Geological adaptive TBM operation parameter decision based on random forest and particle swarm optimization

被引：1
|
作者：
Liu M. [1 ]
Tao J. [1 ]
Qin C. [1 ]
Yu H. [1 ]
Liu C. [1 ]
机构：
[1] School of Mechanical Engineering, Shanghai Jiaotong University, Shanghai
来源：
Zhongnan Daxue Xuebao (Ziran Kexue Ban)/Journal of Central South University (Science and Technology) | 2023年 / 54卷 / 04期
关键词：
operating parameter decision; particle swarm optimization; random forest; tunnel boring machine;
D O I：
10.11817/j.issn.1672-7207.2023.04.010
中图分类号：
学科分类号：
摘要：
Considering that the performance of TBM is affected by geological condition and driver experience, a geological adaptive TBM operation parameter decision based on random forest(RF) and particle swarm optimization algorithm(PSO) was proposed. RF was used to establish the mapping relation model between geological types, operating parameters and thrust speed, cutter head torque. An optimization equation was established using the mapping relationship model in which the maximum TBM thrust speed was taken as the target, and cutterhead speed, screw conveyor speed, total thrust and earth pressure were taken as control variables. PSO was used to solve the optimal combination of operating parameters for each geological type. The validity and superiority of the proposed method were verified by the construction data of a subway project in Singapore. The results show that the R2 of the driving speed and cutter head torque predicted by random forest model reaches 0.936 and 0.961, which are greater than those of adaboost, multiple linear regression, ridge regression, SVR and DNN. PSO can accurately solve the optimal solution of operating parameters, and the time consumption is shorter than that of genetic algorithm, ant colony algorithm and exhaustive algorithm. By using the proposed method, the TBM thrust speed increases by 67.2%, 41.8%, 53.6%, 15.0% in the strata of Fokonnen Pebble Formation, Jurong Formation IV, Jurong Formation V and Marine Clay Formation in this construction section, respectively. © 2023 Central South University of Technology. All rights reserved.
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页码：1311 / 1324
页数：13
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