## 1 Introduction
* Employing real-time anomaly detection methods, such as statistical outlier detection and AI-driven validation, can improve data integrity. * Establishing data quality benchmarks guarantees uniformity in SHM datasets. The challenges associated with high-dimensional feature spaces can be mitigated using the following approaches [45]:  
* SHAP (SHapley Additive exPlanations), Principal Component Analysis (PCA), and autoencoders effectively preserve pertinent features while diminishing dimensionality. * Developing domain-specific characteristics (e.g., modal frequencies, strain energy measures) enhances model precision. * Sparse coding and dictionary learning facilitate the extraction of significant features while preserving information integrity. The challenges associated with false positives and negatives can be minimised using the following approaches [46]:  
* Integrating physics-based models with AI classifiers can enhance reliability. * Employing context-sensitive thresholding that adapts according to environmental factors. * Employing Bayesian Deep Learning (DL) and ensemble techniques (e.g., Random Forest, XGBoost) to enhance reliability. * Regular model retraining with updated data mitigates environmental bias. AI-driven SHM employs ML and DL techniques to evaluate structural integrity. Principal problems encompass damage identification and categorisation, defect localisation, and the prediction of residual usable life. AI models analyse extensive sensor data, extract features, and identify abnormalities while synthesising multimodal data from accelerometers, LiDAR, and computer vision [47]. Real-time SHM utilising edge AI, uncertainty quantification, and transfer learning improves adaptability across various structures. Three-dimensional reconstruction and digital twins facilitate virtual inspections and predictive maintenance. These AI methodologies enhance precision, efficacy, and decision-making in SHM applications. Integrating AI into SHM boosts the reliability of concrete bridge assessments through automated inspections, refined data analysis, and the facilitation of predictive maintenance programmes [48]. Conventional approaches like manual inspections and vibration-based techniques have proved essential in SHM of concrete bridges, but issues with precision and efficacy are frequently encountered. Although specific accuracy percentages for these conventional procedures are not constantly recorded, they are typically regarded as less precise due to human error and environmental impacts. Conversely, AI-driven SHM methodologies have exhibited substantial enhancements in precision. The literature indicated that Machine Learning algorithms can identify cracks and fatigue in bridge structures with over 90% accuracy [49]. Concrete is the construction material most exploited globally in structural and infrastructure works. It has been a widely used construction material in bridge construction over the past decades, and several bridge structures rely on cement-based materials [50]. Concrete offers several possibilities for different types and forms of bridge construction. Most bridges globally consist of concrete or rely on composite solutions with concrete featuring as a primary material; due to its versatility, strength, and durability, it lends itself to different typologies of reinforced and prestressed, including post-tensioned, concrete bridge construction [51]. For instance, a study conducted by [PERSON] et al. [52] in six European countries showed that there were more concrete-based bridges constructed over the past decade than steel bridges. Similarly, according to the United States (U.S.) Department of Transportation Federal Highway Administration [53], concrete bridges significantly outnumber steel bridges in the U.S. Therefore, this supports the need for robust inspection and assessment methods, justifying the global efforts in inspection and monitoring of concrete bridges.