Rockburst prediction for hard rock and deep-lying long tunnels based on the entropy weight ideal point method and geostress field inversion: a case study of the Sangzhuling Tunnel

Abstract

To evaluate rockbursts in deep-lying long tunnels, a multiple factor analysis and predictions were conducted. It is necessary to establish whether the primary evaluation indexes cover the entire development–occurrence–evolution process for rockbursts and how best to determine the weights of the final selected indexes. We developed an evaluation model with attribute reduction and chose 5 out of 11 primary evaluation indexes to cover the typical characteristics of energy storage, rockburst proneness, and risk of failure. The weights of the primary evaluation indexes and the offset distance were then determined using the entropy weight ideal point method. Combining geostress field inversion and rock mechanic tests, the evaluation model was applied to the case of the Sangzhuling Tunnel along the Sichuan–Tibet railway. Rockburst prediction results achieved 41.2% and 94.1% accuracy when using the Manhattan and Euclidean distance functions, respectively. A more specific classification of the evaluation indexes may optimize the weight assignment and help obtain more accurate results. This paper provides a reliable method for rockburst predictions for hard rock and deep-lying long tunnels, which may have good prospects for engineering applications.