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Structural health monitoring (SHM) is crucial for ensuring the safety and longevity of infrastructure by detecting and assessing structural damage through advanced sensing and analytical techniques. The integration of artificial intelligence (AI) into SHM offers significant advantages over traditional methods, enabling real-time, continuous monitoring and predictive maintenance. This approach enhances the accuracy of structural issue detection, allowing for timely intervention before issues become critical, thereby reducing maintenance costs and improving safety. This article examines the application of various AI methods, including supervised, unsupervised, and deep learning, to optimise SHM under different conditions. By exploring hybrid approaches that combine these AI techniques, this study demonstrates how such integrations can improve the accuracy, adaptability, and reliability of SHM systems, ultimately contributing to safer and more cost-effective infrastructure management.

Keywords: Structural Health Monitoring; Supervised Learning; Unsupervised Learning; Deep Learning