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路用集料形貌特征参数分析与性能评价

Morphological Characteristic Parameter Analysis and Performance Evaluation of Road Aggregate
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摘要 沥青路面的长期抗滑性能取决于集料的抗磨光/耗性能,准确快速计算集料的形貌特征十分重要。本文引入一种新型高抗滑路用集料——88#煅烧铝矾土,采用Image-Pro-Plus 6.0(IPP)、IBM SPSS Statistics 26.0(SPSS)等数字图像处理软件对集料形貌特征进行分析评价。同时对不同磨光次数下集料形貌特征参数的变化进行灰熵关联分析,得到各集料形貌特征参数与其磨光值(PSV)的灰熵关联度。结果表明,采用IPP软件能够准确获得各集料的形貌特征参数,并且,相较于传统路用集料,88#煅烧铝矾土具有更加丰富的棱角特性及较高的粗糙度。通过灰熵关联分析得到,分形维数与PSV之间的相关性最显著。 The long-term anti-skid performance of asphalt pavement depends on the skid-resistant and abrasion-resistant performance of aggregates,so it is very important to calculate the morphological characteristics of aggregates accurately and quickly.In the research,88#calcined bauxite,a new type of high skid resistance road aggregate,was introduced.Image-Pro-Plus 6.0(IPP),IBM SPSS Statistics 26.0(SPSS)and other digital image processing softwares were used to analyze and evaluate the morphological characteristics of aggregates.Meanwhile,grey entropy correlation analysis was carried out to obtain the grey entropy correlation degree between the aggregate morphological characteristic parameters and polished stone value(PSV)under different polishing times.The results show that the morphological characteristic parameters of each aggregate can be accurately obtained by IPP software,and compared with traditional aggregates,88#calcined bauxite has richer angular characteristics and higher roughness.According to grey entropy correlation analysis,the correlation between fractal dimension and PSV is the most significant.
作者 王永亮 易江涛 刘悦 WANG Yongliang;YI Jiangtao;LIU Yue(School of Civil Engineering,Chongqing University,Chongqing 400045,China;Qinghai Transportation Holding Group Co.,Ltd.,Xining 810000,China;School of Materials Science and Engineering,Chang’an University,Xi’an 710061,China)
出处 《硅酸盐通报》 CAS 北大核心 2024年第3期1143-1152,共10页 Bulletin of the Chinese Ceramic Society
基金 青海省基础研究计划(2024-ZJ-706)。
关键词 道路工程 路用集料 煅烧铝矾土 Image-Pro-Plus 6.0 形貌特征参数 分形维数 灰熵关联度 road engineering road aggregate calcined bauxite Image-Pro-Plus 6.0 morphological characteristic parameter fractal dimension grey entropy correlation degree
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