HIGHLIGHTS
SUMMARY
Shield tunnelling data are the input of such identification models, and geological types or geological parameters are the output, but the definitions of geological type and the level of refinement of category division vary. Zhang et_al17 used a chisquare test to screen out the seven tunnelling parameters most sensitive to geological changes and constructed a geological-type prediction model using a classification and regression tree algorithm based on data from geological exploration. Shi et_al18 used a deep neural_network (DNN) model to predict seven geological types of excavation faces using 53 shield excavation parameters as . . .
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