Dynamic Cluster Grouping Analysis of Occurrence of Structural Plane in Natural Rock Mass

AI Chun-ming, LU Yun, SUN Ping-ping

Journal of Changjiang River Scientific Research Institute ›› 2023, Vol. 40 ›› Issue (5) : 106-110.

PDF(3505 KB)
PDF(3505 KB)
Journal of Changjiang River Scientific Research Institute ›› 2023, Vol. 40 ›› Issue (5) : 106-110. DOI: 10.11988/ckyyb.20211311
Rock-Soil Engineering

Dynamic Cluster Grouping Analysis of Occurrence of Structural Plane in Natural Rock Mass

  • AI Chun-ming1,2, LU Yun3, SUN Ping-ping1,2
Author information +
History +

Abstract

The grouping of natural structural planes is critical in rock mass engineering. Traditional grouping methods are inaccurate in grouping structural planes with near-vertical or near-horizontal occurrence. In view of this, a dynamic clustering analysis method for structural planes is proposed. The clustering center is determined based on the dip angle and inclination of structural planes, and the grouping result is obtained by iterative calculation based on the angle between structural planes. The method was applied to group 303 structural planes in Bayan Obo East mine and compared with the fast clustering method. Three dominant groups of structural planes were identified after the grouping of 303 structural planes, which agree well with the isodensity map of structural plane occurrence. The dynamic clustering analysis method is not only simple and easy to calculate but also overcomes the limitations of traditional methods in quantification, making the grouping of structural planes more accurate and practically significant.

Key words

natural rock mass / structural plane / dominant occurrence / dynamic clustering

Cite this article

Download Citations
AI Chun-ming, LU Yun, SUN Ping-ping. Dynamic Cluster Grouping Analysis of Occurrence of Structural Plane in Natural Rock Mass[J]. Journal of Changjiang River Scientific Research Institute. 2023, 40(5): 106-110 https://doi.org/10.11988/ckyyb.20211311

References

[1] 张荣春, 衣雪峰, 李 浩, 等. 多源数据多语义岩体结构面提取方法[J]. 测绘通报, 2021(11): 76-80, 105.
[2] 姚文生.基于竞争性神经网络在岩质边坡参数随机结构面优势分组中的应用[J].矿产与地质,2021,35(4): 799-804.
[3] 王述红, 朱宝强, 王鹏宇. 模拟退火聚类算法在结构面产状分组中的应用[J]. 东北大学学报(自然科学版), 2020, 41(9): 1328-1333.
[4] 王述红, 任艺鹏, 陈俊智, 等. 一种改进鱼群聚类算法在结构面分组中的应用[J]. 东北大学学报(自然科学版), 2019, 40(3): 420-424.
[5] 许 扬, 张 文, 符 锐, 等. 基于混合聚类的岩体结构面优势分组方法[J]. 世界地质, 2020, 39(1): 113-120.
[6] 王俊智, 杜朋召, 牛兆轩. 基于K-means聚类方法和Ⅰ Index聚类有效性检验指标的岩体结构面自动分组及应用[J]. 长江科学院院报, 2018, 35(9): 109-113, 120.
[7] 伏 坤, 王 珣, 刘 勇, 等. 基于K近邻改进密度峰值聚类分析法的岩体结构面产状优势分组[J]. 水利水电技术, 2019, 50(11): 124-130.
[8] 徐倚晴, 郝朝阳, 权雪瑞. 基于一种改进K-means聚类方法的岩体结构面优势分组研究[J]. 世界有色金属, 2019(21): 288-289.
[9] 李运生,宋金龙,李 煜,等. 基于凝聚层次聚类的K均值结构面产状分组[J].人民长江,2018,49(6):44-49.
[10] SUN S Q, HUANG R Q, PEI X J, et al. Engineering Geological Classification of the Structural Planes for Hydroelectric Projects in Emeishan Basalts[J]. Journal of Mountain Science, 2016, 13(2): 330-341.
[11] BHARGAVI M S, GOWDA S D. A Novel Validity Index with Dynamic Cut-off for Determining True Clusters[M]. New York: Elsevier Science Inc.,2015.
[12] 王同兴, 郭骏杰, 王 强. 基于K均值动态聚类分析的土样识别[J]. 建筑科学, 2010, 26(7): 52-56, 71.
[13] FENG F, LI X, ROSTAMI J, et al. Modeling Hard Rock Failure Induced by Structural Planes around Deep Circular Tunnels[J]. Engineering Fracture Mechanics, 2019, 205: 152-174.
[14] 王俊杰, 冯 登, 柴贺军, 等. 基于赤平极射投影和K-均值聚类算法的优势结构面分析[J]. 岩土工程学报, 2018, 40(1): 74-81.
PDF(3505 KB)

Accesses

Citation

Detail

Sections
Recommended

/