Project description
Reliable AI for structural health monitoring
Current structural health monitoring systems rely on limited sensor data and black-box models, making it difficult to reliably detect hidden damage in large infrastructure. Supported by the Marie Skłodowska-Curie Actions programme, the U-GLASS project will develop a physics-informed, uncertainty-aware graph learning framework to improve structural health monitoring using sparse sensor data. By combining reduced-order physical models with graph neural networks, it will reconstruct full structural responses and identify damage more accurately in complex infrastructure systems. The approach will be tested on simulations, laboratory structures and real-world cases such as railway bridges, with the aim of improving the safety and long-term management of critical infrastructure.
Objective
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.
Fields of science (EuroSciVoc)
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CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
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Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)
Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)
Programme(s)
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Multi-annual funding programmes that define the EU’s priorities for research and innovation.
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HORIZON.1.2 - Marie Skłodowska-Curie Actions (MSCA)
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Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Funding Scheme
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Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
HORIZON-TMA-MSCA-PF-EF - HORIZON TMA MSCA Postdoctoral Fellowships - European Fellowships
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Call for proposal
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(opens in new window) HORIZON-MSCA-2025-PF
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8092 Zuerich
Switzerland
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