LiDAR and UAV Photogrammetry Techniques for Optimizing 3D Mapping Inspection Systems of Reinforced Concrete Structures

Abstract. This study addresses the challenges of accessibility and laborious intensity in visual inspections of public metropolitan mobility infrastructure, such as elevated Metro systems. It explores an experimental 3D-Mapping Inspection and Classification Evaluation method (3D-MICE) utilizing UAV imagery and geometric mensuration from 3D point clouds. The method introduces two classification techniques: Condition Classification by Intensity (CCI) and Geometry Classification by RGB color (GCC), applied to orthomosaics. 3D-MICE enables semi-automatic detection, segmentation, and measurement of cracks and stains in reinforced concrete by selecting areas of interest based on intensity and geometric features. This approach offers a promising, efficient, and precise alternative to traditional inspection methods. 3D-MICE can detect, segment and measure, semi-automatically, cracks and stains of reinforced concrete structures by selecting areas of interest based on intensity and geometry.