Intelligent prediction of rockburst in tunnels based on back propagation neural network integrated beetle antennae search algorithm.

Abstract

Rockburst is one of the major engineering geological disasters of underground engineering. Accurate rockburst intensity level prediction is vital for disaster control during underground tunnel construction. In this work, a hybrid model integrating the back propagation neural network (BPNN) with beetle antennae search algorithm (BAS) has been developed for rockburst prediction. Before model building, 173 groups of rockburst dataset were collected. Six geological parameters are selected as predictors for rockburst, including the maximum tangential stress of the surrounding rock σθ, the uniaxial compressive strength of rock σc, the tensile strength of rock σt, the stress ratio σθ/σc, the rock brittleness ratio σc/σt, and the elastic energy index Wet. After preprocessed by outlier detection and synthetic minority oversampling technique (SMOTE), the new dataset was divided into training and test parts. BAS could optimize the weights and biases of BPNN from the training process. Then the established hybrid model was applied to the test samples with predicted accuracy of 94.3%, proving that the hybrid model has practical value in researching rockburst prediction.