Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Reinforcement Learning for the Face Support Pressure of Tunnel Boring Machines

Version 1 : Received: 3 February 2023 / Approved: 14 February 2023 / Online: 14 February 2023 (06:10:35 CET)

A peer-reviewed article of this Preprint also exists.

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. 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

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