Water leakage mapping in concrete railway tunnels using LiDAR generated point clouds

Abstract

Detection of water leakage areas is one of the most important tasks of regular underground tunnel inspections. Current manual visual assessments are plagued by reduced efficiency and poor accuracy, requiring personnel to physically access tunnels to conduct assessments. This form of monitoring is no longer sustainable and thus a more cost-effective method is required. Light detection and ranging (LiDAR) is one such method and commonly known for its ability to generate high-density 3D coordinated point clouds of scanned environments. In addition to point coordinates an intensity value, highly dependent on surface properties and the backscattered energy of the laser beam, is recorded. In this study properties such as surface colour, roughness and state of saturation are reviewed. Results of systematic tests of different colour and concrete targets, scanned using a mobile LiDAR scanner, are discussed. The aim of the research was to prove the aptitude for remote sensing of water leakage areas in underground tunnels and provide an automated workflow to extract quantitative information of each leak. Field tests demonstrated the ability to rapidly capture, identify, extract and record areas of water leakage based on the intensity and spatial information of tunnel point cloud data. This is particularly useful as water ingress is known to degrade concrete, resulting in the earlier onset of corrosion, spalling and loss of strength. The LiDAR scanner used here proved capable of reducing survey time and provided inspectors with a complete 3D model of the tunnel which was supplemented with quantitative leakage information (location and area).