首页|A performance-based hybrid deep learning model for predicting TBM advance rate using Attention-ResNet-LSTM

A performance-based hybrid deep learning model for predicting TBM advance rate using Attention-ResNet-LSTM

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The technology of tunnel boring machine(TBM)has been widely applied for underground construction worldwide;however,how to ensure the TBM tunneling process safe and efficient remains a major concern.Advance rate is a key parameter of TBM operation and reflects the TBM-ground interaction,for which a reliable prediction helps optimize the TBM performance.Here,we develop a hybrid neural network model,called Attention-ResNet-LSTM,for accurate prediction of the TBM advance rate.A database including geological properties and TBM operational parameters from the Yangtze River Natural Gas Pipeline Project is used to train and test this deep learning model.The evolutionary polynomial regression method is adopted to aid the selection of input parameters.The results of numerical exper-iments show that our Attention-ResNet-LSTM model outperforms other commonly-used intelligent models with a lower root mean square error and a lower mean absolute percentage error.Further,parametric analyses are conducted to explore the effects of the sequence length of historical data and the model architecture on the prediction accuracy.A correlation analysis between the input and output parameters is also implemented to provide guidance for adjusting relevant TBM operational parameters.The performance of our hybrid intelligent model is demonstrated in a case study of TBM tunneling through a complex ground with variable strata.Finally,data collected from the Baimang River Tunnel Project in Shenzhen of China are used to further test the generalization of our model.The results indicate that,compared to the conventional ResNet-LSTM model,our model has a better predictive capability for scenarios with unknown datasets due to its self-adaptive characteristic.

Tunnel boring machine(TBM)Advance rateDeep learningAttention-ResNet-LSTMEvolutionary polynomial regression

Sihao Yu、Zixin Zhang、Shuaifeng Wang、Xin Huang、Qinghua Lei

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Department of Geotechnical Engineering,College of Civil Engineering,Tongji University,Shanghai,China

Key Laboratory of Geotechnical and Underground Engineering,Ministry of Education,Tongji University,Shanghai,China

Department of Earth Sciences,ETH Zurich,Zürich,Switzerland

Department of Earth Sciences.Uppsala University,Uppsala,Sweden

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National Natural Science Foundation of ChinaShanghai science and Technology Innovation ProgramChina Postdoctoral Science Foundation

5200830719DZ12010042023M732670

2024

10.1016/j.jrmge.2023.06.010

岩石力学与岩土工程学报(英文版)
中国科学院武汉岩土力学所中国岩石力学与工程学会武汉大学

岩石力学与岩土工程学报(英文版)

CSTPCD
影响因子：0.404
ISSN：1674-7755
年,卷(期)：2024.16(1)
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