## 1 Introduction
It is required to know the prior state of the bridge for accurate estimation of the impact and to determine damage extension and location. The SHM system is used to analyse and compare a new model of the bridge with an older one and figure out any discrepancies between the two successive models [30]. Under the broad category, SHM can be divided into model-driven and data-driven types. Model-driven SHM is based on a finite element analysis model and sensitivity matrix [31; 32]. Major hindrances to the model-driven SHM model are that it is time-consuming, the calculations are generally complex, and the model needs validation on an experimental basis [33; 34]. Another hindrance includes vibrations in the surrounding environment, which induce measurement noise and false boundaries. Data-driven SHM reduces ambiguity and unan \begin{table}
\begin{tabular}{c c c c} \hline \hline
**S. No.** & **Technical Aspects of SHM** & **Function Performed** & **References** \\ \hline
1. & Impregnation of sensors & Monitor the condition of the specific structure & [24; 25; 26] \\ \hline
2. & Data acquisition techniques & Gather real-time data that help to track structural integrity & [24; 25] \\ \hline
3. & Signal processing tools & Help to analyse data collected in the previous step & [27] \\ \hline
4. & Damage detection & Assist in damage detection & [24; 27] \\ \hline
5. & Remote monitoring & Help in monitoring structures remotely & [24; 26] \\ \hline
6. & Acoustic emission & Detect and locate different damage in reinforced concrete structure & [26] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Structural health monitoring techniques. ticipated issues attributed to computational intelligence [35]. Data-driven techniques can be applied along with model-driven approaches [36]. Data-driven techniques in SHM offer significant advantages but also face the following limitations, and their solutions are discussed below:  
1. Challenges in Dataset Collection: Data-driven techniques require large datasets for thorough training. The lengthy and costly process of collecting run-to-failure data makes it impractical for novel systems. Guaranteeing that the data encompasses all possible future operating scenarios is nearly impossible, resulting in model prediction efficacy issues [37]. 2. Data Quality Issues: The effectiveness of data-driven SHM techniques heavily depends on the quality of the collected data. Elements such as noise, missing data, and inconsistencies may adversely affect the effectiveness of these methods. Despite the critical significance of data quality, there has been very limited focus on establishing comprehensive data quality indicators and metrics within the SHM community [38]. 3. High-Dimensional Feature Spaces: Data-driven techniques often analyse several features, creating high-dimensional feature spaces. Complexity may limit the model's generalisation and require dimensionality reduction, which may lose important information [39]. 4. False Positives and Negatives: Image-based SHM methods using computer vision and Machine Learning (ML) can produce false positives and negatives, especially in changing environments. This vulnerability can misread structural integrity, especially when damage events are rare [40]. The challenges in the dataset collection can be tackled using the following approaches:  
* Transfer learning and synthetic data generation [41]. * Deploying large-scale sensor networks across multiple infrastructures can help diversify datasets [41]. * Integrating physical knowledge (such as finite element models) with data-driven approaches reduces dependence on large labelled datasets [42]. * Emphasising data collection for ambiguous cases diminishes the necessity for comprehensive data acquisition [43]. The challenges in data quality issues can be solved using the following approaches [44]:  
* AI-based imputation methods, such as autoencoders or KNN imputation, may effectively manage missing or noisy data.