Distress detection in tunnel lining from MLS data

As tunnels age, deterioration of their inner lining worsens, necessitating continuous inspections and evaluations to monitor their condition and implement preventive measures against potential structural damage, thereby avoiding service disruptions. Traditional methods involve visual and manual inspections, leading to temporary closures and resource consumption. The Mobile Laser Scanner (MLS) technique, which is based on LiDAR (Light Detection And Ranging) technology, allows the modeling of the tunnel intrados using a point cloud, without disrupting traffic. Our study introduces a methodology for tunnel intrados analysis utilizing an automated point cloud unrolling algorithm rooted in the RANSAC (RANdom SAmple Consensus) method. Intensity values are examined to identify potential water infiltration, while roughness values evaluate surface integrity and reveal cracks or protruding steel bars. Despite being limited to two types of distress, our research facilitate the identification of tunnel sections necessitating immediate intervention, allowing for the prioritization of high-risk areas, or issuing alerts.