Rockburst intensity prediction in underground buildings based on improved spectral clustering algorithm - Xia, Zhenzhao; Mao, Jingyin; He, Yao (Frontiers in Earth Science, 2022)
Rockbursts occur in many underground building constructions and have caused non-negligible casualties and property losses in underground building activities over the past hundreds of years. Effective early warning approaches to judge the practical situation of rockmass during excavation are one of the best ways to avoid rockbursts, while proposing high demands for monitoring data and computational methods. In this paper, a data-driven method based on spectral clustering to predict rockburst intensity has been proposed: Considering the fact that original spectral clustering has some defects, the improvement strategy that selects K-medoids, or an improved variant of K-medoids to replace the original K-means clustering as the latter clustering process was executed. First, the determination of hyperparameters and selections of the latter clustering algorithms were executed and improved K-medoids with related hyperparameters were determined by 65 rockburst samples collected in underground engineering cases. Based on the previous configurations of flow and hyperparameters, the remaining 14 samples were labeled by a concise labeling flow, which is also based on the spectral processes in spectral clustering. The results of the control experiments show that the proposed method has a certain feasibility and superiority (82.40% in accuracy performance) in rockburst intensity prediction for underground constructions.