SOURCE-LINKED INTELLIGENCE

Physics-informed Generative Learning for Anomaly Detection in Transport Infrastructures under Moving Loads

CORDIS · observation · Publication date unknown

common challenges in structural anomaly detection: data discrepancies between numerical and real-world domains and the scarcity of data in damaged states. Domain adaptation (DA) and physics-informed machine learning (PIML) have demonstrated great promise in addressing these challenges. However, current PIML studies are not directly applicable to anomaly detection when multiple damage states are involved, while existing DA approaches fall short in transferring physical knowledge across numerical and real-world domains. To overcome this, we propose a physics-informed generative learning model for the input-output anomaly detection framework. This model will generate synthetic structural responses in multiple damage states, facilitating damage detection through comparison with actual measurements. The framework will be validated from laboratory to in-service railway bridges using state-of-the-art V-Track and CTO Measurement Train. In addition, a comprehensive reliability analysis and uncertainty quantification will be performed to identify the key factors that impact its various perfor

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recordType

award

status

SIGNED

region

EU

value

232916.16

unit

EUR