Abstract

Obtaining accurate information of defective areas of infrastructures helps to perform repair actions more efficiently. Recently, LiDAR scanners have been used for the inspection of surface defects. Moreover, machine learning methods have attracted the attention of researchers for semantic segmentation and classification based on point cloud data. Although much work has been done for processing visual information with images, research on machine learning methods for semantic segmentation of raw point cloud data is still in its early stages. Moreover, LiDAR technology is commonly used to create as-is BIM models. Therefore, the BIM model needs to be integrated with the results of defect semantic segmentation after the LiDAR-based inspection. Addressing the above issues, this paper has the following objectives: (1) Developing a method for point cloud-based concrete surface defects semantic segmentation; and (2) Developing a semi-automated process for as-inspected modeling. The challenges related to the size of the dataset and imbalanced classes are studied. Sensitivity analysis is applied to capture the best combination of hyperparameters and investigate their effects on the network performance. The proposed method resulted in 98.56% and 96.50% recalls for semantic segmentation of cracks and spalls, respectively. Furthermore, post-processing of the results of the concrete surface defects semantic segmentation is done to semi-automate the process of as-inspected modeling. As-inspected BIM includes the updated information of the facilities at the time of data collection. This semi-automated process made it possible to manage and visualize the detected defects by extracting their dimensions and identifying the conditions on the 3D model.