Identification of Rock Mass Discontinuity Sets Based on Density Peak Algorithm and Fuzzy C-Means Algorithm

Partitioning discontinuities into subsets is a crucial foundation for assessing the stability of rock masses in engineering. However, the conventional fuzzy C-means clustering algorithm (FCM) is sensitive to outliers and initial cluster centers, requires a predetermined cluster number, and tends to...

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Bibliographic Details
Published in:	Rock mechanics and rock engineering Vol. 58; no. 9; pp. 10777 - 10793
Main Authors:	Wu, Qiong, Kang, Qianqian, Tang, Huiming, Zhang, Wen, Liu, Qiang, Qin, Yue, Zhang, Chenxi, Liu, Zhiqi, Zhang, Bo, Lin, Zhiwei
Format:	Journal Article
Language:	English
Published:	Vienna Springer Vienna 01.09.2025 Springer Nature B.V
Subjects:	Accuracy Algorithms Civil Engineering Cluster analysis Clustering Datasets Density Discontinuity Earth and Environmental Science Earth Sciences Effectiveness Geology Geophysics/Geodesy Global optimization Methods Optimization Original Paper Outliers (landforms) Outliers (statistics) Partitioning Regional development Robustness (mathematics) Rock Rock masses Rocks Stability analysis Tectonics Fuzzy C-means clustering algorithm (FCM) Density peak clustering algorithm (DPC) Orientation analysis Rock mass discontinuity sets
ISSN:	0723-2632, 1434-453X
Online Access:	Get full text
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