Soranzo, E.; Guardiani, C.; Wu, W. Reinforcement Learning for the Face Support Pressure of Tunnel Boring Machines. Geosciences2023, 13, 82.
Soranzo, E.; Guardiani, C.; Wu, W. Reinforcement Learning for the Face Support Pressure of Tunnel Boring Machines. Geosciences 2023, 13, 82.
Soranzo, E.; Guardiani, C.; Wu, W. Reinforcement Learning for the Face Support Pressure of Tunnel Boring Machines. Geosciences2023, 13, 82.
Soranzo, E.; Guardiani, C.; Wu, W. Reinforcement Learning for the Face Support Pressure of Tunnel Boring Machines. Geosciences 2023, 13, 82.
Abstract
In tunnel excavation with boring machines, the tunnel face is supported to avoid collapse and minimise settlement. This article proposes the use of reinforcement learning, specifically the Deep Q-Network algorithm, to predict the face support pressure. The approach is tested both analytically and numerically. By using the soil properties ahead of the tunnel face and the overburden depth as the input, the algorithm is capable of predicting the optimal tunnel face support pressure, adapting to changes in geological and geometrical conditions.
Keywords
Tunnelling; Tunnel Boring Machine; Support pressure; Face stability; Reinforcement Learning; Machine Learning; Deep-Q-Network
Subject
Engineering, Civil Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
