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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.
Y. Wan, Z. D. Zhu, L. Song, et al. Study on temporary filling material of synchronous grouting in the middle of shield. Constr Build Mater, 2021, 273: 121681.
S. J. Ma, M. Y. Li, Y. C. Guo, et al. Field test and research on shield cutting pile penetrating cement soil single pile composite foundation. Geomech Eng, 2020, 23: 513–521.
S. H. Tang, X. P. Zhang, Q. S. Liu, et al. Control and prevention of gas explosion in soft ground tunneling using slurry shield TBM. Tunn Undergr Space Technol, 2021, 113: 103963.
H. H. Zhu, X. Wang, X. Q. Chen, et al. Similarity search and performance prediction of shield tunnels in operation through time series data mining. Autom Constr, 2020, 114: 103178.
S. Suwansawat, H. H. Einstein. Artificial neural networks for predicting the maximum surface settlement caused by EPB shield tunneling. Tunn Undergr Space Technol, 2006, 21: 133–150.
J. H. Li, P. X. Li, D. Guo, et al. Advanced prediction of tunnel boring machine performance based on big data. Geosci Front, 2021, 12: 331–338.
Y. K. Zhang, G. F. Gong, H. Y. Yang, et al. Precision versus intelligence: Autonomous supporting pressure balance control for slurry shield tunnel boring machines. Autom Constr, 2020, 114: 103173.
C. Zhou, H. C. Xu, L. Y. Ding, et al. Dynamic prediction for attitude and position in shield tunneling: A deep learning method. Autom Constr, 2019, 105: 102840.
W. L. Liu, A. Li, C. J. Liu. Multi-objective optimization control for tunnel boring machine performance improvement under uncertainty. Autom Constr, 2022, 139: 104310.
S. S. Lin, S. L. Shen, N. Zhang, et al. Modelling the performance of EPB shield tunnelling using machine and deep learning algorithms. Geosci Front, 2021, 12: 101177.
X. Huang, Q. S. Liu, X. X. Peng, et al. Mechanism and forecasting model for shield jamming during TBM tunnelling through deep soft ground. Eur J Environ Civil Eng, 2019, 23: 1035–1068.
R. Hasanpour, J. Rostami, J. Schmitt, et al. Prediction of TBM jamming risk in squeezing grounds using Bayesian and artificial neural networks. J Rock Mech Geotech Eng, 2020, 12: 21–31.
K. Elbaz, S. L. Shen, A. N. Zhou, et al. Prediction of disc cutter life during shield tunneling with AI via the incorporation of a genetic algorithm into a GMDH-type neural network. Engineering, 2021, 7: 238–251.
K. Elbaz, S. L. Shen, A. N. Zhou, et al. Data in intelligent approach for estimation of disc cutter life using hybrid metaheuristic algorithm. Data Brief, 2020, 33: 106479.
Z. X. Li, H. W. Sun. Artificial intelligence-based spatio–temporal vision sensors: Applications and prospects. Front Mater, 2023, 10: 1269992.
W. P. Wang, Z. R. Wang, Z. F. Zhou, et al. Anomaly detection of industrial control systems based on transfer learning. Tsinghua Sci Technol, 2021, 26: 821–832.
N. Li, L. Hu, Z. L. Deng, et al. Research on GRU neural network satellite traffic prediction based on transfer learning. Wirel Pers Commun, 2021, 118: 815–827.
G. Wang, Q. Fang, J. M. Du, et al. Deep learning-based prediction of steady surface settlement due to shield tunnelling. Autom Constr, 2023, 154: 105006.
S. Bair, P. Vergne, P. Kumar, et al. Comment on “history, origins and prediction of elastohydrodynamic friction” by Spikes and Jie. Tribol Lett, 2015, 58: 16.
A. Matsoukas, E. Mitsoulis. Geometry effects in squeeze flow of Bingham plastics. J Non-Newton Fluid Mech, 2003, 109: 231–240.
M. M. I. Al-Doury. A discussion about hydraulic permeability and permeability. Pet Sci Technol, 2010, 28: 1740–1749.
H. H. Zhu, Q. W. Xu, S. M. Liao, et al. Experimental study on thrust thrust model of earth pressure balanced shield construction. Rock Soil Mech, 2007, 28: 1587–1594. (in Chinese)
S. L. Marshall. Nonlinear pressure diffusion in flow of compressible liquids through porous media. Transp Porous Media, 2009, 77: 431–446.
L. H. Chen, X. Li, Y. Xu, et al. Accurate estimation of soil shear strength parameters. J Cent South Univ, 2019, 26: 1000–1010.
G. Z. Xu, Y. F. Gao, C. J. Xu. Permeability behavior of high-moisture content dredged slurries. Mar Georesour Geotechnol, 2015, 33: 348–355.
I. B. Gratchev, K. Sassa. Shear strength of clay at different shear rates. J Geotech Geoenviron Eng, 2015, 141: 06015002.
A. Sadrekarimi. Effect of the mode of shear on static liquefaction analysis. J Geotech Geoenviron Eng, 2014, 140: 04014069.
M. Mašković, B. Jančić-stojanović, A. Malenović, et al. Assessment of liquid chromatographic method robustness by use of Plackett–Burman design. Acta Chromatogr, 2010, 22: 281–296.
M. Kulahci, S. Bisgaard. The use of Plackett–Burman designs to construct split-plot designs. Technometrics, 2005, 47: 495–501.
J. Yoon. Application of experimental design and optimization to PFC model calibration in uniaxial compression simulation. Int J Rock Mech Min Sci, 2007, 44: 871–889.
E. B. Ding, C. N. Cao, H. Q. Hu, et al. Application of central composite design to the optimization of fly ash-based geopolymers. Constr Build Mater, 2020, 230: 116960.
J. D. Kechagias, N. Vidakis. Parametric optimization of material extrusion 3D printing process: An assessment of Box–Behnken vs. full-factorial experimental approach . Int J Adv Manuf Technol, 2022, 121: 3163–3172.
C. Busack, C. M. Knudsen. Using factorial mating designs to increase the effective number of breeders in fish hatcheries. Aquaculture, 2007, 273: 24–32.
S. F. Ding, Y. N. Zhang, J. R. Chen, et al. Research on using genetic algorithms to optimize Elman neural networks. Neural Comput Appl, 2013, 23: 293–297.
N. Zhang, N. Zhang, Q. Zheng, et al. Real-time prediction of shield moving trajectory during tunnelling using GRU deep neural network. Acta Geotech, 2022, 17: 1167–1182.
K. Smagulova, A. P. James. A survey on LSTM memristive neural network architectures and applications. Eur Phys J Spec Top, 2019, 228: 2313–2324.
Y. Yu, X. S. Si, C. H. Hu, et al. A review of recurrent neural networks: LSTM cells and network architectures. Neural Comput, 2019, 31: 1235–1270.
C. Dennis, A. P. Engelbrecht, B. M. Ombuki-Berman. An analysis of activation function saturation in particle swarm optimization trained neural networks. Neural Process Lett, 2020, 52: 1123–1153.
I. Ohn, Y. Kim. Smooth function approximation by deep neural networks with general activation functions. Entropy, 2019, 21: 627.
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