Abstract
Under high-stress conditions, rock burst disasters can significantly impact underground civil engineering construction. For underground metal mines, rock burst evaluations and prevention during mining have become major research topics, and the prediction and prevention of rock burst must be based on the study of rocks and rock burst tendencies. To further prevent the risk of geological disasters and provide timely warnings, a finite-interval cloud model based on the CRITIC algorithm is proposed in this paper to address the uncertainty of rock burst evaluation, the complexity under multi-factor interactions, and the correlations between factors, and it then realizes a preliminary qualitative judgment of rock burst disasters. This paper selects the uniaxial compressive strength σc (I1), ratio of the uniaxial compressive strength to the tensile strength σc/σt (brittleness coefficient, I2), elastic deformation energy index Wet (I3), ratio of the maximum tangential stress to the uniaxial compressive strength σθ /σc (stress coefficient, I4) of the rock, depth of the roadway H (I5), and integrity coefficient of the rock mass Kv (I6) as indicators for rock burst propensity predictions. The CRITIC algorithm is used to consider the relationships between the evaluation indicators, and it is combined with an improved cloud model to verify 20 groups of learning samples. The calculation results obtained by the prediction method are basically consistent with the actual situation. The validity of the model is tested, and then the model is applied to the Dongguashan Copper Mine in Tongling, Anhui Province, China, for rock burst evaluation.
Highlights
Rock burst is due to the impact of ground pressure on hard and brittle rock masses in high earth-stress states during the excavation of underground tunnels
This paper uses the improved CRITIC method-normal cloud model for rock burst propensity prediction to determine the weight of the index
The improved CRITIC algorithm is based on the original calculation steps, adding the concept of coefficient of variation (Formula 12), thereby reducing the shortcomings of using standard deviation to measure the variability of indicators [56], the main steps are as follows: STEP 1: Using the initial data, establish a matrix of predicted sample indicator values: X xij m×n where xij is the original value corresponding to the jth indicator of the ith evaluation object
Summary
Rock burst is due to the impact of ground pressure on hard and brittle rock masses in high earth-stress states during the excavation of underground tunnels. This paper uses the improved CRITIC method-normal cloud model for rock burst propensity prediction to determine the weight of the index. The improved CRITIC algorithm is based on the original calculation steps, adding the concept of coefficient of variation (Formula 12), thereby reducing the shortcomings of using standard deviation to measure the variability of indicators [56], the main steps are as follows: STEP 1: Using the initial data, establish a matrix of predicted sample indicator values:. (1) With reference to the rock burst risk level classification standard and cloud model concept, determine the number and interval of the divided states. (2) According to the numerical characteristics of the cloud model (Eq 7) and the grading standard of the rock burst intensity level, the numerical eigenvalues of different hazard levels of different evaluation factors can be obtained. I (no rock burst) II (weak rock burst) III (moderate rock burst) IV (violent rock burst)
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