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

Información del proyecto

U-GLASS

Identificador del acuerdo de subvención: 101274953

DOI

10.3030/101274953

Fecha de la firma de la CE 10 Marzo 2026

Fecha de inicio 1 Septiembre 2026

Fecha de finalización 31 Agosto 2028

Financiado con arreglo a

• Marie Skłodowska-Curie Actions (MSCA)

Coste total

Sin datos

Aportación de la UE

€ 292 118,88

Coordinado por

EIDGENOESSISCHE TECHNISCHE HOCHSCHULE ZUERICH
Switzerland

Objetivo

Structural health monitoring (SHM) emerged from the critical need to continuously assess the integrity and performance of aging infrastructure to ensure public safety. Current sparse-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 severity/location using reconstructed responses on multi-scale systems. The framework will be first implemented on multi-scale simulation/laboratory cases and then applied to real-world systems such as Swiss railway bridges. By harmonizing uncertainty-aware ROM/topological physics with ML, the goal is to enable physics-enhanced full-field response reconstruction from sparse sensor data, transforming limited sensing data into actionable engineering insights. The magnitude of U-GLASS’s contribution is ultimately measured not just in technical innovation, but in its potential to fundamentally reshape how society manages critical infrastructure systems for future generations.

Ámbito científico (EuroSciVoc)

• ciencias naturales › informática y ciencias de la información › inteligencia artificial › aprendizaje automático

Palabras clave

• Structural health monitoring (SHM)
• physics-informed graph learning
• reduced-order modeling (ROM)
• uncertainty-aware

Programa(s)

• HORIZON.1.2 - Marie Skłodowska-Curie Actions (MSCA) PROGRAMA PRINCIPAL
  

Tema(s)

• HORIZON-MSCA-2025-PF-01-01 - MSCA Postdoctoral Fellowships 2025
  

Régimen de financiación

HORIZON-TMA-MSCA-PF-EF - HORIZON TMA MSCA Postdoctoral Fellowships - European Fellowships

Convocatoria de propuestas

HORIZON-MSCA-2025-PF

Coordinador

EIDGENOESSISCHE TECHNISCHE HOCHSCHULE ZUERICH

• Aportación neta de la UEn: € 292 118,88
• Dirección: Raemistrasse 101 ; 8092 Zuerich ; Suiza
• Región: Schweiz/Suisse/Svizzera › Zürich › Zürich
• Tipo de actividad: Higher or Secondary Education Establishments
• Coste total: Sin datos