The aspects related to the serviceability and long-term of TBM
tunnels have assumed increasing relevance in the design, maintenance and
management of infrastructures. To guarantee a high level of knowledge
through time and improve durability, it is fundamental to carry out new
strategies able to digitalize and automatize the aforementioned factors
reducing time and costs. ETS introduced a new method for the diagnostic
of existing tunnels through an innovative multi-dimensional survey
system (ARCHITA), and a new approach for the Management and
Identification of the Risk for Existing Tunnels (MIRET). A detailed
preliminary assessment of the tunnel condition is obtained through laser
scanning and high-definition photo, with minimal impact on the
serviceability of the line. Results are digitalized and manipulated,
allowing the development of deep learning-based methods for the
segmentation of defects with AI algorithms. The paper explains the
framework of digital strategies, automatization and artificial
intelligence for TBM tunnelling.
Keywords: TBM tunnels maintenance, serviceability, long-term behavior, tunnelling automatization and artificial Intelligence, monitoring.
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