Abstract
Reinforcement learning (RL) - a branch of machine learning - refers to the process of an agent learning to achieve a certain goal by interaction with its environment. The process of conventional tunneling shows many similarities, where a geotechnician (agent) tries to achieve a breakthrough (goal) by excavating the rockmass (environment) in an optimum way. In this paper we present a novel RL based framework for strategy development for conventional tunneling. We developed a virtual environment with the goal of a tunnel breakthrough and with a deep Q-network as the agent's architecture. It can choose from different excavation sequences to reach that goal and learns to do so in an economical and safe way by getting feedback from a specially designed reward system. Result analyses show that the optimal policies have great similarities to current practices of sequential tunneling and the framework has the potential to discover new tunneling strategies.
Original language | English |
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Article number | 103701 |
Journal | Automation in Construction |
Volume | 127 |
DOIs | |
Publication status | Published - Jul 2021 |
Keywords
- Conventional tunneling
- Excavation sequences
- Machine learning
- Reinforcement learning
- Tunnel excavation strategy
ASJC Scopus subject areas
- Control and Systems Engineering
- Civil and Structural Engineering
- Building and Construction