Decision support system for an intelligent operator of utility tunnel boring machines

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