方法。 本研究以 3 种不同质量浓度膏体制备的充填体试块为样本, 养护至指定龄期后经单轴抗压强度试验获得其力学强度, 再对试块断面进行电镜扫描 (SEM) 获得其微观结构图像; 基于图像识别/分析技术提取 SEM 图像的纹理特征参数, 分析纹理特性与 SEM 图像参数(放大倍数)间的关系, 筛选出有效的 SEM 图像样品; 分析各纹理参数与膏体浓度的相关性, 识别出与膏体浓度呈正相关的纹理参数, 并验证该纹理参数与力学强度存在严格的相关关系。

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Authors and Affiliations

  1. School of Civil and Resource Engineering, University of Science and Technology Beijing, Beijing, 100083, China

    Sheng-hua Yin  (尹升华), Ya-jian Shao  (邵亚建), Ai-xiang Wu  (吴爱祥), Yi-ming Wang  (王贻明) & Zhi-yong Gao  (高志勇)

  2. Key Laboratory of Ministry of Education for Efficient Mining and Safety of Metal Mines (University of Science and Technology Beijing), Beijing, 100083, China

    Sheng-hua Yin  (尹升华), Ya-jian Shao  (邵亚建), Ai-xiang Wu  (吴爱祥), Yi-ming Wang  (王贻明) & Zhi-yong Gao  (高志勇)

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  1. Sheng-hua Yin  (尹升华)
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Correspondence to Ya-jian Shao  (邵亚建).

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Foundation item: Project(51722401) supported by the National Natural Science Foundation for Excellent Young Scholars of China; Project(FRF-TP-18-003C1) supported by the Fundamental Research Funds for the Central Universities, China; Project(51734001) supported by the Key Program of National Natural Science Foundation of China

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Yin, Sh., Shao, Yj., Wu, Ax. et al. Texture features analysis on micro-structure of paste backfill based on image analysis technology. J. Cent. South Univ. 25, 2360–2372 (2018). https://doi.org/10.1007/s11771-018-3920-7

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