Abstract
Abstract Rockbursts are common geological disasters in underground engineering, and rockburst proneness evaluation is an important research subject. In this study, a multidimensional cloud model was used to evaluate the rockburst proneness level, and the control variable method was used to establish 15 multidimensional cloud (MC) models. The key factors affecting the accuracy of multidimensional cloud model evaluation are numerical characteristics, weight, and normalization methods. The optimal numerical characteristics calculation method of a multidimensional cloud model was determined, and an improved CRITIC (IC) weight method was optimised by introducing the relative standard deviation and an improved quantisation coefficient. Six rockburst indexes were used as input for the multidimensional cloud model, including the elastic deformation energy index Wet, maximum tangential stress σθMPa of a cavern, uniaxial compressive strength σcMPa, uniaxial tensile strength στMPa, strength brittleness coefficient B1=σc/στ, and stress coefficient σθ/σc. The model was used to learn 271 groups of complete rockburst cases, and the MC-IC rockburst proneness evaluation method was established. The performance of the proposed MC-IC rockburst proneness method was verified by an 8-fold cross-validation and confusion matrix (precision, recall, F1). The method was tested to evaluate 20 groups of rockburst cases from the Jiangbian Hydropower Station, and the accuracy reaches 95%. The evaluation results were compared with three empirical criteria: four cloud-based methods, three unsupervised learning methods, and four supervised learning methods; the accuracy of the method established in this paper is 93.33%. The results showed that the MC-IC method had an excellent performance in evaluating rockburst proneness and can provide a practical basis for identifying rockburst hazard areas in deep engineering.
Highlights
Rockburst is one of the most severe geological disasters in deep diversion tunnels, the deep mining of metal mines, underground water-sealed oil storage caverns, and the deep burial treatment of nuclear waste
Six indexes, including Wet, σθðMPaÞ, σcðMPaÞ, στðMPaÞ, B1 = σc/στ, and B1 = σθ/σc, were used as inputs, and the rockburst level was used as an output in the multidimensional cloud (MC)-improved CRITIC (IC) method
The results clearly show that the MC-IC method’s performance in rockburst proneness evaluation was excellent
Summary
Rockburst is one of the most severe geological disasters in deep diversion tunnels, the deep mining of metal mines, underground water-sealed oil storage caverns, and the deep burial treatment of nuclear waste. A rockburst is a sudden collapse of a surrounding rock mass accompanied by violent shock waves that quickly releases enormous amounts of energy. It has the characteristics of instantaneity and strong destructive power [1,2,3] and poses a significant threat to personnel safety and equipment. Tao et al [11, 12] obtained the dynamic stress concentration around the hole from the perspective of wave theory and the dynamic stress concentration and the initial stress superposition of original rocks for the first time caused by dynamic perturbation scattering through the combined dynamic and static action test device and proved that this is the main reason for the occurrence of rockburst around the chamber. Ren et al [15]
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