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Pathology-based Structural Health Monitoring

Project description

Digital twins for pathology-based structural health monitoring

Structural health monitoring (SHM) of cultural heritage involves regular assessment of a structure’s integrity to detect changes and warn of potential damage. However, conventional approaches can present uncertainties in decision-making because they are usually based on a single model of the heritage asset. With the support of the Marie Skłodowska-Curie Actions programme, the Path-B SHM project proposes a novel pathology-based SHM approach using multiclass digital twins. Systematic diagnosis is carried out onsite to identify potential structural pathologies. High-fidelity scenario-based numerical models will then be formulated for each identified pathology. In turn, AI-based tools will substitute numerical models with effective meta-models to create a multiclass digital twin that can predict the likelihood of a pathology occurring when continuous monitoring data is provided.

Objective

Structural health monitoring (SHM) methods provide a comprehensive approach to the condition-based maintenance of Cultural Heritage (CH) constructions, aiming to extend their lifespan through optimized rehabilitation interventions. While physics-based damage identification is the most effective approach for comprehensive damage assessment, it has well-documented limitations. Specifically, classical methods often rely on a single model of the CH asset, which introduces significant uncertainties in decision-making. To address this challenge, this Action introduces a pioneering SHM paradigm referred to as pathology-based SHM (Path-B SHM) using multi-class digital twins (DTs)—a population of competing, efficient meta-models. The methodology begins with a systematic diagnosis, including site visits supplemented by non-destructive testing (NDT), to identify the most probable structural pathologies the asset may experience throughout its lifespan. High-fidelity scenario-based numerical models are then developed for each identified pathology. To enable real-time damage identification and simultaneous inverse calibration of all scenario-based models, these numerical models are replaced by computationally efficient meta-models constructed using artificial intelligence (AI) tools which form a multi-class DT. The resulting DT is capable of inferring, in quasi real-time, the probability of occurrence of the identified potential damage pathologies and their severity when supplied with continuous monitoring data. If an anomaly is detected, a Bayesian model selection approach identifies the damage mechanism, selecting the meta-model that explains the experimental data with the highest likelihood. This approach, therefore, enables rapid, robust, and comprehensive damage identification. Path-B SHM has the potential to revolutionize and advance the frontiers of SHM, offering unprecedented opportunities for data-informed decision-making in the preservation of CH constructions.

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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-2024-PF-01

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Coordinator

UNIVERSIDAD DE GRANADA
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.

€ 209 914,56
Address
CUESTA DEL HOSPICIO SN
18071 GRANADA
Spain

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Region
Sur Andalucía Granada
Activity type
Higher or Secondary Education Establishments
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Total cost

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