Prediction of TBM tunnelling parameters based on IPSO-BP hybrid model

It is of great significance to predict the TBM tunnelling parameters in stable phase based on the data of the rising phase, which can predict the recommended values of the tunnelling parameters at the early phase of each tunnelling cycle and assist to set and optimize the TBM tunnelling parameters. A TBM tunnelling parameter prediction model based on improved particle swarm integrated back propagation(IPSO-BP) is proposed, in which the standard PSO algorithm is improved by using adaptive inertia weight and the connection weight and bias of BP network are optimized based on improved PSO algorithm. Based on the 802-day TBM operation data of Songhua River water conveyance project, the training and test sets are divided. The variation characteristics(mean value and linear fitting slope) of cutterhead torque, penetration, cutterhead power, advance rate and total thrust in the first 30 s of TBM rising phase, as well as three geological parameters(i.e., lithology, surrounding rock level and groundwater level) are selected as the inputs of IPSO-BP model. Three key hyper-parameters including number of the hidden layer nodes, learning rate and population size are determined by experimental method, and the advance rate v, total thrust F and cutterhead torque T in stable phase are predicted. The results show that the R2 of the proposed model is over 0.85 and the mean absolute percentage error is less than 12.68%. Compared with BP and PSO-BP models, the proposed model has higher prediction accuracy. © 2020, Science Press. All right reserved.