Dominant Partitioning of Discontinuities of Rock Masses Based on DBSCAN Algorithm
Source abstract
Abstract
In the analysis of rock slope stability and rock mass hydraulics, the dominant partitioning of discontinuities of rock masses is a very important concept, and it is still a key for establishing the three-dimensional (3-D) network model of random discontinuities. The traditional graphical analysis method is inadequate and greatly influenced by subjective experience. A new method using density-based spatial clustering of applications with noise (DBSCAN) algorithm is proposed for the dominant partitioning of discontinuities of rock mass. In the proposed method, we do not need to determine the centers of every cluster before clustering, and the acnodes or outliers can be eliminated effectively after clustering. Firstly, the spatial coordinate transformation of the discontinuity occurrence is carried out and the objective function is established by using the sine value of the angle of the unit normal vector as the similarity measure standard. The DBSCAN algorithm is used to establish the optimal clustering centers by searching the global optimal solution of the objective function, and the fuzzy C-means clustering algorithm is optimized and the mathematical model of the advantage grouping of rock discontinuities is established. The new method and the fuzzy C-means method are compared and verified by using the artificially randomly generated discontinuity occurrence data. The proposed method is a better method than the fuzzy C-means method in general cases, and it can provide more accurate results by eliminating the acnodes or outliers. Finally, the proposed method is applied to discontinuity orientation partition data at Maji dam site, Nujiang River, and there is good agreement with the in situ measurement.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Landslides and related hazards · Dam Engineering and Safety · Geotechnical Engineering and Analysis
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
College of Transportation and Civil Engineering, Fujian Agriculture and Forestry University, Fuzhou 350002, China
Fujian Communications Planning and Design Institude Co., Ltd., Fuzhou 350004, China
School of River and Ocean Engineering, Chongqing Jiaotong University, Chongqing 400074, China
Funding
National Natural Science Foundation of China · Natural Science Foundation of Fujian Province, China · Natural Science Foundation of Chongqing, China
References (53)
Show source-provided references
1. Wu, Q. (2012). The Mechanical Parameters of Jointed Rock Mass: Scale-Effect Research and Its Engineering Application. [Ph.D. Dissertation, China University of Geosciences]. (In Chinese).
2. Development and verification of threedimensional equivalent discrete fracture network modelling based on the finite element method10.1016/j.enggeo.2022.106759 ↗
3. A progressive framework for delineating homogeneous domains in complicated fractured rock masses: A case study from the Xulong Dam site, China10.1007/s00603-019-01999-y ↗
4. Reliability analysis of jointed rock slopes using updated probability distributions of structural plane parameters
5. Automated structural discontinuity mapping in a rock face occluded by vegetation using mobile laser scanning10.1016/j.enggeo.2021.106040 ↗
6. Khayrutdinov, M.M., Golik, V.I., Aleksakhin, A.V., Trushina, E.V., Lazareva, N.V., and Aleksakhina, Y.V. (2022). Proposal of an Algorithm for Choice of a Development System for Operational and Environmental Safety in Mining. Resources, 11.10.3390/resources11100088 ↗
7. Stress-strain behavior model of disturbed rock mass with regard to anisotropy and discontinuities10.25018/0236_1493_2021_8_0_93 ↗
8. Geomechanical substantiation of parameters of technology for mining salt deposits with a backfill
9. Models and algorithms of choice of development technology of deposits when selecting the composition of the backfilling mixture10.1088/1755-1315/684/1/012008 ↗
10. XXIII. Gefügestatistik10.1007/BF02993943 ↗
11. Delineation and analysis of clusters in orientation data10.1007/BF01039681 ↗
12. Mahtab, M.A., and Yegulalp, T.M. (1982, January 25–27). A rejection criterion for definition of clusters in orientation data. Proceedings of the 22nd US Symposium Rock Mechanics, Berkeley, CA, USA.
13. Study on the dominant orientations of random fractures of fractured rock masses
14. A new clustering approach for partitioning directional data10.1016/j.ijrmms.2004.08.011 ↗
15. Comprehensive characterization and clustering of orientation data: A case study from the Songta Dam site10.1016/j.enggeo.2017.01.010 ↗
16. Hudson, J.A. (1992). Rock Characterization: International Society of Rock Mechanics (ISRM) Symposium, Eurock’92, Chester, UK, 14–17 September 1992, Thomas Telford, British Geotechnical Society.
