Research on Rockburst Classification Prediction Based on BP-SVM Model

Rockburst is a complex destabilization phenomenon which is a combination of multiple factors, the study of rockburst for classification prediction can help prevent and control engineering geological hazards, reduce casualties and property damage. To achieve efficient and accurate rockburst classification prediction and solve the problem of rockburst propensity assessment, six evaluation factors are selected as the rock explosion prediction and evaluation system: tangential stress
$\sigma _{\theta }$
, uniaxial compressive strength
$\sigma _{\mathrm {c}}$
, uniaxial tensile strength
$\sigma _{\mathrm {t}}$
, tangential stress to uniaxial compressive strength ratio
$\sigma _{\mathrm {\theta }}/\sigma _{\mathrm {c}}$
(BCF), uniaxial compressive strength to tensile strength ratio
$\mathrm {\sigma }_{\mathrm {c}}/\sigma _{\mathrm {t}}$
(SCF), and elastic deformation energy index
$\mathrm {W}_{\mathrm {et}}$
in this study. Widely collected domestic and international groups of rock explosion evaluation data, and 420 sets of valid samples were obtained by data processing. Establish rockburst grading prediction evaluation models based on BP neural networks and support vector machines respectively, then establish BP-SVM prediction models based on arithmetic mean weights and standard deviation weights, analyzing and comparing the prediction rating results of 120 groups of samples among them. Accuracy, Precision, Recall, Specificity, and F1 Score metrics are selected to evaluate the performance of different models, the results show that several models can obtain effective prediction results, among which the standard deviation weight combination BP-SVM model proposed in this paper has the best prediction accuracy and the best effect, which is better than the traditional single machine learning method.