PRELIMINARY TEST ON STRUCTURAL ELEMENTS HEALTH MONITORING WITH A LIDAR-BASED APPROACH
Keywords: LiDAR, Machine learning, Random Forest, Structures health monitoring, Deteriorations detection, Uncrewed Aerial Systems (UAS)
Abstract. The safety and usability of infrastructures such as bridges, roads, and buildings must be monitored throughout their useful life. Traditional inspection methods are time-consuming and expensive, and innovative solutions using LiDAR-based techniques have developed. This study presents a semi-automatic method for detecting deteriorations on structural elements of a bridge using an integrated dataset of point clouds and radiometric information. The method involves using a Terrestrial Laser Scanner (TLS) to obtain high-resolution georeferenced point clouds of the bridge beams, which are then filtered to identify four classes of deteriorations. Six Machine Learning Classifiers are tested and compared using Overall Accuracy and F1-score metrics. The Random Forest emerged as the best-performing. It was then optimised by reducing the input features through an importance analysis and the accuracies measured. The results show promise and can be explored further on a larger dataset. The study aims to generalise the methodology to transfer it to actual cases.