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Research Article | Open Access

Clayshock mechanism and application to shield tunneling through existing tunnels: Settlement prediction using artificial intelligence

Zhiqiang Baia( )Yusheng JiangaChenzhong JingbZhiyong Yanga
School of Mechanics and Civil Engineering, China University of Mining and Technology, Beijing 100083, China
Shanghai Municipal Engineering Design Institute (Group) Co., Ltd., Shanghai 200092, China
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Abstract

In this study, we investigated the mechanism of clayshock during the construction of a new shield tunnel that passes through an existing shield tunnel. The experimental findings demonstrated a significant reduction of approximately 12% in the friction between the shield machine and the clay and silt layers. After the clayshock concentration exceeds 400 kg/m³, it may successfully impede the movement of the slurry, completely fill the space between the formation and the shield, and minimize the surface settling. Furthermore, Plackett–Burman testing was used to assess the sensitivity of the primary shield characteristics that affected settlement. The findings indicated that settlement was greatly influenced by the earth pressure, grouting quantity, and clayshock efficiency. A full factor analysis revealed an intricate link between the shield properties and settlement. If the volume of grouting was less than 6.5 m³, it was very difficult to regulate the settling value within −1.0 mm. We utilized the following neural network models: gated recurrent unit (GRU) network, long short-term memory network (LSTM), and Elman recurrent neural network (ELMAN). The training input data consisted of geometric, geological, and shield parameters, and the prediction output data served as the settlement value. Researchers will be able to generate accurate and reliable predictive outcomes by improving the model training process.

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Journal of Intelligent Construction
Article number: 9180019
Cite this article:
Bai Z, Jiang Y, Jing C, et al. Clayshock mechanism and application to shield tunneling through existing tunnels: Settlement prediction using artificial intelligence. Journal of Intelligent Construction, 2024, 2(2): 9180019. https://doi.org/10.26599/JIC.2024.9180019
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Received: 28 November 2023
Revised: 18 January 2024
Accepted: 19 February 2024
Published: 27 May 2024
© The Author(s) 2024. Published by Tsinghua University Press.

The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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