基于凝聚层次聚类分析法的岩体随机结构面产状优势分组

在岩体斜坡稳定性分析和岩体水力学分析中,岩体随机结构面的优势分组是一项十分重要的内容。提出一种基于凝聚层次聚类分析的岩体随机结构面产状优势分组的新方法,这种方法的优点在于事先无需确定聚类中心,在分类结果生成后还可明显剔除数据的孤点与野值。应用人工随机生成的结构面产状数据对这种新方法和模糊C均值法进行了对比验证。结果表明,凝聚层次聚类分析法不仅在无孤值点的情况下分组结果优于模糊C均值算法,而且还可以有效地剔除孤值点对于分组结果的不利影响。最后将这种方法应用于松塔水电站坝肩结构面优势分组中,同样得到了比较满意的结果。 方法

Dominant partitioning of discontinuities of rock masses based on AGNES

A large number of random discontinuities are widely distributed in rock masses and have significant influences on the mechanical and hydraulic properties of fractured rock masses. In the analysis of the mechanical and hydraulic properties of fractured rock masses, the dominant partitioning of discontinuities of rock masses is an important part, and it is still a key for establishing the three-dimensional (3-D) network model of random discontinuities. A new method is proposed for the dominant partitioning of discontinuities of rock mass based on AGNES. 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. Through the comparison of the proposed method and the fuzzy C-means method applied in the artificial and randomly generated data of discontinuities, the following conclusions can be drawn. The proposed method is a better method than the fuzzy C-means method in general cases, and it can get more accurate results by eliminating the acnodes or outliers. Finally, the proposed method is applied to a practical project, and the results are shown to be satisfactory.