Decision Support System for an Intelligent Operator of Utility Tunnel Boring Machines
This system aims to reduce costly delays in tunnel construction by providing TBM operators with intelligent recommendations for optimizing machine control parameters, which is a problem of high practical relevance for the construction industry.
This paper addresses the challenge of optimizing tunnel boring machine (TBM) advance rates while maintaining safety in uncertain ground conditions. It proposes a decision support system that uses a deep learning model to map TBM sensor measurements to an optimality score, which considers both advance rate and working pressure safety. The system then provides incremental recommendations to improve optimality, showing promise on a real micro-tunnelling project.
In tunnel construction projects, delays induce high costs. Thus, tunnel boring machines (TBM) operators aim for fast advance rates, without safety compromise, a difficult mission in uncertain ground environments. Finding the optimal control parameters based on the TBM sensors' measurements remains an open research question with large practical relevance. In this paper, we propose an intelligent decision support system developed in three steps. First past projects performances are evaluated with an optimality score, taking into account the advance rate and the working pressure safety. Then, a deep learning model learns the mapping between the TBM measurements and this optimality score. Last, in real application, the model provides incremental recommendations to improve the optimality, taking into account the current setting and measurements of the TBM. The proposed approach is evaluated on real micro-tunnelling project and demonstrates great promises for future projects.