Advances in joint roughness coefficient (JRC) and its engineering applications

The joint roughness coefficient (JRC), introduced in Barton (1973) represented a new method in rock mechanics and rock engineering to deal with problems related to joint roughness and shear strength estimation. It has the advantages of its simple form, easy estimation, and explicit consideration of scale effects, which make it the most widely accepted parameter for roughness quantification since it was proposed. As a result, JRC has attracted the attention of many scholars who have developed JRC-related methods in many areas, such as geological engineering, multidisciplinary geosciences, mining mineral processing, civil engineering, environmental engineering, and water resources. Because of such a developing trend, an overview of JRC is presented here to provide a clear perspective on the concepts, methods, applications, and trends related to its extensions. This review mainly introduces the origin and connotation of JRC, JRC-related roughness measurement, JRC estimation methods, JRC-based roughness characteristics investigation, JRC-based rock joint property description, JRC's influence on rock mass properties, and JRC-based rock engineering applications. Moreover, the representativeness of the joint samples and the determination of the sampling interval for rock joint roughness measurements are discussed. In the future, the existing JRC-related methods will likely be further improved and extended in rock engineering.

Data-driven estimation of joint roughness coefficient

Joint roughness is one of the most important issues in the hydromechanical behavior of rock mass.Therefore,the joint roughness coefficient(JRC)estimation is of paramount importance in geomechanics engineering applications.Studies show that the application of statistical parameters alone may not produce a sufficiently reliable estimation of the JRC values.Therefore,alternative data-driven methods are proposed to assess the JRC values.In this study,Gaussian process(GP),K-star,random forest(RF),and extreme gradient boosting(XGBoost)models are employed,and their performance and accuracy are compared with those of benchmark regression formula(i.e.Z2,Rp,and SDi)for the JRC estimation.To analyze the models’performance,112 rock joint profile datasets having eight common statistical parameters(R_(ave),R_(max),SD_(h),iave,SD_(i),Z_(2),R_(p),and SF)and one output variable(JRC)are utilized,of which 89 and 23 datasets are used for training and validation of models,respectively.The interpretability of the developed XGBoost model is presented in terms of feature importance ranking,partial dependence plots(PDPs),feature interaction,and local interpretable model-agnostic explanations(LIME)techniques.Analyses of results show that machine learning models demonstrate higher accuracy and precision for estimating JRC values compared with the benchmark empirical equations,indicating the generalization ability of the data-driven models in better estimation accuracy.

On the calibration of a shear stress criterion for rock joints to represent the full stress-strain profile

Conventional numerical solutions developed to describe the geomechanical behavior of rock interfaces subjected to differential load emphasize peak and residual shear strengths.The detailed analysis of preand post-peak shear stress-displacement behavior is central to various time-dependent and dynamic rock mechanic problems such as rockbursts and structural instabilities in highly stressed conditions.The complete stress-displacement surface(CSDS)model was developed to describe analytically the pre-and post-peak behavior of rock interfaces under differential loads.Original formulations of the CSDS model required extensive curve-fitting iterations which limited its practical applicability and transparent integration into engineering tools.The present work proposes modifications to the CSDS model aimed at developing a comprehensive and modern calibration protocol to describe the complete shear stressdisplacement behavior of rock interfaces under differential loads.The proposed update to the CSDS model incorporates the concept of mobilized shear strength to enhance the post-peak formulations.Barton’s concepts of joint roughness coefficient(JRC)and joint compressive strength(JCS)are incorporated to facilitate empirical estimations for peak shear stress and normal closure relations.Triaxial/uniaxial compression test and direct shear test results are used to validate the updated model and exemplify the proposed calibration method.The results illustrate that the revised model successfully predicts the post-peak and complete axial stressestrain and shear stressedisplacement curves for rock joints.

Investigation on the Indeterminate Information of Rock Joint Roughness through a Neutrosophic Number Approach

To better estimate the rock joint shear strength,accurately determining the rock joint roughness coefficient(JRC)is the first step faced by researchers and engineers.However,there are incomplete,imprecise,and indeterminate problems during the process of calculating the JRC.This paper proposed to investigate the indeterminate information of rock joint roughness through a neutrosophic number approach and,based on this information,reported a method to capture the incomplete,uncertain,and imprecise information of the JRC in uncertain environments.The uncertainties in the JRC determination were investigated by the regression correlations based on commonly used statistical parameters,which demonstrated the drawbacks of traditional JRC regression correlations in handling the indeterminate information of the JRC.Moreover,the commonly used statistical parameters cannot reflect the roughness contribution differences of the asperities with various scales,which induces additional indeterminate information.A method based on the neutrosophic number(NN)and spectral analysis was proposed to capture the indeterminate information of the JRC.The proposed method was then applied to determine the JRC values for sandstone joint samples collected from a rock landslide.The comparison between the JRC results obtained by the proposed method and experimental results validated the effectiveness of the NN.Additionally,comparisons made between the spectral analysis and common statistical parameters based on the NN also demonstrated the advantage of spectral analysis.Thus,the NN and spectral analysis combined can effectively handle the indeterminate information in the rock joint roughness.

