The scientific prediction of the TBM penetration rate is of great significance to the selection of hydraulic tunnel construction methods

construction schedule and cost estimation. In view of the high nonlinearity

fuzziness and complexity of TBM excavation process

and in order to improve the prediction accuracy and computational efficiency

the partial least squares regression (PLSR) has been applied to extract the principal components of the influencing parameters. Then the deep neural network (DNN) is employed to train and forecast the TBM penetration rate. A prediction model of TBM penetration rate based on the coupled method of PLSR and DNN is proposed. Based on the measured data of the double-shield TBM construction of a water conveyance tunnel in the Lanzhou water source construction project

six impact parameters including the rock uniaxial compressive strength

rock uniaxial tensile strength

cutter head thrust

cutter head speed

rock mass integrity coefficient and rock Cerchar abrasiveness index are selected to verify the prediction reasonability of the model. The fitting and prediction accuracy of the different prediction methods are compared and analyzed. The research results show that the PLSR can effectively overcome the problem of multiple collinearity between the independent variables. The extracted principal components are trained as the input layer of the DNN

which simplifies the structure of the neural network. The PLSR-DNN coupled model effectively avoids the over-fitting and inadequate fitting problems. It has the characteristics of fast convergence

stable solution and high fitting accuracy. The average relative fitting error of the PLSR-DNN prediction model is 2.96%

and the average relative prediction error is 3.27%. The fitting accuracy and prediction accuracy of the PLSR-DNN prediction model is significantly higher than those of PLSR model alone

BP neural network model and SVR model

respectively.