Decision Support System For An Intelligent Operator Of Utility Tunnel Boring Machines

In tunnel construction projects, delays entail high costs. Thus, tunnel boring machine (TBM) operators aim for high advance rates without compromising safety, a difficult mission in uncertain subterranean environments. Finding the optimal control parameters based on the TBM sensors' measurements remains an open research question with significant practical relevance. In this paper, we present an intelligent decision support system developed in three steps. First, we propose an optimality score to evaluate TBM operation performance, taking into account the advance rate and the working pressure safety. A deep learning model then learns the mapping between the TBM measurements and this optimality score. Finally, in the context of a real application, the model provides incremental recommendations in order to improve the optimality, taking into account the current setting and measurements of the TBM. The proposed approach is evaluated on a real micro-tunnelling project and demonstrates great promise for future applications.