Multi-index dominant grouping of rock mass discontinuities based on the combined weighting method: A case study for the Huayang tunnel

Wang, Shuhong; Dong, Furui; Zhang, Ze; Yang, Bo; Hou, Qinkuan

doi:10.1016/j.tust.2023.105211

Cited by 5 publications

(1 citation statement)

References 40 publications

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“…In addition, these two methods are local optimal algorithms, and the final grouping result is often local optimal, rather than global optimal. At the same time, these algorithms still need to determine the number of rock mass discontinuities in advance, and it is difficult to identify the noise points and solitary values in the occurrence of discontinuities [27,28].…”

Section: Introductionmentioning

confidence: 99%

Dominant Partitioning of Discontinuities of Rock Masses Based on DBSCAN Algorithm

et al. 2023

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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.

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Section: Introductionmentioning

confidence: 99%