Bridge substructure damage morphology identification based on the underwater sonar point cloud data

Bridge underwater foundation inspection is always a prominent and challenging issue due to an unknown and unsafe underwater environment. Effective identification of bridge foundations is significant for the safety assessment of water-related bridges. However, due to the interference of numerous objective factors in the water environment (e.g., water quality, flow velocity, water depth, etc.), reliable and valid data are often difficult to obtain, and the inspection of bridge substructures remains a major challenge, especially for deep water bridge foundations. To solve this problem, a damage morphology identification method based on underwater sonar point cloud data (USPCD) is proposed in this paper for underwater bridge structures. The method is divided into two stages, including potential damage region attention and fine damage morphology identification. The former considers the regional connectivity properties of the damage, focusing on potential damage regions employing a curve fitting method based on iterative median absolute deviation. The latter gives a significant density difference between intact and damaged regions based on the density-based spatial clustering of applications with the noise clustering method to separate damaged data points from normal data points while preserving fine damage morphology features. Based on the swept sonar point cloud of underwater piles from a cross-Yangtze River bridge, we simulated spalling and cavity damage at different scales to comprehensively evaluate our proposed method. The results show that the method can detect damage at different scales and can identify most of the damaged regions. For larger-scale damage, four evaluation indicators are kept at a high level, in which the maximum GTOR and IOU can reach 95.8 % and 85.9 %, respectively. For small-scale damage, based on the synthesized high-resolution point cloud, the method can accurately identify even the damage as small as 12 cm with GTOR above 94 % and IOU over 85 %.