Description
Inspecting and monitoring the conditions of an infrastructure are two key steps in increasing user safety and properly managing available resources and are preliminary to the later steps of decision-making on the interventions to be put in place. Traditional techniques involve visual and manual inspections, which inevitably involve the closure of the section being investigated with time and resource consumption. The Mobile Laser Scanner (MLS) technique based on LiDAR (Light Detection And Ranging) technology allows modelling via a point cloud of the tunnel intrados quickly and without traffic interruption. To analyze the intrados of the tunnel, we would need to have it in a plane system needing the change from a 3D reference system to a 2D one. This requires a coordinate transformation that is strictly dependent on the development of the tunnel, which rarely results in being part of a perfect cylinder. The proposed methodology focuses on analyzing the intrados of a tunnel by applying an automated point cloud unrolling algorithm based on the RANSAC (RANdom SAmple Consensus) method. Intensity values were then analyzed to assess possible water infiltration, and the roughness values were calculated to assess the integrity of the surface part of the tunnel wall and highlight both degradation such as spalling and swelling and highlight any cracks or expulsion of steel bars. Deformation is evaluated on each individual section at given interaxle spacing by analyzing the differences between the circumference interpolated by the RANSAC method and the point cloud describing the section. The results can be useful in identifying all those sections in tunnels that need emergency interventions and therefore characterized by high priority of intervention or alertness. The test site is San Liberatore tunnel located on the A3 Naples-Salerno freeway section (Campania Region, Italy). The tunnel analyzed is in the southern carriageway near Vietri sul Mare. It extends for a total length of about 100 m within an area at the foot of Mount San Liberatore. The results, although limited to two types of distress, are helpful in identifying all those sections in tunnels that need emergency interventions.
