A nonlinear method for determining two-dimensional joint roughness coefficient based on statistical parameters

WANG Chang-shuo; WANG Liang-qing; GE Yun-feng; LIANG Ye; SUN Zi-hao; DONG Man-man; ZHANG Nan

doi:10.16285/j.rsm.2017.02.033

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A nonlinear method for determining two-dimensional joint roughness coefficient based on statistical parameters

更新时间：2026-06-25

- A nonlinear method for determining two-dimensional joint roughness coefficient based on statistical parameters
- Vol. 38, Issue 2, Pages: 565-573(2017)
- 作者机构：
  
  中国地质大学（武汉）工程学院,湖北,武汉,430074
- 作者简介：
- 基金信息：
- DOI：10.16285/j.rsm.2017.02.033
  CLC： TU457
- Online First：05 June 2018，
  
  Published：2017
- 稿件说明：
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WANG Chang-shuo, WANG Liang-qing, GE Yun-feng, et al. A nonlinear method for determining two-dimensional joint roughness coefficient based on statistical parameters[J]. 2017, 38(2): 565-573.
DOI：

WANG Chang-shuo, WANG Liang-qing, GE Yun-feng, et al. A nonlinear method for determining two-dimensional joint roughness coefficient based on statistical parameters[J]. 2017, 38(2): 565-573. DOI： 10.16285/j.rsm.2017.02.033.

摘要

岩体节理粗糙度系数JRC与其统计参数之间具有复杂的非线性关系，单一统计参数因存在对结构面形貌描述的片面性，从而导致JRC计算结果的可靠性较低。从结构面起伏角、起伏高度及其分布特征的角度选取了平均起伏角、平均相对起伏度、起伏角标准偏差和起伏高度标准偏差 4个参数共同反映结构面形貌。以已知JRC试验反算值的102条结构面剖面线作为样本数据对支持向量机进行训练，构建JRC与所选取的统计参数之间的非线性映射关系，建立了JRC支持向量回归（SVR）预测模型，并通过Barton标准剖面线的JRC预测值与试验反算值的对比证明了模型的可靠性。以三峡库区秭归县马家沟滑坡所处地层岩体结构面为例，基于三维激光扫描试验获取了其表面形貌数据并建立了三维形貌模型，开展室内直剪试验反算得到了其JRC。实例JRC的计算结果表明，SVR模型预测结果与试验反算值的相对误差仅为4.5%，不同统计参数回归关系式对于相同剖面线的估算结果存在较大差异，表明基于所选取的统计参数，采用SVR模型预测得到的JRC更加可靠。该方法为JRC的定量确定提供了新思路。

Abstract

There is a complex nonlinear relationship between rock joint roughness coefficient (JRC) and the statistical parameters. And JRC value calculated using single statistical parameter is not reliable

since the description is one-sided on the morphology of discontinuity surface. Four parameters

average inclination angle

average relative amplitude

standard deviation of inclination angle and standard deviation of amplitude

are selected to describe the morphology of discontinuity surface. The training samples containing 102 profiles with available experimental back-calculated JRC values are chosen to establish the nonlinear relationship between JRC and the selected statistical parameters

then a JRC support vector regression (SVR) prediction model is established. The SVR model is proved to be reliable by comparing the predicted and experimental back-calculated JRC of Barton standard profiles. The discontinuities of Majiagou landslide in Zigui county

Three Gorges Reservoir Region are selected as a case. Three-dimensional laser scanning test is conducted to obtain the morphology data of discontinuity surface and a 3D model of discontinuity surface morphology is developed. Direct shear test is carried out to back calculate its JRC. The results show that JRC predicted by the SVR model and experimental back-calculated value have good consistency

and the relative error is only 4.5%. The JRC estimation results by different regression equations based on statistical parameters for the same profile have larger variation

which indicates that the JRC predicted by SVR model based on the chosen statistical parameters is more reliable. The results may also provide a new approach to quantitatively determine JRC.

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