Statistical representation of joint roughness

Wu, Tien H.; Ali, Elfatih M.

doi:10.1016/0148-9062(78)90958-0

Cited by 50 publications

(10 citation statements)

References 2 publications

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“…Shortcomings of JRC in quantifying rock joint roughness have been pointed out by several researchers (Miller et al, 1990;Maerz et al, 1990;Hsiung et al, 1993;Kodikara and Johnston, 1994;Kulatilake et al, 1995). A number of researchers have investigated the applicability of various conventional statistical parameters in quantifying roughness (Tse and Cruden, 1979;Wu and Ali, 1978;Krahn and Morgenstern, 1979;Dight and Chiu, 1981;Maerz et al, 1990;Reeves, 1990). Kulatilake et al (1995) have pointed out that the values obtained for conventional statistical parameters vary with the measurement scale.…”

Section: Introductionmentioning

confidence: 95%

Natural rock joint roughness quantification through fractal techniques

et al. 2006

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Accurate quantification of roughness is important in modeling hydro-mechanical behavior of rock joints. A highly refined variogram technique was used to investigate possible existence of anisotropy in natural rock joint roughness. Investigated natural rock joints showed randomly varying roughness anisotropy with the direction. A scale dependant fractal parameter, K v , seems to play a prominent role than the fractal dimension, D r1d , with respect to quantification of roughness of natural rock joints. Because the roughness varies randomly, it is impossible to predict the roughness variation of rock joint surfaces from measurements made in only two perpendicular directions on a particular sample. The parameter D r1d Â K v seems to capture the overall roughness characteristics of natural rock joints well. The one-dimensional modified divider technique was extended to two dimensions to quantify the two-dimensional roughness of rock joints. The developed technique was validated by applying to a generated fractional Brownian surface with fractal dimension equal to 2.5. It was found that the calculated fractal parameters quantify the rock joint roughness well. A new technique is introduced to study the effect of scale on two-dimensional roughness variability and anisotropy. The roughness anisotropy and variability reduced with increasing scale.

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Section: Introductionmentioning

confidence: 95%

Natural rock joint roughness quantification through fractal techniques

et al. 2006

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“…The JRC value was recommended by ISRM [4] and has been widely used in engineering practices. Over the years, some statistical parameters having correlations with JRC have been developed [5][6][7][8][9]. Tse and Curden [6] studied eight statistical parameters and found that Z 2 (the slope of asperity) and structure function (SF) are strongly correlated with the values of JRC.…”

Section: Introductionmentioning

confidence: 99%

Estimating the relation between surface roughness and mechanical properties of rock joints

Jiang

Tanabashi

2006

International Journal of Rock Mechanics and Mining Sciences

203

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“…However, it is obvious that JRC determined by visual comparison is subjective and sometimes erratic. Many researchers have attempted to calculate the JRC value from the profile geometry such as root mean square (RMS), RMS of the first derivatives (Z2), RMS of the second derivatives (Z3), structure function (SF), roughness profile index (RP), and fractal dimension [49][50][51][52].…”

Section: Measurement Of Joint Roughness Coefficient (Jrc)mentioning

confidence: 99%