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Reinforcement learning-based optimizer to improve the steering of shield tunneling machine
https://orcid.org/http://orcid.org/0000-0003-2599-1616
https://orcid.org/http://orcid.org/0000-0002-5610-7988
https://orcid.org/http://orcid.org/0000-0001-5209-5169
Reliable and timely prediction of the shield tunneling path is essential to avoid deviation and successfully complete a tunneling project. This study presents a reinforcement learning-based new optimal model for improving the forecasting accuracy of the shield tunneling path and alleviating shield drivers’ over-reliance on their practical experience. This model integrates the Q-learning network with a metaheuristic gray wolf algorithm to explore and exploit the implicit information of the shield machine through Q-Table. The proposed method is applied to a field tunneling case with data collected from a real tunneling scenario in Tianjin City, China. The model is also evaluated using various numerical benchmark approaches and compared to a deep learning method. The results show that the proposed model produces an accurate prediction with a root-mean-square error of 0.539 and correlation coefficient of 0.925 for pitch values. A sensitivity analysis indicated that the thrust force and the buried depth have a significant influence on the prediction of shield tunneling trajectory.
Reinforcement learning-based optimizer to improve the steering of shield tunneling machine
https://orcid.org/http://orcid.org/0000-0003-2599-1616
https://orcid.org/http://orcid.org/0000-0002-5610-7988
https://orcid.org/http://orcid.org/0000-0001-5209-5169
Reliable and timely prediction of the shield tunneling path is essential to avoid deviation and successfully complete a tunneling project. This study presents a reinforcement learning-based new optimal model for improving the forecasting accuracy of the shield tunneling path and alleviating shield drivers’ over-reliance on their practical experience. This model integrates the Q-learning network with a metaheuristic gray wolf algorithm to explore and exploit the implicit information of the shield machine through Q-Table. The proposed method is applied to a field tunneling case with data collected from a real tunneling scenario in Tianjin City, China. The model is also evaluated using various numerical benchmark approaches and compared to a deep learning method. The results show that the proposed model produces an accurate prediction with a root-mean-square error of 0.539 and correlation coefficient of 0.925 for pitch values. A sensitivity analysis indicated that the thrust force and the buried depth have a significant influence on the prediction of shield tunneling trajectory.
Reinforcement learning-based optimizer to improve the steering of shield tunneling machine
https://orcid.org/http://orcid.org/0000-0003-2599-1616
https://orcid.org/http://orcid.org/0000-0002-5610-7988
https://orcid.org/http://orcid.org/0000-0001-5209-5169
Reliable and timely prediction of the shield tunneling path is essential to avoid deviation and successfully complete a tunneling project. This study presents a reinforcement learning-based new optimal model for improving the forecasting accuracy of the shield tunneling path and alleviating shield drivers’ over-reliance on their practical experience. This model integrates the Q-learning network with a metaheuristic gray wolf algorithm to explore and exploit the implicit information of the shield machine through Q-Table. The proposed method is applied to a field tunneling case with data collected from a real tunneling scenario in Tianjin City, China. The model is also evaluated using various numerical benchmark approaches and compared to a deep learning method. The results show that the proposed model produces an accurate prediction with a root-mean-square error of 0.539 and correlation coefficient of 0.925 for pitch values. A sensitivity analysis indicated that the thrust force and the buried depth have a significant influence on the prediction of shield tunneling trajectory.
Reinforcement learning-based optimizer to improve the steering of shield tunneling machine
Acta Geotech.
Elbaz, Khalid (Autor:in) / Shen, Shui-Long (Autor:in) / Zhou, Annan (Autor:in) / Yoo, Chungsik (Autor:in)
Acta Geotechnica ; 19 ; 4167-4187
01.06.2024
21 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
Deep learning , Q-learning network , Reinforcement learning , Sensitivity analysis , Tunneling Engineering , Geoengineering, Foundations, Hydraulics , Solid Mechanics , Geotechnical Engineering & Applied Earth Sciences , Soil Science & Conservation , Soft and Granular Matter, Complex Fluids and Microfluidics
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