Challenges and Opportunities in AI-Based Structural Health Monitoring Solutions

Abstract

The integration of advanced Artificial Intelligence (AI) techniques into Structural Health Monitoring (SHM) has transformed the way infrastructure is monitored and maintained. This paper explores several cutting-edge developments in the field, with a particular focus on vision-based SHM, which leverages AI-driven image recognition and video processing to detect and assess structural damage, material degradation, and performance characteristics. Furthermore, Physics-Informed Artificial Intelligence (PIAI) emerges as a powerful approach that combines physics-based modeling with data-driven techniques, ensuring that AI predictions align with fundamental engineering principles. To address the "black-box" nature of traditional AI models, Interpretable Artificial Intelligence (XAI) is gaining importance, providing insights into how and why AI models make specific predictions, thereby increasing trust and adoption in critical SHM applications. The paper also reviews the diverse applications of AI in SHM, such as real-time monitoring, damage classification, predictive maintenance, and autonomous inspections using drones and robotics. However, several challenges and limitations impede widespread implementation, including data quality, model interpretability, computational complexity, and system integration. Lastly, future trends and directions are discussed, highlighting the need for explainable and hybrid models, the expansion of AI-driven autonomous monitoring systems, and the integration of IoT and edge computing technologies. These advancements hold the potential to revolutionize the monitoring and management of critical infrastructure, making AI a key enabler for future SHM systems.