Measurement of joint roughness coefficient by using profilograph and roughness ruler

Du, Shigui; Hu, Yuzheng; Hu, Xiaofei

doi:10.1007/s12583-009-0075-3

Cited by 61 publications

(20 citation statements)

References 5 publications

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“…As an actual case study in this paper, the original roughness profiles were measured by using profilograph and a roughness ruler [13] on a natural rock joint surface in Changshan County of Zhejiang Province, China. In the actual case, based on a classical statistical method we have obtained the average values µ ij and standard deviations σ ij (i = 1, 2, .…”

Section: Jrc Values and Jrc-nns In An Actual Casementioning

confidence: 99%

Scale Effect and Anisotropy Analyzed for Neutrosophic Numbers of Rock Joint Roughness Coefficient Based on Neutrosophic Statistics

Chen

2017

Symmetry

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170

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Abstract:In rock mechanics, the study of shear strength on the structural surface is crucial to evaluating the stability of engineering rock mass. In order to determine the shear strength, a key parameter is the joint roughness coefficient (JRC). To express and analyze JRC values, Ye et al. have proposed JRC neutrosophic numbers (JRC-NNs) and fitting functions of JRC-NNs, which are obtained by the classical statistics and curve fitting in the current method. Although the JRC-NNs and JRC-NN functions contain much more information (partial determinate and partial indeterminate information) than the crisp JRC values and functions in classical methods, the JRC functions and the JRC-NN functions may also lose some useful information in the fitting process and result in the function distortion of JRC values. Sometimes, some complex fitting functions may also result in the difficulty of their expressions and analyses in actual applications. To solve these issues, we can combine the neutrosophic numbers with neutrosophic statistics to realize the neutrosophic statistical analysis of JRC-NNs for easily analyzing the characteristics (scale effect and anisotropy) of JRC values. In this study, by means of the neutrosophic average values and standard deviations of JRC-NNs, rather than fitting functions, we directly analyze the scale effect and anisotropy characteristics of JRC values based on an actual case. The analysis results of the case demonstrate the feasibility and effectiveness of the proposed neutrosophic statistical analysis of JRC-NNs and can overcome the insufficiencies of the classical statistics and fitting functions. The main advantages of this study are that the proposed neutrosophic statistical analysis method not only avoids information loss but also shows its simplicity and effectiveness in the characteristic analysis of JRC.

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Section: Jrc Values and Jrc-nns In An Actual Casementioning

confidence: 99%

Scale Effect and Anisotropy Analyzed for Neutrosophic Numbers of Rock Joint Roughness Coefficient Based on Neutrosophic Statistics

Chen

2017

Symmetry

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170

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“…(1), in which the flow rate would be smaller for the rough-walled fracture comparing to the smooth-walled fracture when other parameters are identical (Boutt et al 2006). The surface roughness of natural rock fractures has been widely investigated (e.g., Neuzil and Tracy 1981;Brown and Scholz 1985;Moreno et al 1988;Dubuc et al 1989;Schmittbuhl et al 1995;Develi and Babadagli 1998;Drazer and Koplik 2000;Du et al 2009;Crandall et al 2010;Rasouli and Hosseinian 2011), and different approaches have been proposed to characterize them such as roughness coefficient (Z 2 ) (Myers 1962), JRC (Barton 1973), and fractal dimension (Babadagli et al 2015). JRC values scale the fracture roughness with 10 typical profiles in the range from 0 (smooth) to 20 (rough) that can be back calculated from tilting tests.…”

Section: Permeability and Fracture Surface Roughnessmentioning

confidence: 99%

Review: Mathematical expressions for estimating equivalent permeability of rock fracture networks

et al. 2016

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Fracture networks play a more significant role in conducting fluid flow and solute transport in fractured rock masses, comparing with that of the rock matrix. Accurate estimation of the permeability of fracture networks would help researchers and engineers better assess the performance of projects associated with fluid flow in fractured rock masses. This study provides a review of previous works that have focused on the estimation of equivalent permeability of twodimensional (2-D) discrete fracture networks (DFNs) considering the influences of geometric properties of fractured rock masses. Mathematical expressions for the effects of nine important parameters that significantly impact on the equivalent permeability of DFNs are summarized, including (1) fracturelength distribution, (2) aperture distribution, (3) fracture surface roughness, (4) fracture dead-end, (5) number of intersections, (6) hydraulic gradient, (7) boundary stress, (8) anisotropy, and (9) scale. Recent developments of 3-D fracture networks are briefly reviewed to underline the importance of utilizing 3-D models in future research.

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“…Twenty thousand and seventy eight profile curves have been recorded by use of the profilograph instrument [12]. These curves belong to six categories of hard joint surfaces (such as stratification, foliation, phyllite, schistosity, fault, and joint) with various sample lengths (such as 10 cm, 15 cm, 22 cm, 30 cm, 40 cm, 50 cm, 60 cm, 70 cm, 80 cm, 90 cm, and 100 cm).…”

Section: Typical Surface Profile Curves Of Rock Jointsmentioning

confidence: 99%

Generalized Models for Rock Joint Surface Shapes

2014

The Scientific World Journal

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Generalized models of joint surface shapes are the foundation for mechanism studies on the mechanical effects of rock joint surface shapes. Based on extensive field investigations of rock joint surface shapes, generalized models for three level shapes named macroscopic outline, surface undulating shape, and microcosmic roughness were established through statistical analyses of 20,078 rock joint surface profiles. The relative amplitude of profile curves was used as a borderline for the division of different level shapes. The study results show that the macroscopic outline has three basic features such as planar, arc-shaped, and stepped; the surface undulating shape has three basic features such as planar, undulating, and stepped; and the microcosmic roughness has two basic features such as smooth and rough.

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Measurement of joint roughness coefficient by using profilograph and roughness ruler

Cited by 61 publications

References 5 publications

Scale Effect and Anisotropy Analyzed for Neutrosophic Numbers of Rock Joint Roughness Coefficient Based on Neutrosophic Statistics

Scale Effect and Anisotropy Analyzed for Neutrosophic Numbers of Rock Joint Roughness Coefficient Based on Neutrosophic Statistics

Review: Mathematical expressions for estimating equivalent permeability of rock fracture networks

Generalized Models for Rock Joint Surface Shapes

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