## 5 Discussion  
AI-driven SHM has significantly increased bridge inspection efficiency, precision, and automation, alleviating many manual restrictions. Despite these advances, several challenges and research gaps remain to improve the robustness and practicality of AI-based structural health monitoring methods. The identification, categorisation, and quantification of concrete bridge cracks and other structural flaws have significantly improved because of AI-driven methods like DL and CNNs. UAVs and AI-driven image processing have permitted broad and rapid inspections, reducing manual work and traffic disruptions [56]. In addition, advanced image processing methods together with YOLO [153; 158], Faster R-CNN [133; 139; 141; 142], and U-Net [142; 153; 157; 169] can detect cracks and damage with a high precision and accuracy of 95%. AI-driven predictive maintenance allows authorities to eliminate likely structural flaws before they escalate, enhancing bridge safety and longevity. AI-driven SHM techniques remain limited in practice despite these advancements. Training dataset quality and diversity are major issues. AI models need large, high-quality annotated datasets to generalise across climates, structural materials, and bridge types. Many datasets that train these models lack diversity and fail to simulate real-world unpredictability, affecting model resilience and adaptability. Image quality, including illumination, weather, occlusions, and structural details, greatly impacts defect detection in AI models [175]. A major limitation is false positives and negatives. AI models are accurate but can make mistakes, especially when distinguishing cracks from surface defects or shadows [176]. Misclassification might lead to unnecessary maintenance or undetected significant damages that pose a safety risk. Real-time SHM techniques also face processing challenges. Advanced Deep Learning models require large computational resources for training and inference, making them difficult to execute on edge devices or real-time processing systems. The lack of explainability and interpretability in AI models, especially Deep Learning-based ones, limits their use in SHM. Engineers and decision-makers need explicit and interpretable AI models to understand fault discovery and evaluation. AI-driven SHM has many research gaps. First, standardised benchmark datasets with different bridge structures, weather conditions, and structural defects are needed. Current datasets are often regional or narrow, making constructing AI models that can generalise across numerous geographies and bridge designs challenging. Multimodal data fusion is a new field with little research. Failure identification may be improved by combining infrared thermography, LiDAR, GPR, and acoustic emission sensors with Artificial Intelligence models [177]. Together, sensor and optical data can better assess bridge health. AI model practicality is a major research need. Most AI models are developed on curated datasets and may perform poorly in complex field scenarios. Domain adaptation and transfer learning can increase AI models' flexibility to innovative bridge architectures without costly retraining. Numerous aspects will shape AI-driven SHM. Developing self-supervised and unsupervised learning methods is promising. These methods allow models to learn from unannotated data, making AI-driven SHM more scalable and cost-effective. Another trend is using Explainable AI (XAI) to create DL models that are more interpretable. Transparency in AI decision-making helps engineers and stakeholders understand fault evaluations and trust AI-driven structural health monitoring solutions. Additionally, edge AI- and IoT-based SHM systems are growing [56]. AI models on low-power edge devices provide rapid defect identification and monitoring without centralised computer infrastructure, which can significantly reduce latency and increase bridge inspection system responsiveness. SHM, with digital twin technology, has a bright future.