SOURCE-LINKED INTELLIGENCE

Population-based Structural health monitoring: Advancing Foundations, mEthods, and real-world validations

CORDIS · observation · Publication date unknown

idelines for PBSHM applications. Second, it will develop adaptable, interpretable, and data-efficient learning methods tailored for PBSHM by integrating Graph Neural Networks (GNNs), Physics-Enhanced Machine Learning (PEML) and transfer learning. Third, to ensure practical relevance, the developed thresholds, guidelines, and methods will be validated using available real-world datasets, including data from over 30 wind turbines and more than 400 short- and medium-span bridges. The outcomes will deliver accurate, robust, and scalable PBSHM methodologies that enable robust anomaly detection and support proactive maintenance for industry and government partners. Scientifically, the project will extend SHM beyond the conventional individual-based paradigm. Societally and economically, it will promote safer, more resilient, and sustainable infrastructure systems. Population-Based Structural Health Monitoring (PBSHM); Graph Neural Networks (GNN); Physics-Enhanced Machine Learning (PEML); Transfer Learning; Wind Turbines; Short- and Medium-Span Bridges

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recordType

award

status

SIGNED

region

EU

value

292118.88

unit

EUR