Comparative Evaluation of Statistical and Fractal Approaches for JRC Calculation Based on a Large Dataset of Natural Rock Traces

After the publication of the type-profiles for the estimation of the joint roughness coefficient (JRC) a discussion evolved about how to adequately use these traces. Based on the chart numerous researchers assembled mathematical correlations with various parameters seeking objectivity in the determination of JRC. Within these works differences concerning the database and the mathematical implementations exist. Consequently, each correlation, although predominantly the same parameters are used, leads to different JRC values. In theory, for any arbitrary profile, irrespective of the particular calculation approach, the same JRC should result. This is a requisite because of the referencing of all correlations to the 10 type-profiles. However, it is shown in this study that in most cases equal or even satisfactorily similar results are not obtained. The discrepancies are vast when non-standard profiles are evaluated, in this case, more than 40,000 traces from six different rock surfaces that cover a broad range of roughness categories. The simple intuitive parameter Z2 served as an agent for the statistical methods because of its broad use and consequently good comparability. On the part of the fractal approaches, three definitions were used. However, JRC inferred from fractal correlations are very much dependent on the particular calculation routine. In fact, the theory of fractals is overly complex for the sparse and low-resolution type-profiles. In summary, fractal approaches do not produce safer or more reliable estimates of roughness compared to simple statistical means and using Z2 perfectly suffices to determine the class of JRC.