# Identification of Underwater Structural Bridge Damage and BIM-Based Bridge Damage Management
###### Abstract
Many scholars have combined YOLO-v4 with underwater target recognition to explore the recognition effectiveness in different underwater environments [37; 38; 39]. For example, [PERSON] et al. tested six CNN detectors in different depths of underwater environments and proved that YOLO-v4 outperformed other models in detecting underwater pipeline objects with poor light visibility [40]; [PERSON] et al. found that YOLO-v4 could identify underwater cracks quickly and accurately under different water quality conditions (clear water, turbid water, and deep water), proving that the powerful image enhancement technique of YOLO-v4 can adapt to the conditions of underwater environment [41]. Therefore, this study considers the optimization of YOLO-v4 and proposes a more lightweight recognition method, Lite-YOLO-v4, to reduce the hardware performance requirements and improve the accuracy of underwater structural damage recognition of bridges. There are many different representations of 3D data, such as depth images, point clouds and grids, and others. As a form of 3D data representation, point clouds can retain information about the 3D shape and geometric features of the original object surface in 3D space, not requiring discretization [42]. For example, [PERSON] et al. analysed the application of 3D point cloud acquisition technology in the construction industry, including the creation of original BIM models for generating projects and the quality inspection of various buildings and infrastructures [43]; [PERSON] et al. used 3D point clouds to capture image features by processing multiple aerial platform plan views to generate models to improve the detection of post-disaster building damage [44]; [PERSON] et al. used ground scanners combined with 3D point cloud acquisition techniques to comparatively measure surface changes, improving the accuracy of generating 3D models for handling multiple complex situations and reducing uncertainty in point cloud imaging [45]; [PERSON] et al.,based on close-range image capture by UAVs, created dense point cloud data, and the final findings showed that the accuracy of 3D data obtained using point cloud data can reach the thousandth percentile [46]; it has been demonstrated in several studies that 3D point cloud acquisition techniques are suitable for high precision image identification and for 3D information reconstruction, therefore, this paper considers the use of underwater cameras for multi-directional image acquisition and uses a method based on 3D point cloud data acquisition to obtain 3D information about underwater damage. The safety of the underwater structure of a bridge affects the overall durability of the bridge, and the complex environment of the underwater structure makes ordinary damage detection methods inapplicable to the complex working conditions underwater; therefore, a health management system needs to be set up for the characteristics of the underwater environment. In summary, this study considers the establishment of a BIM technology-based health management system for underwater bridge structures, incorporating deep learning and point cloud data collection techniques to improve the accuracy of underwater damage identification and 3D index calculation of damaged areas, correlating inspection data with the model, through which the bridge management system can observe information about the location, appearance images, and dimensions of the bridge substructure regarding the damage. Improving the correlation between the model and the bridge management system allows the observation of the location, appearance, and measurements of the bridge substructure, enhances the correlation between the model and the inspection data, and realises the automatic identification and 3D visualisation of the underwater structural damage of the bridge.