Abstract Bayes discriminative analysis was adopted to predict rockburst grades, and based on 108 rockburst cas? es, a Bayes discriminative model was built with six basic indicators such as maximum tangential stress of the surrounding rock σθ, uniaxial compressive strength σc , uniaxial tension strength σt , stress coefficient σθ/σc , brittleness coefficient σc/σt and elastic energy indicator Wet. In light of the Bayes discriminative model, the concept of odds ratio was proposed and used in the analysis of relationships between rockburst indicators and rockburst grades,and the analysis of the sensitivity of rockburst grades to indicators, then the reasonableness of the Bayes discriminative model with different indicator combinations was evaluated. The results show that the Bayes discriminative model with the combination of indicators σθ/σc and Wet was the most reasonable with high prediction effects, and the obtained expression for rockburst grade probability can be a reference for predicting rockburst grades in underground engineering.
Abstract：
Bayes discriminative analysis was adopted to predict rockburst grades, and based on 108 rockburst cas? es, a Bayes discriminative model was built with six basic indicators such as maximum tangential stress of the surrounding rock σθ, uniaxial compressive strength σc , uniaxial tension strength σt , stress coefficient σθ/σc , brittleness coefficient σc/σt and elastic energy indicator Wet. In light of the Bayes discriminative model, the concept of odds ratio was proposed and used in the analysis of relationships between rockburst indicators and rockburst grades,and the analysis of the sensitivity of rockburst grades to indicators, then the reasonableness of the Bayes discriminative model with different indicator combinations was evaluated. The results show that the Bayes discriminative model with the combination of indicators σθ/σc and Wet was the most reasonable with high prediction effects, and the obtained expression for rockburst grade probability can be a reference for predicting rockburst grades in underground engineering.
2017, Vol. 54 