# Identification of Underwater Structural Bridge Damage and BIM-Based Bridge Damage Management
## 5 Results and Discussion  
In this paper, the underwater bridge health monitoring system combining the Lite-YOLO-v4 algorithm and 3D point cloud damage identification algorithm is applied to bridge detection. In the second part of the article, the types of depth cameras are introduced and compared. A binocular structured light camera combined with an underwater robot is selected to obtain images of bridge diseases. The principle and calibration of underwater camera imaging are described. A high order distortion correction formula is introduced to aim at the underwater imaging distortion problem. The camera parameters are solved and written into the camera, and the corresponding relationship between the three-dimensional points of the underwater space and the image pixels is established. The distorted image can be corrected by the calibration parameters to output a normal image, and the calibrated camera can complete the disease appearance acquisition task underwater. The third part introduces the Lite-YOLO-v4 algorithm and 3D point cloud damage identification algorithm. In the YOLO-v4 algorithm, the classification layer and output layer of Mobilenetv3 are removed, and CSPDarkent53 is used as the main feature extraction network. In addition, it replacing many ordinary convolutions in the PANet structure with deep separable convolutions, multi-feature fusion is performed on the prior box. The improved network model parameters and calculation amount are significantly reduced, and the model size is reduced to \(1/5\). For the acquired disease 3D point cloud image, the image noise is removed by statistical filtering algorithm and setting filtering parameters. Then, the Poisson surface reconstruction algorithm is used to reconstruct the defect surface, the depth and width of the defect are obtained by three-dimensional data points, and the defect volume is calculated by the convex hull algorithm. Comparing the crack information and calculating the three-dimensional information of concrete diseases shows that the  
Figure 17: Bridge Underwater Structure Inspection Record Add. two algorithms have high accuracy for the three-dimensional information calculation of underwater diseases and have practical application potential. The fourth part establishes a bridge underwater structure health management system based on BIM technology. The integration of BIM technology and underwater disease detection has greatly improved the environment of underwater bridge structure detection. The system is based on Revit's secondary development tools, developed by C # language, and then uses a Revit.API interface to achieve 3D visualisation of structures and defects. It uses an SQL Server as a storage database to store the acquired disease information to meet the health detection system's damage data management function. Revit is used to establish the bridge model and create the relevant information about the bridge. The underwater robot is used to scan the disease. Then, the two damage identification algorithms of Lit-YOLO-v4 algorithm and 3D point cloud are used to obtain a clear image of the underwater disease. The detailed information of the underwater disease is added to the system, which verifies that the system has a good application prospect in bridge detection. In addition, using the Revit.API interface, the bridge underwater health management system can be given more diversified functions to further improve and optimize the system for bridge detection and maintenance. Conceptualization, X.L. and T.Z. ; methodology, Q.M. ; software, H.S. ; validation, R.S. and M.W. ; formal analysis, R.S. and H.S. ; data curation, M.W. and R.S. ; writing--original draft preparation, R.S., Q.M. and M.W. ; writing--review and editing, Q.M. and M.W; visualisation, H.S. ; supervision, X.L. and T.Z. ; project administration, X.L. ; funding acquisition, X.L. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable.