Bayes Discriminant Analysis Model and Its Application to the Prediction and Classification of Rockburst
FU Yu-hua
DONG Long-jun
Abstract：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 dis-criminate between objects. The main factors controlling rockburst were included in the analy-sis, including: tangential stress, σ_θ; uniaxial compressive strength, σ_c; uniaxial tensile strength, σ_t; and, elastic energy index, W_(et). Three discrimination factors, σ_θ/σ_c,σ_c/σ_t, and W_(et), 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.
Keywords：mining engineeringrockburstbayes discriminant analysis modelprediction
Publication Date：2009-01-01
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：6( 528-533 )