基于TBM破岩数据的岩体条件深度学习表征方法

姚敏; 李旭; 原继东; 王玉杰; 李鹏宇

doi:10.3799/dqkx.2022.281

Volume 48 Issue 5

May 2023

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Article Navigation > Earth Science > 2023 > 48(5): 1908-1922

Yao Min, Li Xu, Yuan Jidong, Wang Yujie, Li Pengyu, 2023. Deep Learning Characterization Method of Rock Mass Conditions Based on TBM Rock Breaking Data. Earth Science, 48(5): 1908-1922. doi: 10.3799/dqkx.2022.281

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Yao Min, Li Xu, Yuan Jidong, Wang Yujie, Li Pengyu, 2023. Deep Learning Characterization Method of Rock Mass Conditions Based on TBM Rock Breaking Data. Earth Science, 48(5): 1908-1922. doi: 10.3799/dqkx.2022.281

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Deep Learning Characterization Method of Rock Mass Conditions Based on TBM Rock Breaking Data

doi: 10.3799/dqkx.2022.281

Yao Min^{1
,
,},
Li Xu^{1
,
,},
Yuan Jidong¹,
Wang Yujie²,
Li Pengyu³

1.
Key Laboratory of Urban Underground Engineering, Ministry of Education, Beijing Jiaotong University, Beijing 100044, China
2.
China Institute of Water Resources and Hydropower Research, Beijing 100048, China
3.
China Railway Engineering Equipment Group Co., Ltd., Zhengzhou 450016, China

Received Date: 2022-07-14
Available Online: 2023-06-06
Publish Date: 2023-05-25

Abstract

Abstract

Surrounding rock perception based on TBM construction data is essential to ensuring the safety of TBM construction and improving its construction efficiency, in which the accuracy of TBM tunnelling parameter prediction is crucial to testing the effect of surrounding rock perception. Therefore, in this paper it takes Jilin Yin-Song's project TBM4 bid section as the research object, selects the characteristic parameters of the surrounding rock from the rock breaking data of the loading phase of the TBM as input feature X1, and selects two construction control parameters (the rotation speed and penetration rate) as input feature X2, and constructs a convolutional neural network machine learning model to predict the TBM tunnelling response parameters Y (cutterhead torque and total thrust). According to the different learning objects, the point prediction model that only learns the response behavior of the stable boring phase and the line prediction model that simultaneously learns the response behavior of the loading phase and the stable boring phase are constructed, respectively. The improved results show that the point prediction model cannot describe the influence of control parameters on tunnelling response parameters. Although the line prediction model can describe the influence of control parameters on tunnelling response parameters, the prediction value of driving response in the stable boring phase is low. Considering that the low predictive value of the line prediction model in the stable boring phase is because the number of behavior samples in the stable boring phase only accounts for 9% of the total number of samples, in this paper, a method of adjusting loss function is proposed to improve the weight of behavior samples in the stable boring phase, which significantly improves the prediction accuracy of the line prediction model. The results show that the behavior of the loading boring phase should be studied, and the weight of the behavior of the stable boring phase should be increased to obtain a high-precision prediction model of tunnelling response parameters. The model obtained in this paper can provide a basis for further surrounding rock perception and control parameter optimization.
- TBM tunnelling response parameter,
- convolutional neural network,
- control parameter,
- line prediction model,
- weight,
- engineering geology

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References(50)

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Deep Learning Characterization Method of Rock Mass Conditions Based on TBM Rock Breaking Data

doi: 10.3799/dqkx.2022.281

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