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Shield Attitude Optimization and Control Based on Random Forest-NSGA-Ⅲ
(1. Wuhan Metro Group Co., Ltd., Wuhan 430030; 2. School of Civil and Hydraulic Engineering, Huazhong University of Science and Technology, Wuhan 430074; 3. School of Civil Engineering and Environment, Nanyang Polytechnic University, Singapore 639798;4. Civil Engineering Testing Center, Huazhong University of Science and Technology, Wuhan 430074)
Abstract In order to realize attitude control of the shield tunnelling machine and ensure safety and quality of tunnel construction, in this study, a hybrid intelligent framework incorporating random forest (RF) and non-dominant sorting genetic algorithm-Ⅲ(NSGA-Ⅲ) is proposed. Based on the Wuhan Metro project, 17 influencing factors are selected as input variables, and the nonlinear mapping function relationship between input parameters and shield attitude is established by the RF algorithm, which is used as the fitness function of NSGA-Ⅲ, and then the key influencing factors are determined by the RF algorithm. A multi-objective intelligent optimization model of RF-NSGA-Ⅲ is established to minimize the absolute value of shield attitude parameter. Based on the proposed optimization principle, case study is conducted to test the applicability and effectiveness of the proposed method. The results show that the prediction model obtained by training and simulating measured engineering data using the RF algorithm has high accuracy. With the developed RF-NSGA-Ⅲ intelligent algorithm, a remarkable optimization and control effect of shield attitude is obtained.
Abstract：
In order to realize attitude control of the shield tunnelling machine and ensure safety and quality of tunnel construction, in this study, a hybrid intelligent framework incorporating random forest (RF) and non-dominant sorting genetic algorithm-Ⅲ(NSGA-Ⅲ) is proposed. Based on the Wuhan Metro project, 17 influencing factors are selected as input variables, and the nonlinear mapping function relationship between input parameters and shield attitude is established by the RF algorithm, which is used as the fitness function of NSGA-Ⅲ, and then the key influencing factors are determined by the RF algorithm. A multi-objective intelligent optimization model of RF-NSGA-Ⅲ is established to minimize the absolute value of shield attitude parameter. Based on the proposed optimization principle, case study is conducted to test the applicability and effectiveness of the proposed method. The results show that the prediction model obtained by training and simulating measured engineering data using the RF algorithm has high accuracy. With the developed RF-NSGA-Ⅲ intelligent algorithm, a remarkable optimization and control effect of shield attitude is obtained.
WU Zhongtan1 WU Xianguo2 LIU Jun2 CHEN Hongyu3 XIAO Hongdi2,
4 QIN Yawei2,
4
.Shield Attitude Optimization and Control Based on Random Forest-NSGA-Ⅲ[J] MODERN TUNNELLING TECHNOLOGY, 2023,V60(5): 48-57
2023, Vol. 60 