## 3 Bridge Inspection
### Structural Health Monitoring  
The SHM of bridges is neither predictable nor monotonic. Certain uncertainties while the bridge is being constructed create structural behaviour changes that were not included in the bridge's design. After completion and being put to active use, bridges are subjected to evolving patterns of loads and other actions. Integrated SHM systems have enhanced bridges' maintenance, management, and decision-making processes through continuous monitoring and assessment of operational conditions [79]. The uncertainties created during design and construction or while the bridge is in active use all bring unexpected challenges to the engineers and institutions in charge of the bridge's safety, maintenance, and operation. Superficial maintenance by only surface observation and relying on the type of model of the bridge is risky and will also hamper efficient resource utilisation. However, regular inspection can be highly beneficial in deciding and detecting uncertainties, although this also has limitations. The observation of the structure surface is performed for a short time, and then a long inactive phase occurs, with damages that go unnoticed in the manual inspection. SHM aims to render reliable data on the actual conditions of the bridge, scrutinise its evolution, and detect any appearance of new degradation [80]. Different types of sensors can be installed according to the relevance of the structural condition and other environmental factors, which will render a clear and detailed picture of the state and evolution of the bridge. When this observation is carried out with the help of digital technology and devices, it is known as digital health monitoring and can perform better than modelling and visual inspection [19]. Having healthy bridges is a prominent need everywhere, including in coastal regions. The appearance and presence of cracks are typical in bridges, which can be due to many factors like weathering, ageing, improper loading, and, in the case of coastal bridges, constant proximity to saline water. Several methods have facilitated crack detection in civil infrastructures like bridges, roads, buildings, dams, tunnels, walls, etc., including hiring human visual inspection [81]. To overcome human error, several studies are now being conducted to replace human detection with scientific and computer-aided techniques that include image processing [82], computer vision [83], and classical ML [84]. None of the methods are absolute and all have their respective limitations [85]. Nowadays, AI has marked its presence in several types of fields, including AI-powered disruptive technology [86], CNNs [87; 88], and many other object detection techniques [9]. The role of AI-powered technologies can be better understood by looking at a few examples, like [PERSON] et al. [89], who applied CNN to reinforced concrete bridges in China for multi-category damage detection and recognition. [PERSON] et al. [90] conducted a study on cracks on bridges using drones and Faster-RCNN. The results of this study indicate that automated bridge fracture detection with UAVs and Faster R-CNN can achieve good efficiency while preserving high accuracy. CNN-based crack detection has encountered two pivotal issues: complex network architecture and a high number of training parameters [91]. The CNN model consists of millions of parameters (weights and biases) that must be learned during training. Thus, it is right to say that the optimisation problem is adjusting the model's internal parameters (e.g., weights) and the associated computational cost. These pivotal issues can be solved by applying an object detection method broadly classified as a one-stage or two-stage object detection model. There is a simple differentiation between these two types of models, where the one-stage model consists of a single shot-multibox detector (SSD) [92] along with a series of You Only Look Once (YOLO) architectures [93].