# Identification of Underwater Structural Bridge Damage and BIM-Based Bridge Damage Management
###### Abstract  
The number of bridges in operation has increased. Along with the increase in the length of time bridges are in service, the structural safety of the bridges also decreases. Bridge substructure is a key component of bridges, but there are few studies on safety management and identification of water bridge substructure damage. Deep learning is a focus of research in the field of target detection, and this document lightens YOLO-v4 to achieve precise and intelligent determination of concrete cracks. This was combined with a point cloud algorithm to provide a three-dimensional estimate of faulty lesions. Finally, the BIM was combined with the method of identifying the underwater structure of the deck. Based on Revit, an integrated management system for underwater bridge structures is put in place. Performing detailed bridge damage management includes (1) 3D visualization of the bridge detail model view, (2) establishment of a bridge damage database, (3) bridge damage management, and (4) management of the comprehensive underwater bridge inspection cycle. BIM; target detection; 3D point cloud; bridge underwater structure damage; bridge damage management system +
Footnote †: journal: Medical Imaging  
Delgado modelled a structural performance inspection system in a building information modelling environment, thus allowing sensor data to be visualised directly on the BIM model [20]; [PERSON] et al. built a BIM model to connect and analyse data related to bridge inspection, assessment, and management to help decision makers better manage bridge health information [21]; [PERSON] marked the damage information of a steel arch bridge in the 3D model drawn by Revit to achieve rapid automatic assessment of the overall technical condition of the bridge and significantly speed up the bridge inspection [22]. BIM technology, with its convenient interoperability, data information integration, and imageability, has greatly improved the efficiency of bridge inspection and structural assessment. However, in the process of system construction, the bridge management system is often set up in a comprehensive way for each structure of the bridge, without considering the complex environment that distinguishes underwater structures from above-water structures, such as sewage environment, sediment, special damages, and others [23; 24; 25; 26; 27; 28]. Underwater structures are an essential part of bridges, and their condition significantly affects the safety of their use. As a result of long-term service and external environmental factors, damage to underwater structures has gradually emerged, such as the uneven settlement of foundations due to water scour, cracking and spalling of concrete, and others [29; 30; 31]. The New York State Department of Transportation's underwater inspection of local bridges in service revealed that more than half of the bridge collapses were due to large structural damage to the substructure [32]. Underwater structural damage poses a significant threat to the bridges' overall structural safety and durability, and scientific monitoring and assessment of the structural health of bridges are urgently needed to ensure the safe operation of bridges. Therefore, it is necessary to develop a bridge underwater structural health management system for the particular characteristics of underwater structures. With the rapid development of computer technology, bridge inspection techniques have become progressively more intelligent, with the emergence of non-destructive testing techniques such as image recognition technology and machine learning algorithms. Some researchers have used bridge damage images to automate the processing of bridge damage images through techniques such as deep learning, where deep learning algorithms with convolutional neural networks (CNNs) as the core can extract features from many images to achieve the detection of additional damages [33; 34; 35; 36]; YOLO-v4 is one of the CNNs.