Dominant Partitioning of Rock Masses Discontinuities Based on Information Entropy Selective Heterogeneous Ensemble

In order to make up for the shortcomings of traditional discontinuity occurrence clustering algorithms, such as narrow application scope, difficulty in effectively identifying noise points, and susceptibility to initial conditions and hyperparameters. In this work, the dominant partitioning of rock masses discontinuities based on information entropy selective heterogeneous ensemble (DRIE) model is proposed. Firstly, the base cluster set is generated by four base-clustering algorithms. Secondly, the comprehensive quality evaluation index is obtained by evaluating the quality and difference of the base cluster, and the information entropy weighting matrix is integrated. Finally, the number of integrated base clusters and the optimal number of groups are determined, and the noise points are eliminated. In addition, this work lists four base-clustering algorithm models, establishes nine comparison models, and applies all models to the dominant partitioning of an adit discontinuity of Songta Hydropower Station in Tibet, China. The results show that the DRIE model and the above 13 models are evaluated by combining the silhouette index and the ability to identify and eliminate noise. Compared with other models, the DRIE model has wide applicability and objective grouping results. It provides a new idea for the study of the dominant occurrence grouping of discontinuity.