Project description
Technology that listens to transport structures before damage becomes danger
Every passing train or vehicle makes bridges and railways vibrate, and those vibrations carry clues about hidden structural damage. Supported by the Marie Skłodowska-Curie Actions programme, the PiGLAD project will develop an AI-powered framework that combines moving-load information with acceleration responses to detect anomalies earlier and more reliably. Unlike methods that examine responses alone, it uses both what acts on a structure and how the structure reacts. By linking physics-informed machine learning, domain adaptation and generative AI, PiGLAD will create synthetic damage-state data when real examples are scarce. Tested from the laboratory to operational railway bridges, it aims to support safer, smarter and more cost-effective infrastructure maintenance across busy transport networks in Europe.
Objective
Transport infrastructures are crucial for the mobility of people and goods. Beam-like transport infrastructures like railways and bridges, subjected to moving loads, face increasing safety risks due to growing traffic demands. Timely detection of structural anomalies is therefore essential for risk mitigation and condition-based maintenance. This project aims to develop and experimentally validate an innovative input-output structural anomaly detection framework that integrates moving loads and acceleration responses. In contrast to extensively studied approaches that only use structural responses, the input-output framework, free from external load assumptions, can potentially transform anomaly detection from an ill-posed inverse problem into a manageable forward problem. Despite its promise, such a framework faces two 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 performance metrics, forming recommendations for the framework design.
Fields of science (EuroSciVoc)
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.
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.
- natural sciences computer and information sciences artificial intelligence generative artificial intelligence
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Keywords
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)
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
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)
MAIN PROGRAMME
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Topic(s)
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.
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
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.
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
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
(opens in new window) HORIZON-MSCA-2025-PF
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Net EU financial contribution. The sum of money that the participant receives, deducted by the EU contribution to its linked third party. It considers the distribution of the EU financial contribution between direct beneficiaries of the project and other types of participants, like third-party participants.
2628 CN Delft
Netherlands
The total costs incurred by this organisation to participate in the project, including direct and indirect costs. This amount is a subset of the overall project budget.