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Bayes Discriminant Analysis Model and Its Application to the Prediction and Classification of Rockburst

FU Yu-hua1,2,DONG Long-jun1(1.School of Resources and Safety Engineering,Central South University,Changsha,Hunan 410083,China;2.School of Application and Science,Jiangxi University of Science and Technology,Ganzhou,Jiangxi 341000,China)  
A Bayes discriminant analysis(BDA) model is used to predict the possibility,and classification,of rockburst.Bayes discriminant analysis is a powerful way to classify and discriminate between objects.The main factors controlling rockburst were included in the analysis,including: tangential stress,σθ;uniaxial compressive strength,σc;uniaxial tensile strength,σt;and,elastic energy index,Wet.Three discrimination factors,σθ/σc,σc/σt,and Wet were considered to be the discriminating factors of the model.Fifteen deep rock projects located either domestically or abroad were used as the training and testing samples.Rockbursts in the Dongyu mine of Lingbao,the PingDingShan deep development opening coal mine company and the Dongguashan deep-buried hard rock mine were predicted using this model.The predicted results are consistent with the observed ones.
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