Sparrow search algorithm-driven clustering analysis of rock mass discontinuity sets

Rock discontinuity has a crucial impact on the deformation and strength of rock masses, and thus, the clustering of discontinuities is a critical aspect of rock mechanics. Traditional clustering methods require initial cluster centers to be specified and involve a multitude of parameter calculations, leading to a complex and cumbersome process. In this paper, a novel clustering approach based on the sparrow search algorithm (SSA) is introduced to overcome these limitations. This method utilizes a sparrow population coding technique and fitness function tailored to the unique characteristics of rock discontinuity orientation data. The SSA is adeptly applied to the clustering of rock joints, and the optimal number of clusters are automatically determined via the silhouette coefficient method. This methodology was tested on artificial datasets and actual discontinuity survey results from the underground powerhouse of the Henan Wuyue Hydropower Station to evaluate its feasibility and efficacy in analyzing rock discontinuities. Comparative data analysis reveals that the proposed method outperforms classic algorithms such as FCM and KPSO in terms of clustering accuracy and stability. The proposed method stands out among various clustering methods of discontinuity orientation for its ability to achieve convergent results without user intervention, demonstrating significant practical utility.