Measurement of Joint Roughness Coefficient by Using Profilograph and Roughness Ruler

Joint roughness coefficient(JRC) is the key parameter for the empirical estimation of joint shear strength by using the JRC-JCS(joint wall compressive strength) model.Because JRC has such characteristics as nonuniformity,anisotropy,and unhomogeneity,directional statistical measurement of JRC is the precondition for ensuring the reliability of the empirical estimation method.However,the directional statistical measurement of JRC is time-consuming.In order to present an ideal measurement method of JRC,new profilographs and roughness rulers were developed according to the properties of rock joint undulating shape based on the review of measurement methods of JRC.Operation methods of the profilographs and roughness rulers were also introduced.A case study shows that the instruments and operation methods produce an effective means for the statistical measurement of JRC.

JRC修正直边法的数学表达

ＪＲＣ是反映爬坡角力学效应的岩体结构面表面形态等效描述指标。本文在爬坡角力学效应机理研究基础上，阐述了ＪＲＣ直边法的物理意义；通过岩体结构面表面起伏幅度与金属表面粗糙度幅度对比分析，肯定了直边法的合理性；考虑ＪＲＣ的尺寸效应，推导ＪＲＣ修正直边法的数学表达式，编译计算机常用程序。最后，实例检验了ＪＲＣ修正直边法数学表达式的合理性和可用性。

JRC快速测量技术

在回顾粗糙度测量方法的基础上 ,从岩石节理的具体特点出发 ,研制了轮廓曲线仪和粗糙度尺 ,为节理粗糙度系数的统计测量提拱了一种快速测量手段。

JRC-JCS模型与直剪试验对比研究

结构面抗剪强度是影响工程岩体稳定的重要因素,它决定工程岩体破坏的可能性。基于结构面粗糙度系数定向统计测量技术的JRC-JCS模型可以考虑地质和环境因素的影响,具有费用低、速度快、简便易行等特点,是获取工程岩体无填充或少填充硬岩结构面抗剪强度参数的实用方法,已为50多个岩体工程提供了结构面抗剪强度参数。为分析JRC-JCS模型评价结构面抗剪强度参数的可靠性,选取天然岩石结构面试样,进行干燥状态和饱和状态结构面抗剪强度直剪试验和JRC-JCS模型评价的对比研究。结果表明,在结构面粗糙度系数的定向统计测量、尺寸效应分析,以及剪切过程衰减折减的基础上,运用结构面粗糙度系数定向统计测量技术的JRC-JCS模型评价的结构面抗剪强度参数与直剪试验结果具有较好的一致性,JRC-JCS模型对结构面峰值摩擦角有较好的预测能力。

岩体结构面粗糙度系数JRC的研究现状

岩体结构面粗糙度系数JRC是描述岩体结构面粗糙起伏状态的力学参数,对于无填充或少填充的硬性结构面而言是关键性指标。本文介绍了岩体结构面粗糙度系数JRC估算方法的理论及应用研究现状,并探讨了JRC研究中存在的几个问题。

智能JRC测量仪的研制

研制了一种智能 JRC 测量仪,该仪器可直接测量岩体结构面的粗糙度系数 JRC 值,适用于野外任意产状的岩体结构面,可以沿任意方向测量岩体结构面粗糙度系数,具有重量轻、体积小、携带方便、操作简单、测量速度快等特点。

JRC分形估测方法的实用性

基于分形几何的码尺法分维数与岩石节理粗糙度系数的物理意义剖析，认为Ｄ－ＪＲＣ之间不存在必然的相关性．分析标准轮廓曲线的分维数，发现其分维数差级微小，难以实行粗糙度系数分级．根据实测资料阐述了岩石节理表面轮廓曲线的“自相似”是统计意义而不是绝对的，它要求ＪＲＣ分形估测应统计求取，而过繁的分维数测量步骤削弱了ＪＲＣ的分形统计估测的可行性．建立在实测资料统计分析基础上的ＪＲＣ尺寸效应分形模型ＪＲＣｎ＝ＪＲＣ０（Ｌｎ／Ｌ０）－Ｄ客观而真实地刻画了粗糙度系数随取样长度增大而降低的规律，其中，ＪＲＣ尺寸效应分维数（Ｄ）具明确的物理意义，它描述了ＪＲＣ随结构面规模增大而降低的衰减速率．最后，运用ＪＲＣ尺寸效应分维数（Ｄ）探讨了岩石节理粗糙度系数尺寸效应的各向异性规律．

