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.
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: https://op.europa.eu/en/web/eu-vocabularies/euroscivoc.
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: https://op.europa.eu/en/web/eu-vocabularies/euroscivoc.
- engineering and technologycivil engineeringstructural engineeringstructural health monitoring
- medical and health sciencesclinical medicinephysiotherapy
- medical and health sciencesbasic medicinepathology
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Keywords
Programme(s)
- HORIZON.1.2 - Marie Skłodowska-Curie Actions (MSCA) Main Programme
Funding Scheme
HORIZON-TMA-MSCA-PF-EF - HORIZON TMA MSCA Postdoctoral Fellowships - European FellowshipsCoordinator
18071 Granada
Spain