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Condition Assessment and Damage Localisation in Fluid-Filled Buried Sandwich (FFBS) Pipe Using Guided Wave and Impedance Fusion Technique (GIFT) Under Various Exposure Conditions

Objective

The safe and reliable transport of clean fuels such as hydrogen and biofuels depends on pipelines that can withstand demanding service conditions. Sandwich-structured pipelines are focused due to their unprecedented lightweight properties and superior mechanical resilience. However, buried sandwich-pressurised pipelines face persistent vulnerabilities, including embrittlement, corrosion, and delamination, that can threaten both environmental safety and energy reliability. To mitigate these risks, the project proposes an innovative, multidisciplinary Structural Health Monitoring (SHM) system for the pipelines by integrating cutting-edge non-destructive techniques, Guided Wave (GW) propagation and Electromechanical Impedance (EMI) with advanced machine learning (ML) and data fusion for robust, real-time monitoring and damage detection. Execution consists of four coordinated levels: i. a comprehensive numerical model to simulate the pipeline responses under varied operational and environmental conditions, ii. Experimental setup optimised for real-world deployment to collect high-fidelity sensor data under controlled and field conditions. iii. development of a hybrid ML-driven decision framework enhanced by data fusion for robust, accurate, and real-time damage monitoring. iv. Hybrid probability-weighted RAPID imaging tool for precise defect visualisation and localisation, elevating the sensitivity and accuracy of the damage detection system. The project’s multidisciplinary approach combines solid and fluid mechanics, electrostatics, signal processing, and advanced data analytics, delivering improved diagnostic reliability, predictive capabilities, and adaptable tools for other fluid storage applications, fully supporting EU sustainable energy objectives. Dedicated knowledge transfer to the host institution and structured training in advanced NDE, modelling, and AI-driven analytics, strengthening the fellow's skills and supporting career development in research.

Keywords

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Programme(s)

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Topic(s)

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Funding Scheme

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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.

(opens in new window) HORIZON-MSCA-2025-PF

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Coordinator

KAUNO TECHNOLOGIJOS UNIVERSITETAS
Net EU contribution

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.

€ 181 136,16
Address
K DONELAICIO 73
LT-44029 Kaunas
Lithuania

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Region
Lietuva Vidurio ir vakarų Lietuvos regionas Kauno apskritis
Activity type
Higher or Secondary Education Establishments
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Total cost

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.

No data

Partners (2)