Rockburst Prediction Model Based on Entropy Weight Integrated with Grey Relational BP Neural Network

Abstract

"Arockburstpredictionmodeloftheentropyweightgreyrelationalbackpropagation(BP)neuralnetworkisdeveloped.Themodel needs to select the evaluation factors according to the engineering practice and establish the sample library. The entropy weight methodisusedtocalculatetheobjectiveweightofthecharacteristicfactors,andthesimilaritybetweenthesamplesiscalculatedby the combination of grey relational theory and the entropy method. The training sample of the BP neural network is selected by threshold determination. Finally, we use the trained neural network to estimate the rockburst intensity grade of samples to be tested. This model is applied to the rockburst prediction of Qamchiq tunnel project, and the prediction results are in good agreementwiththeactualconditionsofthesubsequentconstruction,thusverifyingthefeasibilityandeffectivenessofthemodelin the rockburst prediction. "