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A Fast Clustering Method for Identifying Rock Discontinuity Sets
Abstract Identifying rock discontinuity sets is a major factor for analyses of hydraulic properties and rock mass stability. To perform efficient and precise grouping of discontinuities, a new approach based on clustering by fast searches and finding density peaks is proposed for the identification of rock discontinuity sets. By measuring the similarity of each pair of discontinuities, the local density and controlled distance of each discontinuity can be calculated for a certain cutoff distance. The number of potential clusters and central discontinuities of corresponding clusters can be found by observing the decision graph constructed based on the decision values of all discontinuities in descending order. The discontinuities of each cluster are divided into core discontinuities and outlier discontinuities based on the corresponding boundary density. This strategy can avoid interference from subjective factors and improve the accuracy of the clustering analysis. The new approach is verified using artificial data, and the appropriate cutoff distance thresholds for identifying rock discontinuity sets are given. Finally, the new approach is applied to group discontinuities in an actual underground mine. The clustering results obtained with the proposed approach are more reliable than those obtained with traditional methods.
A Fast Clustering Method for Identifying Rock Discontinuity Sets
Abstract Identifying rock discontinuity sets is a major factor for analyses of hydraulic properties and rock mass stability. To perform efficient and precise grouping of discontinuities, a new approach based on clustering by fast searches and finding density peaks is proposed for the identification of rock discontinuity sets. By measuring the similarity of each pair of discontinuities, the local density and controlled distance of each discontinuity can be calculated for a certain cutoff distance. The number of potential clusters and central discontinuities of corresponding clusters can be found by observing the decision graph constructed based on the decision values of all discontinuities in descending order. The discontinuities of each cluster are divided into core discontinuities and outlier discontinuities based on the corresponding boundary density. This strategy can avoid interference from subjective factors and improve the accuracy of the clustering analysis. The new approach is verified using artificial data, and the appropriate cutoff distance thresholds for identifying rock discontinuity sets are given. Finally, the new approach is applied to group discontinuities in an actual underground mine. The clustering results obtained with the proposed approach are more reliable than those obtained with traditional methods.
A Fast Clustering Method for Identifying Rock Discontinuity Sets
Gao, Feng (author) / Chen, Dapeng (author) / Zhou, Keping (author) / Niu, Wenjing (author) / Liu, Hanwen (author)
KSCE Journal of Civil Engineering ; 23 ; 556-566
2018-12-17
11 pages
Article (Journal)
Electronic Resource
English
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