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

Huang, Man; Xia, C. C.; Sha, Peng; Du, Shigui

doi:10.1155/2019/1643842

Cited by 6 publications

(6 citation statements)

References 29 publications

(22 reference statements)

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“…The anisotropy of the JRC values indicates that there are obvious differences in the JRC values in different measurement directions on the same rock joint surface. In addition, the sampling bias will also increase the uncertainty of the JRC values [104,105].…”

Section: Neutrosophic Expression and Analysis Of Rock Joint Roughness...mentioning

confidence: 99%

Developments and Applications of Neutrosophic Theory in Civil Engineering Fields: A Review

Zhang¹,

Ye²

2023

Computer Modeling in Engineering &Amp; Sciences

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Neutrosophic theory can effectively and reasonably express indeterminate, inconsistent, and incomplete information. Since Smarandache proposed the neutrosophic theory in 1998, neutrosophic theory and related research have been developed and applied to many important fields. Indeterminacy and fuzziness are one of the main research issues in the field of civil engineering. Therefore, the neutrosophic theory is very suitable for modeling and applications of civil engineering fields. This review paper mainly describes the recent developments and applications of neutrosophic theory in four important research areas of civil engineering: the neutrosophic decision-making theory and applied methods, the neutrosophic evaluation methods and applications of slope stability, the neutrosophic expressions and analyses of rock joint roughness coefficient, and the neutrosophic structural optimization methods and applications. In terms of these research achievements in the four areas of civil engineering, the neutrosophic theory demonstrates its advantages in dealing with the indeterminate and inconsistent issues in civil engineering and the effectiveness and practicability of existing applied methods. In the future work, the existing research results will be further improved and extended in civil engineering problems. In addition, the neutrosophic theory will also have better application prospects in other fields of civil engineering.

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Section: Neutrosophic Expression and Analysis Of Rock Joint Roughness...mentioning

confidence: 99%

Developments and Applications of Neutrosophic Theory in Civil Engineering Fields: A Review

Zhang¹,

Ye²

2023

Computer Modeling in Engineering &Amp; Sciences

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“…As the existing roughness estimation methods are sensitive to the sampling interval (Δx), it is extremely important to study the relationship between the sampling interval and roughness in order to determine the maximum sampling interval. Huang et al [22] established an empirical formula of JRC, Z2 and Δx.…”

Section: Size Effect Of Regular Sampling Intervalmentioning

confidence: 99%

“…As the existing roughness estimation methods are sensitive to the sampling interval (∆x), it is extremely important to study the relationship between the sampling interval and roughness in order to determine the maximum sampling interval. Huang et al [22] established an empirical formula of JRC, Z 2 and ∆x. JRC = −13.96∆x −0.24 + 55.90∆x 0.01 Z 2 (6) Energies 2021, 14, x FOR PEER REVIEW 8 of 18 Unfortunately, it is difficult to determine the maximum sampling interval directly through the above empirical formula.…”

Section: Size Effect Of Regular Sampling Intervalmentioning

confidence: 99%

“…Thus, equations with amplitude parameters are recommended to facilitate the rapid estimation of JRC in engineering practice for their easy calculation. Huang et al [22] analyzed the variation in fitting parameters at different values of ∆x based on the relationship between JRC and statistical parameter Z 2 , and they established empirical formulas for JRC, Z 2 and ∆x. Due to the scale dependency of roughness, a changing scale will indirectly affect the surface contact mechanics, such as shear strength and dilation.…”

Section: Introductionmentioning

confidence: 99%

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Influences of Roughness Sampling Interval and Anisotropy on Shear Failure Mechanism of Rock Joint Surface

