JRMGE / Vol 14 / Issue 4
Real-time analysis and prediction of shield cutterhead torque using optimized gated recurrent unit neural network
Song-Shun Lin, Shui-Long Shen, Annan Zhou
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a Department of Civil Engineering, School of Naval Architecture, Ocean, and Civil Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China
b Key Laboratory of Intelligent Manufacturing Technology, Department of Civil and Environmental Engineering, College of Engineering, Shantou University, Shantou,
515063, China
c Discipline of Civil and Infrastructure, School of Engineering, Royal Melbourne Institute of Technology (RMIT), Melbourne, Victoria 3001, Australia
d Department of Civil and Environmental Engineering, National University of Singapore, 117576, Singapore
2022, 14(4): 1232-1240. doi:10.1016/j.jrmge.2022.06.006
Received: 2022-01-22 / Revised: 2022-04-25 / Accepted: 2022-06-14 / Available online: 2022-06-30
2022, 14(4): 1232-1240.
doi:10.1016/j.jrmge.2022.06.006
Received: 2022-01-22
Revised: 2022-04-25
Accepted: 2022-06-14
Available online: 2022-06-30
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.
Keywords: Earth pressure balance (EPB) shield tunneling, Cutterhead torque (CHT) prediction, Particle swarm optimization (PSO), Gated recurrent unit (GRU) neural network
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Author(s) Information
Shui-Long Shen
Prof. Shui-Long Shen received his BSc degree in underground space technology and the MSc degree in structural engineering from Tongji University in 1986 and 1989, respectively, and PhD in social system engineering from Saga University, Japan, in 1998. In 2003, he joined the Department of Civil Engineering, Shanghai Jiao Tong University and became the department head in 2010 till the end of 2018. In 2019, he joined the College of Engineering, Shantou University, serving as the Dean. He has been keeping collaboration with other international organization, e.g. Saga University, The University of Hong Kong, Ecole Centrale de Nantes, France. He is now an Adjunct Professor of RMIT University and Swinburne University of Technology, Australia. He has published more than 370 refereed journal articles with total citation over 15,000 times.