Reinforcement Learning for the Face Support Pressure of Tunnel Boring Machines

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 algorithm uses a neural network to make decisions based on the expected rewards of each action. 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 whilst minimising settlement, and adapting to changes in geological and geometrical conditions. The algorithm reaches maximum performance after 400 training episodes and can be used for random geological settings without retraining.

Keywords

• tunnelling
• tunnel boring machine
• support pressure
• face stability
• reinforcement learning
• machine learning