Welcome to Metal Mine! Today is Share:
×

扫码分享

Modern Mining ›› 2024, Vol. 40 ›› Issue (03): 48-52.

Previous Articles     Next Articles

Short-term Rock Burst Grade Prediction Based on Bayesian Optimized XGBoost Model

In order to further improve the accuracy of rock-burst short-term prediction，the ratio of to⁃ tal maximum principal stress to uniaxial compressive strength of rock，supporting conditions，excavation span，geological conditions，micro seismic magnitude and peak particle velocity were selected as the dynam⁃ ic and static indicators for rock burst grade prediction. XGBoost algorithm under Bayesian optimization is employed to build prediction model，and the sensitive analysis is studied. The predictive results of Bayesian optimized XGBoost model is then compared with XGBoost，Random forest，Neural network and Decision tree models. The results show that the Bayesian optimized XGBoost model has higher accuracy and reliabili⁃ ty，and the accuracy for short-term rock burst grade prediction is 74.67%. Peak particle velocity，excava⁃ tion span and the ratio of total maximum principal stress to uniaxial compressive strength of rock are more sensitive to rock-burst grade. The results can provide certain theoretical basis and decision tool for short⁃ term rock burst grade prediction in site.   

  1. 1. School of Mining Engineering，Anhui University of Science and Technology; 2. State Key Laboratory of Abstract Mining Response and Disaster Prevention and Control in Deep Coal Mines
  • Online:2024-03-18 Published:2024-08-15

PDF (PC)

30

Abstract: In order to further improve the accuracy of rock-burst short-term prediction，the ratio of to⁃ tal maximum principal stress to uniaxial compressive strength of rock，supporting conditions，excavation span，geological conditions，micro seismic magnitude and peak particle velocity were selected as the dynam⁃ ic and static indicators for rock burst grade prediction. XGBoost algorithm under Bayesian optimization is employed to build prediction model，and the sensitive analysis is studied. The predictive results of Bayesian optimized XGBoost model is then compared with XGBoost，Random forest，Neural network and Decision tree models. The results show that the Bayesian optimized XGBoost model has higher accuracy and reliabili⁃ ty，and the accuracy for short-term rock burst grade prediction is 74.67%. Peak particle velocity，excava⁃ tion span and the ratio of total maximum principal stress to uniaxial compressive strength of rock are more sensitive to rock-burst grade. The results can provide certain theoretical basis and decision tool for short⁃ term rock burst grade prediction in site.

Key words: