A new multiple-factor clustering method considering both box fractal dimension and orientation of joints

Abstract

The paper proposes a new multiple-factor clustering method (NMFCM) with consideration of both box fractal dimension (BFD) and orientation of joints. This method assumes that the BFDs of different clusters were uneven, and clustering was performed by redistributing the joints near the boundaries of clusters on a polar map to maximize an index for estimating the difference of the BFD (DBFD). Three main aspects were studied to develop the NMFCM. First, procedures of the NMFCM and reasonableness of assumptions were illustrated. Second, main factors affecting the NMFCM were investigated by numerical simulations with disk joint models. Finally, two different sections of a rock slope were studied to verify the practicability of the NMFCM. The results demonstrated that: (1) The NMFCM was practical and could effectively alleviate the problem of hard boundary during clustering; (2) The DBFD tended to increase after the improvement of clustering accuracy; (3) The improvement degree of the NMFCM clustering accuracy was mainly influenced by three parameters, namely, the number of clusters, number of redistributed joints, and total number of joints; and (4) The accuracy rate of clustering could be effectively improved by the NMFCM.

Keywords

• Joint clustering
• Box fractal dimension (BFD)
• Orientation
• Hard boundary