Improvement of the JRC Calculation Using Different Parameters Obtained Through a New Survey Method Applied to Rock Discontinuities

Alameda-Hernández, Pedro; Jiménez-Perálvarez, J. D.; Palenzuela, J. Antonio; Hamdouni, Rachid El; Irigaray, Clemente; Cabrerizo, M.A.; Chacón, José Luis Torres

doi:10.1007/s00603-013-0532-2

Cited by 37 publications

(13 citation statements)

References 19 publications

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“…Various methods for determining the JRC values have been proposed by many scholars [2][3][4][5][6][7][8][9][10] since Barton [11] firstly introduced the concept of JRC. Since then there have also been many studies about the anisotropy and scale effect, which are two important characteristics of JRC in the sample sizes and measuring orientations.…”

Section: Introductionmentioning

confidence: 99%

Expression and Analysis of Joint Roughness Coefficient Using Neutrosophic Number Functions

Chen

Rui

et al. 2017

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Abstract:In nature, the mechanical properties of geological bodies are very complex, and its various mechanical parameters are vague, incomplete, imprecise, and indeterminate. In these cases, we cannot always compute or provide exact/crisp values for the joint roughness coefficient (JRC), which is a quite crucial parameter for determining the shear strength in rock mechanics, but we need to approximate them. Hence, we need to investigate the anisotropy and scale effect of indeterminate JRC values by neutrosophic number (NN) functions, because the NN is composed of its determinate part and the indeterminate part and is very suitable for the expression of JRC data with determinate and/or indeterminate information. In this study, the lower limit of JRC data is chosen as the determinate information, and the difference between the lower and upper limits is chosen as the indeterminate information. In this case, the NN functions of the anisotropic ellipse and logarithmic equation of JRC are developed to reflect the anisotropy and scale effect of JRC values. Additionally, the NN parameter ψ is defined to quantify the anisotropy of JRC values. Then, a two-variable NN function is introduced based on the factors of both the sample size and measurement orientation. Further, the changing rates in various sample sizes and/or measurement orientations are investigated by their derivative and partial derivative NN functions. However, an actual case study shows that the proposed NN functions are effective and reasonable in the expression and analysis of the indeterminate values of JRC. Obviously, NN functions provide a new, effective way for passing from the classical crisp expression and analyses to the neutrosophic ones.

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Section: Introductionmentioning

confidence: 99%

Expression and Analysis of Joint Roughness Coefficient Using Neutrosophic Number Functions

Chen

Rui

et al. 2017

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“…According to (9), the variation trend of scale effect can be expressed by the derivative of JRC(l). e expression of the derivative D JRC is…”

Section: Profile Sampling Process Figuresmentioning

confidence: 99%

“…Micallef and Williams [8] used an H-shaped rock profiler to measure locally common smooth and irregular natural rock surfaces. Alameda-Hernández et al [9] applied a needle profilometer and image-processing method for fast and economical digitization of rock surface profiles. Although these methods can obtain high-resolution and high-precision joint profiles, the allowable size of the measured joint is usually very limited (≤1 m), because the digitization of a large joint is very time-consuming and inconvenient to operate in situ.…”

Section: Introductionmentioning

confidence: 99%

Neutrosophic Function for Assessing the Scale Effect of the Rock Joint Surface Roughness

Rui

Tan

et al. 2021

Mathematical Problems in Engineering

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A new investigation method is proposed for recording large-sized joint profiles and making statistical analyses of the joint roughness coefficient (JRC) values of the 10–300 cm sized profiles. The mechanical hand profilograph is used for joint roughness measurement due to its advantage of easy operation and high accuracy in recording joint traces. Based on the proposed method, it provides sufficient samples from various positions on the large joint profile, which allows the statistical evaluation of JRC values. A neutrosophic number (NN) is employed for revealing determinate and/or indeterminate information as it consists of determinate and indeterminate parts. Due to the uncertainty of JRC in the real world, NN is chosen to represent the JRC value, which is not only random but also a fuzzy indefinite parameter. The neutrosophic function is used to analyze and express the scale effect of joint surface roughness, and its derivative is used to describe the changing trend of the scale effect. The results show that the JRC value of the joint profile is related to the scale and has a negative effect on the surface roughness of the rock joint. The indeterminate information about the scale effect on joint roughness is described by the neutrosophic functions, and the derivative indicated that the JRC values of small samples are more sensitive than those of large-sized examples. When the length of the sample exceeds the stationarity limit of 80 cm, the roughness appears to be almost scale independent.

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“…e JRC can be visually estimated from the standard profiles that correspond to values within [0,20] in laboratory tests. Subsequently, many researchers have proposed various improvements to calculate JRC values more precisely than visual assessment [2][3][4][5]. To facilitate the application of JRCs in rock engineering practices, a series of new parameters and methods, such as statistical methods [6][7][8][9][10], straight edge method [11,12], fractal dimension method [13][14][15][16][17], and other quantitative methods, has been introduced to evaluate the JRCs of the ten standard profiles.…”

Section: Introductionmentioning

confidence: 99%

Correlation between the Joint Roughness Coefficient and Rock Joint Statistical Parameters at Different Sampling Intervals

Huang

Xia

Sha

et al. 2019

Advances in Civil Engineering

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Joint roughness coefficient (JRC) is a major factor that affects the mechanical properties of rock joints. Statistical methods that are used to calculate the JRC increasingly depend on a sampling interval (Δx). The variation rules of fitting parameters a, b, and b/a at different Δx values were analyzed on the basis of the relationship between the JRC and statistical parameter Z2. The relationship between the fitting parameters a and b was deduced in accordance with the ten standard profiles proposed by Barton. Empirical formulas for the JRC, Z2, and Δx were also established. The estimation accuracy of the JRC was the highest in the analysis of Δx values within 0.1–5.0 mm. JRC tests were conducted through inverse value comparative analysis. Results showed that the outcome calculated using the general formula and the JRC inverse values demonstrate improved agreement and verify the rationality of the general formula. The proposed formula can perform rapid and simple JRC calculation within the Δx range of 0.1–5.0 mm using Z2, thereby indicating favorable application prospects.

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Improvement of the JRC Calculation Using Different Parameters Obtained Through a New Survey Method Applied to Rock Discontinuities

Cited by 37 publications

References 19 publications

Expression and Analysis of Joint Roughness Coefficient Using Neutrosophic Number Functions

Expression and Analysis of Joint Roughness Coefficient Using Neutrosophic Number Functions

Neutrosophic Function for Assessing the Scale Effect of the Rock Joint Surface Roughness

Correlation between the Joint Roughness Coefficient and Rock Joint Statistical Parameters at Different Sampling Intervals

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