SOURCE-LINKED INTELLIGENCE

Uncertainty-aware Graph Learning Approach for Sparse-Sensing Structural Health Monitoring

CORDIS · observation · Publication date unknown

sensing schemes leave vast portions of structures unmonitored and vulnerable to undetected damage, which necessitates full-field response reconstruction to gain systematic structural insight. Classic machine learning (ML) approaches struggle with the ‘black-box’ issue and topological blindness in complex connectivity patterns, which limit their prediction accuracy and model applicability. The proposed project, ‘Uncertainty-aware Graph Learning Approach for Sparse-Sensing Structural Health Monitoring’ (U-GLASS), aims to develop a reliable physics-informed graph learning framework while accounting for uncertainty that addresses fundamental sparse-sensing limitations plaguing current SHM. To implement the project, four sub-objectives guided by the framework include: 1) Develop a reduced-order model (ROM) with parametric uncertainties and reduction errors quantification; 2) Construct a GNN embedded with ROM-derived physics and topological knowledge; 3) Develop a training protocol for GNN to reconstruct full-field responses from sparse measurements; and 4) Quantify structural damage sever

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recordType

award

status

SIGNED

region

EU

value

292118.88

unit

EUR