17. Fuzzy cluster algorithm for the automatic identification of joint sets10.1016/S0148-9062(98)00011-4 ↗
18. On distance measures for the fuzzy K-means algorithm for joint data
19. Implementation of multivariate clustering methods for characterizing discontinuities data from scanlines and oriented boreholes10.1016/S0098-3004(01)00111-X ↗
20. A spectral method for clustering of rock discontinuity sets10.1016/j.ijrmms.2006.02.003 ↗
21. Fuzzy spectral clustering for identification of rock discontinuity sets10.1007/s00603-007-0155-6 ↗
22. Data analysis method for optimized and dominant orientations of joints in rock mass
23. Fuzzy C-means cluster analysis based on rough set for grouping of discontinuities
24. Cluster analysis of discontinuity occurrence of rock mass based on improved genetic algorithm
25. Ant colony ATTA clustering algorithm of rock mass structural plane in groups10.1007/s11771-014-1992-6 ↗
26. Dominant partitioning of discontinuities of rock masses based on AGNES
27. Dominant partitioning method of rock mass discontinuity based on DBSCAN selective clustering ensemble
28. Multi-index dominant grouping of rock mass discontinuities based on the combined weighting method: A case study for the Huayang tunnel10.1016/j.tust.2023.105211 ↗
29. Probabilistic tunnel collapse risk evaluation model using analytical hierarchy process (AHP) and Delphi survey technique10.1016/j.tust.2021.104262 ↗
30. Detection of discontinuity pattern and orientation for tunnel faces by using 2D and 3D image analysis techniques10.1016/j.pce.2022.103330 ↗
31. Effects of discontinuities on stress redistribution and rock failure: A case of underground caverns10.1016/j.tust.2022.104583 ↗
32. Wang, W.T., Wu, Y.L., Tang, C.Y., and Hor, M.K. (2015, January 12–15). Adaptive density-based spatial clustering of applications with noise (DBSCAN) according to data. Proceedings of the 2015 International Conference on Machine Learning and Cybernetics (ICMLC), Guangzhou, China.10.1109/ICMLC.2015.7340962 ↗
33. Battulwar, R., Emami, E., Naghadehi, M.Z., and Sattarvand, J. (2020). Advances in Visual Computing: 15th International Symposium, Diego, CA, USA, 5–7 October 2020, Proceedings, Part II, Springer.
34. Dowding, C.H., and Singh, M.M. (1984, January 25–27). A similarity test for grouping orientation data in rock mechanics. Proceedings of the 25th US Symposium on Rock Mechanics, Evanston, IL, USA.
35. Fuzzy C-means cluster analysis based on mutative scale chaos optimization algorithm for the grouping of discontinuity sets10.1007/s00603-012-0244-z ↗
36. K-means algorithm based on particle swarm optimization for the identification of rock discontinuity sets10.1007/s00603-014-0569-x ↗
37. Determining the equivalent permeability tensor for fractured rock masses using a stochastic REV approach: Method and application to the field data from Sellafield, UK10.1007/s10040-004-0331-7 ↗
38. Feature weighting in DBSCAN using reverse nearest neighbours10.1016/j.patcog.2023.109314 ↗
39. Sharp Curve Detection of Autonomous Vehicles using DBSCAN and Augmented Sliding Window Techniques
40. Improved DBSCAN algorithm based signal recovery technology in coherent optical communication systems10.1016/j.optcom.2022.128590 ↗
41. Research on WPD and DBSCAN-L-ISOMAP for circuit fault feature extraction10.1515/phys-2022-0254 ↗
42. Accurate and real-time object detection in crowded indoor spaces based on the fusion of DBSCAN algorithm and improved YOLOv4-tiny network
43. Parallel DBSCAN Clustering Algorithm Using Hadoop Map-reduce Framework for Spatial Data
44. A Fuzzy Clustering Validity Index Induced by Triple Center Relation10.1109/TCYB.2023.3263215 ↗
45. A validity measure for fuzzy clustering10.1109/34.85677 ↗
46. Pramanik, A., Das, A.K., Pelusi, D., and Nayak, J. (2023). An Effective Fuzzy Clustering of Crime Reports Embedded by a Universal Sentence Encoder Model. Mathematics, 11.10.3390/math11030611 ↗
47. Fuzzy C-Means Clustering Validity Function Based on Multiple Clustering Performance Evaluation Components10.1007/s40815-021-01243-2 ↗
48. A fuzzy clustering algorithm influenced by validity indices (FCVI) for recognizing the differentially expressed cancer mediating genes10.1016/j.mgene.2019.100615 ↗
49. An adaptive threshold fast DBSCAN algorithm with preserved trajectory feature points for vessel trajectory clustering10.1016/j.oceaneng.2023.114930 ↗
50. Enhanced technology for sewage sludge advanced dewatering from an engineering practice perspective: A review10.1016/j.jenvman.2022.115938 ↗
51. Arauz-Garofalo, G., Jodar, M., Vilanova, M., de la Iglesia Rodriguez, A., Castillo, J., Soler-Ventura, A., Oliva, R., Vilaseca, M., and Gay, M. (2021). Protamine Characterization by Top-Down Proteomics: Boosting Proteoform Identification with DBSCAN. Proteomes, 9.10.3390/proteomes9020021 ↗
52. Coupling analysis of the heat-water dynamics and frozen depth in a seasonally frozen zone10.1016/j.jhydrol.2020.125603 ↗
53. Liu, Y., Zhang, L., Li, P., Jia, T., Du, J., Liu, Y., Li, R., Yang, S., Tong, J., and Yu, H. (2023). Laser Radar Data Registration Algorithm Based on DBSCAN Clustering. Electronics, 12.10.3390/electronics12061373 ↗