Requirements for accurate quantification of self affine roughness using the roughness-length method

Abstract Self-affine fractals have the potential to represent rock joint roughness profiles. Fractional Brownian profiles (self-affine profiles) with known values of fractal dimension, D , input standard deviation, σ , and data density, d , were generated. For different values of the input parameter of the roughness–length method (window length, w ), D and another associated fractal parameter A were calculated for the aforementioned profiles. The calculated D was compared with the D used for the generation to determine the accuracy of calculated D . Suitable ranges for w were estimated to produce accurate D (within ±10% error) for the generated profiles. The results showed that to obtain reliable estimates for fractal parameters of a natural rock joint profile, it is necessary to choose a unit for the profile length to satisfy a data density ( d ) greater than or equal to 5.1. For roughness profiles having 5.1≤ d ≤51.23 and 1.2≤ D ≤1.7, w values between 2.5% and 10% of the profile length were found to be highly suitable to produce accurate fractal parameter estimates. It is recommended to use at least seven w values between the estimated minimum and maximum suitable w values in estimating fractal parameters of a natural rock joint profile. It was found that σ and a global trend of a roughness profile have no effect on calculated D . The estimated A was found to increase with both D and σ . The parameter D captures the auto-correlation and A captures the amplitude of a roughness profile at different scales. Therefore, the parameters D and A are recommended to use with the roughness–length method in quantifying rock joint roughness. In addition, at least one more parameter is required to quantify the global trend of a roughness profile, if it exist; in many cases just the inclination or declination angle of the roughness profile in the direction considered would be sufficient to estimate the global trend. Calculated cross-over lengths (segment length of a profile at which a self-affine profile becomes self-similar) for the profiles investigated were found to be extremely small (less than 0.6% of the profile length) indicating that laser profilometers are required to make roughness measurements at interval lengths comparable to the cross-over lengths of the natural rock joint profiles. To calculate rock joint roughness parameters accurately using the self-similar techniques, it is necessary to have roughness measurements made at interval lengths comparable to the cross-over length of the profile. This indicate clearly the difficulty of using self-similar techniques such as the divider method in estimating rock joint roughness accurately.

[1]  D. Cruden,et al.  ESTIMATING JOINT ROUGHNESS COEFFICIENTS , 1979 .

[2]  P. Kulatilake,et al.  Requirements for accurate estimation of fractal parameters for self-affine roughness profiles using the line scaling method , 1997 .

[3]  C. G. Fox An inverse Fourier transform algorithm for generating random signals of a specified spectral form , 1987 .

[4]  Terry E. Tullis,et al.  Euclidean and fractal models for the description of rock surface roughness , 1991 .

[5]  H.R.G.K. Hack,et al.  Difficulties with using continuous fractal theory for discontinuity surfaces , 1996 .

[6]  R. C. Speck,et al.  APPLICABILITY OF FRACTAL CHARACTERIZATION AND MODELLING TO ROCK JOINT PROFILES , 1992 .

[7]  N. Odling,et al.  Natural fracture profiles, fractal dimension and joint roughness coefficients , 1994 .

[8]  Accuracy of the spectral method in estimating fractal/spectral parameters for self-affine roughness profiles , 1997 .

[9]  P. Kulatilake,et al.  Requirements for accurate quantification of self-affine roughness using the variogram method , 1998 .

[10]  A. Malinverno A simple method to estimate the fractal dimension of a self‐affine series , 1990 .

[11]  Norbert R. Morgenstern,et al.  THE ULTIMATE FRICTIONAL RESISTANCE OF ROCK DISCONTINUITIES , 1979 .

[12]  Donald S. Fussell,et al.  Computer rendering of stochastic models , 1982, Commun. ACM.

[13]  Kevin Duncan Privett Use of thick layers in chalk earthworks at Port Solent Marina, Portsmouth, UK , 1991 .

[14]  Heinz-Otto Peitgen,et al.  The science of fractal images , 2011 .

[15]  D. Saupe Algorithms for random fractals , 1988 .

[16]  Richard F. Voss,et al.  Fractals in nature: from characterization to simulation , 1988 .

[17]  M. Berry,et al.  On the Weierstrass-Mandelbrot fractal function , 1980, Proceedings of the Royal Society of London. A. Mathematical and Physical Sciences.

[18]  Jens Feder Random Walks and Fractals , 1988 .

[19]  S. Orey Gaussian sample functions and the Hausdorff dimension of level crossings , 1970 .

[20]  Shunji Ouchi,et al.  On the self-affinity of various curves , 1989 .

[21]  Nick Barton,et al.  Review of a new shear-strength criterion for rock joints , 1973 .

[22]  H. K. Chiu,et al.  PREDICTION OF SHEAR BEHAVIOUR OF JOINTS USING PROFILES , 1981 .

[23]  Benoit B. Mandelbrot,et al.  Fractal Geometry of Nature , 1984 .

[24]  Jayantha Kodikara,et al.  Shear behaviour of irregular triangular rock-concrete joints , 1994 .

[25]  Elfatih Mohamed Ali,et al.  Statistical representation of joint roughness , 1978 .

[26]  Pinnaduwa Kulatilake,et al.  New peak shear strength criteria for anisotropic rock joints , 1995 .

[27]  P. C. McWilliams,et al.  Ambiguities in estimating fractal dimensions of rock fracture surfaces , 1990 .

[28]  M. J. Reeves,et al.  Rock surface roughness and frictional strength , 1985 .

[29]  Norbert H. Maerz,et al.  Joint roughness measurement using shadow profilometry , 1990 .

[30]  D. D. Kana,et al.  Laboratory characterization of rock joints , 1994 .

[31]  Stephen R. Brown,et al.  Broad bandwidth study of the topography of natural rock surfaces , 1985 .