岩体结构面粗糙度系数JRC的定向统计研究

本文回顾了岩体结构面粗糙度系数ＪＲＣ的研究成果，分析了各种ＪＲＣ研究方法的应用范围。在野外实际结构面形态的详细调查和深入研究的基础上，发展了Ｂａｒｔｏｎ直边法，并提出按岩性定向统计研究结构面粗糙度系数ＪＲＣ的科学思想。

甘肃北山花岗岩岩体结构面粗糙度系数(JRC)研究

岩体结构面表面形态研究对岩石工程结构体强度及变形特征研究评价具有重要的指导意义。文章评述了现有岩体结构面粗糙度系数(JRC)的各种测定方法及存在的问题,分析了在高放废物处置库预选场址甘肃北山花岗岩地区,用"简易纵剖面仪"进行地表岩体及钻孔岩心裂隙结构面JRC的测量结果。

基于多元拟合方程的结构面粗糙度系数JRC估算

本文以BARTON的标准粗糙度剖面为研究对象,假定剪切方向为从左向右,量取剖面曲线长L以及"爬坡"时的倾角i_i和与倾角相对应高差h_i、面积S,把不同的因素组合在一起,用MATLAB对测得的数据进行拟合,得到用来估算结构面粗糙度系数且不依赖于结构面长度的解析方程。结果表明:剖面长度比和面积比组合得到的拟合方程较差,剖面长度比、面积比和爬坡高组合得到的方程较好;剖面长度比、倾角i_i和高差h_i三个指标的组合拟合方程最好,可很好地反映结构面粗糙度,拟合优度达97. 1%。

Macro and meso characteristics evolution on shear behavior of rock joints

Direct shear tests were conducted on the rock joints under constant normal load(CNL), while the acoustic emission(AE) signals generated during shear tests were monitored with PAC Micro-II system. Before and after shearing, the surfaces of rock joints were measured by the Talysurf CLI 2000. By correlating the AE events with the shear stress-shear displacement curve, one can observe four periods of the whole course of shearing of rock joints. By the contrast of AE location and actual damage zone, it is elucidated that the AE event is related to the morphology of the joint. With the increase of shearing times, the shear behavior of rock joints gradually presents from the response of brittle behavior to that of ductile behavior. By combining the results of topography measurement, four morphological parameters of joint surface, S p(the maximum height of joint surface), N(number of islands), A(projection area) and V(volume of joint) were introduced, which decrease with shearing. Both the joint roughness coefficient(JRC) and joint matching coefficient(JMC) drop with shearing, and the shear strength of rock joints can be predicted by the JRC-JMC model. It establishes the relationship between micro-topography and macroscopic strength, which have the same change rule with shearing.

Morphology characteristics of joint surface in rock mass

In order to quantify the characteristics of the surface of jointed rock mass,new equipment,the three-dimensional laser surface topography instrument,was used to accurately measure surface morphology of joints.Scan pictures and parameters were obtained to describe the rock joint surface characteristics,for example,the height frequency of surface,and mean square roughness.Using the method of fractal dimension,the values of joint roughness coefficient(JRC) were calculated based on the above parameters.It could access to the joint surface rock sample morphology of the main parameters of characteristic.The maximum peak height is 2.692 mm in the test joint plane.The maximum profile height is 4.408 mm.JRC value is 6.38 by fractal dimension computing.It belongs to the smooth joint surface.The results show that it is a kind of the effective method to quantitatively evaluate the surface topography by the three-dimensional laser surface topography instrument and the fractal dimension method.According to the results,during the process of underground large-scale mining,safe measures to prevent slip failure of the joint plane by controlling surface tension and shear mechanical response were proposed.

