基于数字图像技术的UHPC-NC界面特征识别及粘结性能研究

doi:10.16552/j.cnki.issn1001-1625.2024.0817

摘要/Abstract

摘要： 为更加准确分析超高性能混凝土-普通混凝土(UHPC-NC)界面特征对粘结强度的影响,本文基于数字图像技术实现对基底表面三维特征信息的定量化表征,研究了不同基底表面粗糙度与粗骨料面积对界面粘结强度的影响。结果表明:基底表面粗糙度和粗骨料面积之间存在较强的相关性,获得的轮廓算术平均偏差R_a和标准偏差S_td与粗骨料面积S_c之间的相关系数分别为0.838和0.855,基底表面粗糙度随着粗骨料面积的增加而增加;基底表面进行高压水射流处理界面组试件的失效荷载相较于光滑界面组试件提升了143.0%~240.0%,同时高压水射流处理界面组试件可以获得53.4%~89.6%整体试件的失效荷载;NC基底表面特征是影响界面粘结强度的关键性因素,基底表面粗糙度特征参数轮廓算术平均偏差R_a、标准偏差S_td和粗骨料面积S_c与界面粘结强度之间呈正相关,相关系数分别为0.935、0.927和0.959,界面粘结强度随着基底表面粗糙度和粗骨料面积的增加而增加。

关键词: UHPC-NC, 数字图像技术, 特征识别, 定量化表征, 粘结强度

Abstract: In order to more accurately analyze the influences of ultra high performance concrete-normal concrete (UHPC-NC) interface features on bonding strength, this paper quantitatively characterized the three-dimensional feature information of the substrate surface based on digital image technology, and studied the influences of different substrate surface roughness and coarse aggregate area on the interface bonding strength. The results indicate that there is a strong correlation between the substrate surface roughness and the area of coarse aggregate, and the correlation coefficients between the contour arithmetic mean deviation R_a and standard deviation S_td of the obtained profiles and the coarse aggregate area S_c are 0.838 and 0.855, respectively. The substrate surface roughness increases with the increase of coarse aggregate area. The failure load of the interface group specimen treated with high-pressure water jet on the substrate surface increases by 143.0% to 240.0% compared to the smooth interface group specimen, while the high-pressure water jetting treatment interface group specimen could obtain 53.4% to 89.6% of the overall specimen failure load. The surface characteristics of NC substrate are key factors affecting the interface bonding strength. The characteristic parameters of the substrate surface roughness, the profile arithmetic mean deviation R_a, standard deviation S_td and coarse aggregate area S_c, are positively correlated with the interface bonding strength, and their correlation coefficients are 0.935, 0.927 and 0.959, respectively. The interface bonding strength increases with the increase of substrate surface roughness and coarse aggregate area.

Key words: UHPC-NC, digital image technology, feature recognition, quantitative characterisation, bonding strength

中图分类号:

TV32

张江江, 孙文, 鲜雪蕾, 李瑞泽, 展淑敏, 王甲泽. 基于数字图像技术的UHPC-NC界面特征识别及粘结性能研究[J]. 硅酸盐通报, 2025, 44(1): 81-89.

ZHANG Jiangjiang, SUN Wen, XIAN Xuelei, LI Ruize, ZHAN Shumin, WANG Jiaze. UHPC-NC Interface Feature Recognition and Bonding Performance Based on Digital Image Technology[J]. BULLETIN OF THE CHINESE CERAMIC SOCIETY, 2025, 44(1): 81-89.

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