## 2 Bridge Structural Health Monitoring (SHM) System
### Challenges and Different Sorts of SHM Systems  
Many variables combine to make SHM a challenging procedure to perform. Data collection, sensors that are not always accurate and are not all the same, and the use of different ways to analyze data all make this task a lot harder. Visual inspections are still used for the great majority of in-service bridge data collection, despite the extensive academic work on bridge deterioration detection and identification [18; 55]. There are several problems with this traditional approach of SHM, such as the high inaccuracy of this technique, the inaccessibility of certain bridge sections, the failure to detect many localized failures, and the inability to identify situations when damage originates from inside structural components. But in recent years, with the progress made in the scientific field, the SHM system of bridges has undergone fundamental changes. Advances in technology, such as optical sensors, lasers, image processing systems, and cheap sensors have allowed for advances in SHM system performance [56; 57; 58; 59; 60; 61]. Also, in recent years, other technologies like blockchain technology and information security, as well as the rise of 5G Internet and the IoT, have made many researchers interested in using these technologies [62; 63; 64; 65; 66; 67; 68]. Using current methods may have a number of benefits, such as the ability to keep an eye on the structure in real time, the ability to find damage early on, shorter inspection times and costs, and lower repair and reinforcement costs. Generally, the best SHM system will be low-cost, non-invasive, and fully automated to eliminate the potential for human error. In particular, the bridge should not have to be shut down for any phase of the installation or operation of the system [69]. In general, SHM can be considered for two systems, including Model-driven SHM and data-driven SHM. The undamaged condition of the building must be assumed or created in order to locate the damage, as was indicated before. In Model-driven SHM, for damage detection, the vibration data is analyzed using a system identification paradigm to find the modal properties and evaluate trends. Researchers suggested using sensitivity matrices, which are the backbone of the new field of model updating, to find damage. The models used in model-driven SHM often use finite element analysis (FEA) [70]. By including the discrepancy between FEA predictions and experimental data, sensitivity matrices are utilized to refine and improve upon the original design. Appropriate research has been done using model-based SHM on bridges. It should be noted that this technique has not only been used in real studies but also works in the lab [71; 72]. Despite its many benefits, the Model-driven SHM has certain drawbacks as well. Model updates require time, and Model-driven calculations are difficult and need to be confirmed with experimental data. Data-driven methods, on the other hand, are excellent at dealing with uncertainty and unpredictability. Recently, with the advent of computational intelligence and the use of data-driven methodologies based on machine learning techniques, SMH systems have made significant strides. There are a number of methods for keeping SHM system uncertainty under control. The use of AI and ML techniques is becoming more popular due to its efficacy. Due to their efficacy, these methods have seen widespread application in recent years. Building damage can be found using either only data-driven methods or a combination of data-driven methods and model-driven methods [73; 74]. In recent decades, the rapid growth of technology as well as increasing progress in the field of artificial intelligence have had a direct and indirect effect on increasing the performance of SHM systems, especially data-driven SHM systems. So, this study looks into how artificial intelligence and other technologies affect data-driven SHM systems.