  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.