Fan

Yang

et al. 2021

Energies

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Roughness is an important factor affecting the engineering stability of jointed rock masses. The existing roughness evaluation methods are all based on a uniform sampling interval, which changes the geometrical morphology of the original profile and inevitably ignores the influence of secondary fluctuations on the roughness. Based on the point cloud data obtained by 3D laser scanning, a non-equal interval sampling method and an equation for determining the sampling frequency on the roughness profile are proposed. The results show that the non-equal interval sampling method can successfully maintain the morphological characteristics of the original profile and reduce the data processing cost. Additionally, direct shear tests under constant normal load (CNL) conditions are carried out to study the influence of roughness anisotropy on the shear failure mechanism of joint surfaces. It is found that with the increase in shear displacement, the variations in the shear stress are related to the failure mechanisms of dilatancy and shear fracture of the joint. Finally, the distributions of shear stress, dilatancy and fracture areas on the rough joint in different shear directions are calculated theoretically. Results show that the anisotropy and failure mechanism of rough joint can be well characterized by the modified root mean square parameter Z2′.

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“…erefore, in order to obtain the roughness coefficient of joints more accurately, statistical parameters such as the straight edge method [15], modified straight edge method [16], fractal dimension (D) [17,18], root mean square roughness index (RMS) and mean square value roughness index (MSV) [19], root mean square of the first deviation of profiles (Z 2 ) [19][20][21][22][23][24][25][26][27], structure function (SF) [19][20][21][22][23], standard deviation of the angle (SDi) [21,22], roughness profile index (R P ) [21][22][23][24]28], maximum inclination (θ * max ) [23,24], mean positive angle (θ p+ ) [29], modified root mean square (Z′' 2 ) [30], and support vector regression (SVR) model [31] had been presented. ese aforementioned parameters can be calculated from geometric coordinates of joint profile.…”

Section: Introductionmentioning

confidence: 99%

Suggested New Statistical Parameter for Estimating Joint Roughness Coefficient considering the Shear Direction

Zhang

Huan

et al. 2021

Advances in Civil Engineering

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The 10 standard roughness joint profiles provided a visual comparison to get the joint roughness coefficient (JRC) of rock joint surface, but the accuracy of this method is influenced by human factors. Therefore, many researchers try to evaluate the roughness morphology of joint surface through the statistical parameter method. However, JRC obtained from most of the existing statistical parameters did not reflect the directional property of joint surface. Considering the 10 standard profiles as models of different roughness joints, we proposed a new idea for the accurate estimation of JRC. Based on the concept of area difference, the average of positive area difference (Sa) and sum of positive area difference (Ss) were first proposed to reflect the roughness of joint surfaces on the basis of directional property, and their fitting relationship with JRC was also investigated. The result showed that the Sa and Ss calculated by shearing from right to left (FRTL) and JRC backcalculated from right to left (FRTL) came to a satisfying power law. The correlation between JRC and Sa was better than that of Ss. The deviation between the predicted value calculated by Sa and the true value was smaller than that obtained from the existing statistical parameters. Therefore, Sa was recommended as a new statistical parameter to predict the JRC value of joint profile. As the sampling interval increased from 0.5 to 4 mm, the correlation between Sa and JRC gradually decreased, and the accuracy of the prediction results also declined. Compared with the single JRC values for joint profiles mentioned in the literature, the forward and reverse JRC were obtained. Based on the laboratory direct shear test of the natural joint surface, the JRC values of two joint surfaces in four shear directions were backcalculated by the JRC-JCS model. Based on 3D scanning and point cloud data processing technology, JRC of joint surface in different directions were obtained by Sa method, and they are very close to those obtained by JRC-JCS model. It is confirmed that Sa could accurately estimate the joint roughness coefficient and reflect its anisotropy.

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

Cited by 6 publications

References 29 publications

Developments and Applications of Neutrosophic Theory in Civil Engineering Fields: A Review

Developments and Applications of Neutrosophic Theory in Civil Engineering Fields: A Review

Influences of Roughness Sampling Interval and Anisotropy on Shear Failure Mechanism of Rock Joint Surface

Suggested New Statistical Parameter for Estimating Joint Roughness Coefficient considering the Shear Direction

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