## 6 Conclusions and Future Scope  
This study has reviewed the AI-driven strategies for SHM of concrete bridges due to their necessity in today's world. A narrative review was conducted, and several relevant studies were explored. It was observed that AI-based SHM methodologies, such as CNNs and Faster R-CNN integrated with UAVs and LiDAR systems, have demonstrated superior accuracy in identifying critical structural defects. However, AI-driven SHM still faces significant challenges, including false positive detections, data quality and environmental variations, high-dimensional data complexity, and limited generalisability of models. These challenges underline the necessity for further research and the development of advanced AI methods. This review is crucial because it identifies explicit gaps in conventional SHM and highlights how AI-driven methodologies significantly enhance inspection accuracy, reliability, and decision-making, thus improving overall structural integrity and public safety. Such AI-driven solutions are necessary for the proactive and precise assessment of concrete bridges, leading to improved reliability and extended service life of bridge infrastructures worldwide. Data analysis has become an important part of SHM in bridge engineering and is expected to remain a fundamental part of infrastructure maintenance in the future. AI and ML are transforming SHM, so future research should focus on the following:  
* Integrating multimodal data sources like LiDAR, infrared thermography, and ground penetrating radar into AI-driven bridge inspection models. Integrating these data sources improves fault detection, damage categorisation, and bridge integrity evaluation. For resilient, real-time monitoring solutions, self-learning models that adapt to changing structural conditions without manual retraining are essential.
* Examine how temperature, humidity, and air pollutants affect AI-driven fault detection systems to improve model generalisation across many climates.  
* Autonomous UAV-based bridge inspection systems can be used with real-time AI analytics. UAVs could automate bridge inspections. However, flight stability, data processing latency, and problem localisation remain issues. Future advances should concentrate on edge computing with UAV-based SHM systems to enable onboard AI inference, reduce cloud-based post-processing, and improve real-time decision-making.
* Adding XAI methods to SHM models to increase transparency and regulatory acceptance of defect assessment. Standardised AI frameworks and benchmarking datasets are needed to scale and replicate AI-driven structural health monitoring solutions across infrastructure systems.  
Conceptualization, V.P. and C.J.D.; methodology, V.P., C.J.D. and M.A.M.; validation, C.J.D., R.P.B., D.S., W.D. and J.S.; formal analysis, V.P., C.J.D. and M.A.M.; investigation, V.P., C.J.D. and M.A.M.; resources, V.P., C.J.D., M.A.M., R.P.B. and D.S.; data curation, V.P., C.J.D. and M.A.M.; writing--original draft preparation, V.P., C.J.D., M.A.M., J.S. and R.P.B.; writing--review and editing, C.J.D., R.P.B., D.S., W.D. and J.S.; visualization V.P., C.J.D. and M.A.M.; supervision, C.J.D. and J.S.; project administration, C.J.D. and J.S.; funding acquisition, C.J.D. and J.S. All authors have read and agreed to the published version of the manuscript.  
The project (DiHICs) received funding from Xjenza Malta, grant number SINO-MALTA2023-16, and the Ministry for Science and Technology of the People's Republic of China (MOST) through the SINO-MALTA Fund 2023 Call (Science and Technology Cooperation). The APC was funded by Xjenza Malta.  
The authors declare no conflict of interest.