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 hyper-parameters 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 con-struction variables(main thrust and foam liquid volume)display the highest correlation with the cut-terhead torque(CHT).This work provides a feasible and applicable alternative way to estimate the performance of shield tunneling.

Song-Shun Lin;Shui-Long Shen;Annan Zhou

Department of Civil Engineering,School of Naval Architecture,Ocean,and Civil Engineering,Shanghai Jiao Tong University,Shanghai,200240,China;Department of Civil and Environmental Engineering,National University of Singapore,117576,Singapore;Key Laboratory of Intelligent Manufacturing Technology,Department of Civil and Environmental Engineering,College of Engineering,Shantou University,Shantou,515063,China;Discipline of Civil and Infrastructure,School of Engineering,Royal Melbourne Institute of Technology(RMIT),Melbourne,Victoria 3001,Australia

岩石力学与岩土工程学报（英文版）

[1]Song-Shun Lin;Shui-Long Shen;Annan Zhou-.Real-time analysis and prediction of shield cutterhead torque using optimized gated recurrent unit neural network)[J].岩石力学与岩土工程学报（英文版）,2022(04):1232-1240

cutterhead,terhead

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