A method for clustering rock discontinuities with multiple properties based on an improved netting algorithm

Clustering analysis is fundamental for determining dominant discontinuity properties in rock engineering. Orientation has commonly been considered the only factor when performing clustering, which ignores the contributions of other discontinuity properties to the deformations and strengths of rock masses. This study proposes an improved netting algorithm to identify discontinuity sets based on multiple discontinuity properties. The method takes ten discontinuity properties as the clustering factors: dip direction, dip, trace length, spacing, aperture, infilling material, infilling percentage, roughness, water permeability, and rock strength. Meanwhile, a novel weighting method is used to weigh each property and combines the advantages of subjective and objective weighting methods. The validity of the proposed method is tested using artificial data based on the Monte Carlo method and in situ data from the relevant literature. The results indicate that the method can effectively filter the noise data, and the rejection rate is approximately 26%. The initial number of sets and initial clustering centers are not necessary, which facilitates achieving global optimization. Finally, an open-pit mine slope in Xinjiang Province, China, is used as a case study to illustrate the utility of the method. The new method is considered a potentially useful tool to rapidly obtain dominant discontinuity sets in rock engineering.

Automated summary

This study proposes an improved netting algorithm to identify discontinuity sets based on multiple discontinuity properties. A novel weighting method is used to weigh each property and combines the advantages of subjective and objective weighting methods. The method can effectively filter the noise data and achieve global optimization without the need for initial clustering centers and group numbers.