• Bulletin of the Chinese Ceramic Society
  • Vol. 43, Issue 7, 2490 (2024)
ZHANG Jiahao1, CHEN Zhengfa1,*, SONG Yan2, and CHEN Zhaoyan3
Author Affiliations
  • 1[in Chinese]
  • 2[in Chinese]
  • 3[in Chinese]
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    DOI: Cite this Article
    ZHANG Jiahao, CHEN Zhengfa, SONG Yan, CHEN Zhaoyan. Morphology Characteristics and Distribution Patterns of Slurry-Modified Recycled Aggregate Evaluated by Machine Learning[J]. Bulletin of the Chinese Ceramic Society, 2024, 43(7): 2490 Copy Citation Text show less

    Abstract

    The defects such as high water absorption, high pressure crushing value, surface microcracks and irregular morphology of recycled aggregate (RA) limit its recycling and utilization. The shell film structure formed on the surface of slurry-modified recycled aggregate ( SRA) not only improves the mechanical properties of RA, but also has a significant impact on morphological characteristics of RA. In order to evaluate the effect of slurry-modified on morphological characteristics of RA, this study used fly ash based polymer slurry to modify recycled concrete aggregates (RCA) and recycled brick aggregates (RBA), and obtained and established datasets of morphological characteristics of RCA and RBA before and after slurry-modified using image processing technology. In addition, the article also combined machine learning techniques to extract key information from the dataset, thereby quantifying the morphological features of RA. After quantitative analysis, it is found that compared with before modification, the distribution range of axial coefficient, angularity, and sphericity of modified RA has been improved to varying degrees. Among them, slurry-modification has the most significant effect on improving angularity. The maximum improvement amplitude of slurry-modified recycled concrete aggregate (SRCA) is 132. 2%, and the maximum improvement amplitude of slurry-modified recycled brick aggregate (SRBA) is 69. 2%.
    ZHANG Jiahao, CHEN Zhengfa, SONG Yan, CHEN Zhaoyan. Morphology Characteristics and Distribution Patterns of Slurry-Modified Recycled Aggregate Evaluated by Machine Learning[J]. Bulletin of the Chinese Ceramic Society, 2024, 43(7): 2490
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