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QPSO-ILF-ANN-based optimization of TBM control parameters considering tunneling energy efficiency

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Abstract

In recent years, tunnel boring machines (TBMs) have been widely used in tunnel construction. However, the TBM control parameters set based on operator experience may not necessarily be suitable for certain geological conditions. Hence, a method to optimize TBM control parameters using an improved loss function-based artificial neural network (ILF-ANN) combined with quantum particle swarm optimization (QPSO) is proposed herein. The purpose of this method is to improve the TBM performance by optimizing the penetration and cutterhead rotation speeds. Inspired by the regularization technique, a custom artificial neural network (ANN) loss function based on the penetration rate and rock-breaking specific energy as TBM performance indicators is developed in the form of a penalty function to adjust the output of the network. In addition, to overcome the disadvantage of classical error backpropagation ANNs, i.e., the ease of falling into a local optimum, QPSO is adopted to train the ANN hyperparameters (weight and bias). Rock mass classes and tunneling parameters obtained in real time are used as the input of the QPSO-ILF-ANN, whereas the cutterhead rotation speed and penetration are specified as the output. The proposed method is validated using construction data from the Songhua River water conveyance tunnel project. Results show that, compared with the TBM operator and QPSO-ANN, the QPSO-ILF-ANN effectively increases the TBM penetration rate by 14.85% and 13.71%, respectively, and reduces the rock-breaking specific energy by 9.41% and 9.18%, respectively.

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Acknowledgements

This study was supported by the National Natural Science Foundation of China (Grant Nos. 41941018, 52074258, 42177140, and 41807250), and the Key Research and Development Project of Hubei Province (No. 2021BCA133). We gratefully acknowledge the support provided.

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Authors and Affiliations

  1. Yellow River Engineering Consulting Co., Ltd., Zhengzhou, 450003, China

    Xinyu Wang & Jihua Yang

  2. School of Civil Engineering, Wuhan University, Wuhan, 430072, China

    Xinyu Wang, Jian Wu, Xin Yin, Quansheng Liu & Yucong Pan

  3. Power China Huadong Engineering Co., Ltd., Hangzhou, 311122, China

    Jian Wu, Lei Huang & Shuangping Miao

  4. State Key Laboratory of Geomechanics and Geotechnical Engineering, Institute of Rock and Soil Mechanics, Chinese Academy of Sciences, Wuhan, 430071, China

    Xing Huang

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  1. Xinyu Wang
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  3. Xin Yin
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Correspondence to Xin Yin.

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Wang, X., Wu, J., Yin, X. et al. QPSO-ILF-ANN-based optimization of TBM control parameters considering tunneling energy efficiency. Front. Struct. Civ. Eng. 17, 25–36 (2023). https://doi.org/10.1007/s11709-022-0908-z

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  • DOI: https://doi.org/10.1007/s11709-022-0908-z

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