Intelligent tunnel asset management of CERN underground facilities

Maintenance strategy and routine inspections play a crucial role in the management of the largescale infrastructures built at the European Laboratory for Particle Physics (CERN), which hosts more than 80km of underground tunnels with particle accelerators and beam lines. Most tunnels are not accessible during beam operation. Signs of ageing tunnel defects have been observed during tunnel inspections, particularly in tunnels constructed many decades ago. In the light of the safety of CERN personnel and the operational lifetime of CERN tunnels, the implementation of smart monitoring tools is essential for data automation and remote inspections. In this paper, an overview of the advanced monitoring technologies that are implemented for the inspection of CERN underground tunnels is presented. This includes the state-of-the-art monitoring tools, robotic mounted imaging, unmanned aerial vehicles (UAV) and fibre optic sensors. The frontier analysis is conducted via artificial intelligence technology, machine learning, graphic based deep learning, and photogrammetry. The feasibility of the above-mentioned methodologies has been tested in CERN underground areas, providing instrumental data for the CERN’s tunnel asset management.