Leveraging Artificial Intelligence to unlock Next-Generation SHM software: Advanced Feature Extraction and Damage Identification
Enrique García‐Macías, Elisa Tomassini, Israel Alejandro Hernández-González, Filippo Ubertini · BER : · 2025
The significant socio-economic impacts of aging infrastructure have driven the growing implementation of Structural Health Monitoring (SHM) worldwide as a key preventive maintenance strategy. However, scaling SHM systems nationwide presents significant hardware and software challenges, particularly in managing densely instrumented structures and deploying effective damage identification algorithms. This explains the circumstance that most SHM software tools are custom-built by specialized research groups, limiting technology transfer. In this context, although still in its early stages, the latest advances in Artificial Intelligence (AI) offer promising solutions to overcome these scalability issues. In this line, this work introduces the latest developments of MOVA/MOSS, a comprehensive SHM software platform developed by the authors that leverages AI to accelerate feature extraction in vibration-based systems and generate digital twins for quasi real-time damage identification. The potential of the developed platform is demonstrated through a real-world structure, the Mendez Nuñez Bridge in Spain, highlighting AI’s potential in facilitating the widespread adoption of SHM.