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结构面粗糙度系数分形评价与采样间距的关联性研究    

Study on the Correlation Between Fractal Evaluation of Roughness Cofeeicient and Sampling Interval

文献类型:期刊文献

中文题名:结构面粗糙度系数分形评价与采样间距的关联性研究

英文题名:Study on the Correlation Between Fractal Evaluation of Roughness Cofeeicient and Sampling Interval

作者:马文会[1];黄曼[1,2];马成荣[1];徐常森[1]

机构:[1]绍兴文理学院岩土工程系;[2]同济大学地下建筑与工程系

年份:2017

卷号:33

期号:12

起止页码:30

中文期刊名:科技通报

外文期刊名:Bulletin of Science and Technology

收录:北大核心2014、北大核心

基金:国家自然科学基金(No.41572299;No.41427802;No.41302257;No.41472248);浙江省自然科学基金(No.LZ13D020001;No.LQ13D020001)

语种:中文

中文关键词:结构面;分形维数;采样间距

外文关键词:joint surface;fractal dimension;sampling interval

中文摘要:关于二维结构面粗糙度系数的分形维数评价方法,目前比较常用的方法之一是码尺法。由于采样间距r和长度L(r)对二维轮廓曲线的分形维数D值的影响不容忽视。本文以Barton给出的十条标准轮廓曲线为研究对象,首先,分析分形维数D和变异系数CV随采样间距区间的变化规律,得出采样间距区间为0.1~1.2 mm时,D值有较好的稳定性,其变异系数不大于0.05;其次,设置不同采样间距间隔,分析分形维数D值的变化规律进行分析,得出最佳采样间距;最后,提出一种新的与采样间距有关的粗糙度系数JRC的分形维数评价方法。通过与JRC试验反算值结果对比表明该方法可以较为准确地预测结构面粗糙度系数JRC值,具有较强的适用性。

外文摘要:The fractal dimension evaluation method on two-dimensional structural of surface roughness coefficient,one of the commonly used method is the divider's method. The influence of the sampling interval r and length L(r) on the fractal dimension D value of the two-dimensional profile curve can not be neglected. In this paper,ten standard profile given by Barton as the research object. Firstly,analysis of the fractal dimension D and the variation coefficient of CV changes with the sampling interval,when the sampling interval is 0.1~1.2 mm,the D value is stable and their coefficient of variation is not greater than 0.05;secondly,the sampling spacing interval of different sampling variation across the calculation of the fractal dimension of the distance between the value of D the analysis using the optimal,the optimal sampling interval is obtained;Finally,a new sampling interval is proposed to analyze the variation law of the fractal dimension D.Compared with the back calculation results of JRC test,it is shown that the method can predict the roughness coefficient JRC of the structure surface more accurately and has better applicability.

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