Application of Self-Organizing Feature Map in Rockburst Classification

Rockburst is one of the common dynamic disasters in underground engineering. An unsupervised learning model of rockburst clustering is suggested with the help of a self-organizing feature map (SOFM) neural network to improve the prediction accuracy of rockburst and study the latent relation between rockburst-related evaluating indicators. Firstly, six parameters, including the maximum tangential stress, uniaxial compressive strength, uniaxial tensile strength, stress coefficient, brittleness coefficient, and elastic energy index are selected as evaluation criteria. Then, 46 typical rockburst cases chosen from the literature are trained by the proposed SOFM model with its competitive layer being a 2×2 topological structure. The results suggest that the SOFM model possesses reliable clustering ability, and its positive judgment rate for the test sample is 90%. Furthermore, compared with the existing supervised learning model for rockburst prediction, the superiority of the SOFM model in this paper is verified, and some suggestions on the application of the SOFM model are given. Finally, the analysis of clustering results shows that the brittleness coefficient has a high influence on the occurrence of mild, moderate, and strong rockburst types. In addition, the distinction between strong and moderate rockburst types is not obvious by using the six evaluating parameters considered.