岩体裂隙粗糙度表征及其对裂隙渗流特性的影响

岩体裂隙粗糙程度对裂隙渗流特性的影响显著。利用三维光学扫描系统获取岩体裂隙面点云数据,结合SURFER和GEOMAGIC STUDIO等软件计算裂隙面节理粗糙度系数JRC和表面粗糙比率Rs,建立JRC与Rs的定量关系,开展应力、渗流和化学耦合作用下石灰岩裂隙渗流试验,研究JRC和Rs对粗糙裂隙渗流特性的影响。结果表明:JRC与Rs呈对数函数关系,其平方根R2为0.9128,该表征公式与裂隙渗流试验结果最大相对误差MRE、平均绝对误差MAE和均方根误差RMSE分别为6.93%、0.34和0.27。JRC与渗流量、稳定期渗透率分别呈二次函数和对数函数关系,Rs和各参数的拟合关系与JRC相同。JRC值越大,渗流量和渗透率越小,且三场耦合作用下裂隙面JRC和Rs值均有所增大。该表征方法可用于岩体裂隙面粗糙度估算,由裂隙面JRC值可预测该裂隙渗流量和稳定时刻渗透率。

高温作用后砂岩-混凝土黏结界面拉伸力学特性研究

热灾害频发对地下工程的安全性提出了挑战,尤其围岩-衬砌黏结界面作为地应力传递的“纽带”,对支护结构的稳定至关重要。文章对不同界面粗糙度的砂岩-混凝土组合圆盘开展25~800℃高温处理后的劈裂试验,结合单一材料性能、界面黏结机理和组合圆盘的破裂演化过程,分析了粗糙程度、温度效应对界面拉伸强度和劈裂模式的影响。结果表明:组成围岩-衬砌结构的混凝土和砂岩的物理力学性能均随温度的升高出现一定程度的劣化,且前者受热损伤程度远大于后者。高温作用下,热裂隙并不局限于界面过渡区,而是主要汇集在砂岩和混凝土结构内部;同时,组合体的拉伸强度基本介于两种材料强度之间,说明岩-混凝土黏结界面未必是组合结构的薄弱面。存在以JRC=5.86的阈值粗糙度,砂岩-混凝土组合试样的拉伸强度以此为界先降低而后增强,这种强度变化趋势在25~400℃温度范围内相当显著(降幅介于49.11%~59.31%,增幅达56.13%~69.79%),而600℃和800℃加热后强度变化基本趋于平缓(降低24.09%~28.85%,无明显回升)。破裂演化过程表明,随着加热温度的提高,组合试样的劈裂模式由端部压剪破坏向中部拉伸破坏过渡;增大界面粗糙度使得界面从端部单点起裂向多点复合断裂转变。

线-面结合的结构面粗糙系数经验计算方法

岩体结构面的剪切力学特性主要取决于其表面粗糙特征,结构面粗糙系数是表征该粗糙特征的主要方法。目前对结构面粗糙系数的研究局限于单一维度,多角度且定量化计算结构面粗糙系数能避免单一维度分析导致计算精度不准的局限性。采用立方体花岗岩,通过巴西劈裂的方式制备含结构面的试样;利用高精度三维扫描仪对试样结构面进行扫描,得到结构面的点云数据,同时借助逆向软件对点云数据进行三维重构。研究了点云数据Z方向上的分布频率,剖面线剖面比与节理粗糙系数(JRC)值的关系,结构面面积比与JRC均值的关系。研究表明:点云数据Z方向上的分布频率可以作为初步评估结构面粗糙度的手段;剖面线剖面比与JRC数值、结构面面积比与JRC均值有二次函数的关系。通过数值分析建立了结构面JRC均值与剖面比、面积比的二元函数关系,并得到结构面JRC均值的经验计算公式。本次研究为结构面粗糙度提供了一种“点-线-面”逐渐深入的多角度评估思路,得到的经验公式为计算结构面JRC均值提供了一种新的计算方法。

基于有效受剪面积的节理面吻合度量化方法研究

节理粗糙度系数-节理抗压强度(JRC-JCS)模型为岩体节理面抗剪强度估算提供了很好的思路,但对于吻合度较差的节理岩体,该模型往往过高地估算其抗剪强度。节理粗糙度系数-节理吻合系数(JRC-JMC)模型进一步考虑节理吻合度对抗剪强度的影响,为吻合程度较差的节理面的抗剪强度估算提供了很好的思路,但目前节理吻合度的量化分析还存在困难。在以往研究基础上,采用重复剪切试验的方法制备了不同吻合度的节理岩体试样,综合节理面形貌特征和节理面抗剪性能变化规律分析,提出了节理吻合度的量化计算方法。研究结果表明:(1)在重复剪切作用下,岩体节理面的抗剪强度和节理面形貌参数呈现先陡后缓的降低趋势,节理面上下盘的吻合程度逐渐降低;(2)根据剪切前后节理面形貌特征的对比分析,确定了剪切过程节理面的有效接触区域,提出了节理吻合系数JMC的量化计算方法;(3)对比分析表明,与单独考虑JRC相比,将JRC和JMC综合在一起可以更好地反映节理形貌特征对其剪切性能的影响。相关研究方法和分析结果可为岩体节理面抗剪性能分析提供较好的参考。