Vol.128, No.1, 2021, pp.109-127, doi:10.32604/cmes.2021.015683
Optimal Control of Slurry Pressure during Shield Tunnelling Based on Random Forest and Particle Swarm Optimization
  • Weiping Luo1,2, Dajun Yuan1,2, Dalong Jin1,2,*, Ping Lu1,2, Jian Chen3
1 Key Laboratory of Urban Underground Engineering of the Education Ministry, Beijing Jiaotong University, Beijing, 100044, China
2 School of Civil Engineering, Beijing Jiaotong University, Beijing, 100044, China
3 China Railway 14 Bureau Group Co., Ltd., Jinan, 250101, China
* Corresponding Author: Dalong Jin. Email:
Received 04 January 2021; Accepted 18 March 2021; Issue published 28 June 2021
The control of slurry pressure aiming to be consistent with the external water and earth pressure during shield tunnelling has great significance for face stability, especially in urban areas or underwater where the surrounding environment is very sensitive to the fluctuation of slurry pressure. In this study, an optimal control method for slurry pressure during shield tunnelling is developed, which is composed of an identifier and a controller. The established identifier based on the random forest (RF) can describe the complex non-linear relationship between slurry pressure and its influencing factors. The proposed controller based on particle swarm optimization (PSO) can optimize the key factor to precisely control the slurry pressure at the normal state of advancement. A data set from Tsinghua Yuan Tunnel in China was used to train the RF model and several performance measures like R2, RMSE, etc., were employed to evaluate. Then, the hybrid RF-PSO control method is adopted to optimize the control of slurry pressure. The good agreement between optimized slurry pressure and expected values demonstrates a high identifying and control precision.
Shield tunnelling; slurry pressure; optimal control; random forest; particle swarm optimization
Cite This Article
Luo, W., Yuan, D., Jin, D., Lu, P., Chen, J. (2021). Optimal Control of Slurry Pressure during Shield Tunnelling Based on Random Forest and Particle Swarm Optimization. CMES-Computer Modeling in Engineering & Sciences, 128(1), 109–127.
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