Real-time analysis and prediction of shield cutterhead torque using optimized gated recurrent unit neural network

An accurate prediction of earth pressure balance(EPB)shield moving performance is important to ensure the safety tunnel excavation.A hybrid model is developed based on the particle swarm optimization(PSO)and gated recurrent unit(GRU)neural network.PSO is utilized to assign the optimal hyperparameters of GRU neural network.There are mainly four steps:data collection and processing,hybrid model establishment,model performance evaluation and correlation analysis.The developed model provides an alternative to tackle with time-series data of tunnel project.Apart from that,a novel framework about model application is performed to provide guidelines in practice.A tunnel project is utilized to evaluate the performance of proposed hybrid model.Results indicate that geological and construction variables are significant to the model performance.Correlation analysis shows that construction variables(main thrust and foam liquid volume)display the highest correlation with the cutterhead torque(CHT).This work provides a feasible and applicable alternative way to estimate the performance of shield tunneling.

A new calculation model of cutterhead torque and investigation of its influencing factors

Cutterhead torque is a crucial parameter for the design and operation of earth pressure balance （EPB） shield tunneling machine. However, the traditional calculation models of cutterhead torque are too rough or exist gross errors under variable geological conditions. In order to improve the precision of the calculation model of cutterhead torque, dynamic operation parameters are considered and a new model is proposed. Experiment is carried out on a ~1.8 m shield machine test rig and the calculating re- sult with the new model is compared with the experimental data to verify the validity of the new model. The relative error of the new model is as low as 4% at smooth stage and is reduced to 5% at the end of trembling stage. Based on the results of the new model and the test data obtained from the 001.8 m test rig and the construction site, the inner relationships between several operation parameters and cutterhead torque are investigated and some quantitative conclusions are obtained.

TBM performance prediction using LSTM-based hybrid neural network model:Case study of Baimang River tunnel project in Shenzhen,China

Accurately predicting tunnel boring machine(TBM)performance is beneficial for excavation efficiency enhancement and risk miti-gation of TBM tunneling.In this paper,we develop a long short-term memory(LSTM)based hybrid intelligent model to predict two key TBM performance parameters(advance rate and cutterhead torque).The model combines the LSTM,BN,Dropout and Dense layers to process the raw data and improve the fitting quality.The features,including the ground formation properties,tunnel route cur-vature,tunnel location and TBM operational parameters,are divided into historical/real-time time-varying parameters,time-invariant parameters and historical/real-time output prediction data.The effectiveness of the proposed model is verified based on a large moni-toring database of the Baimang River Tunnel Project in Shenzhen,south China.We then discuss the influence of the prediction mode,neural network structure and time division interval length of historical data on the prediction accuracy.The significance evaluation of input features shows that the historical output prediction has the largest influence on the prediction accuracy,and the influence of ground properties is secondary.It is also found that the correlations between input features and the output prediction are coincident with their interrelationships with the ground properties and ease of TBM excavation.Finally,it is found that the prediction results are most affected by the total propulsion force followed by the rotation speed of the cutterhead.The established model can provide useful guidance for construction personnel to roughly grasp the possible TBM status from the prediction results when adjusting the operational parameters.