Towards an active-learning approach to resource allocation for population-based damage prognosis

• URL: http://arxiv.org/abs/2409.18572v1
• Date: Fri, 27 Sep 2024 09:15:44 GMT
• Title: Towards an active-learning approach to resource allocation for population-based damage prognosis
• Authors: George Tsialiamanis, Keith Worden, Nikolaos Dervilis, Aidan J Hughes,
• Abstract summary: Damage prognosis is arguably one of the most difficult tasks of structural health monitoring (SHM) To address common problems of damage prognosis, a population-based SHM approach is adopted in the current work. The prognosis problem is considered as an information-sharing problem where data from past structures are exploited to make more accurate inferences regarding currently-degrading structures.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Damage prognosis is, arguably, one of the most difficult tasks of structural health monitoring (SHM). To address common problems of damage prognosis, a population-based SHM (PBSHM) approach is adopted in the current work. In this approach the prognosis problem is considered as an information-sharing problem where data from past structures are exploited to make more accurate inferences regarding currently-degrading structures. For a given population, there may exist restrictions on the resources available to conduct monitoring; thus, the current work studies the problem of allocating such resources within a population of degrading structures with a view to maximising the damage-prognosis accuracy. The challenges of the current framework are mainly associated with the inference of outliers on the level of damage evolution, given partial data from the damage-evolution phenomenon. The current approach considers an initial population of structures for which damage evolution is extensively observed. Subsequently, a second population of structures with evolving damage is considered for which two monitoring systems are available, a low-availability and high-fidelity (low-uncertainty) one, and a widely-available and low-fidelity (high-uncertainty) one. The task of the current work is to follow an active-learning approach to identify the structures to which the high-fidelity system should be assigned in order to enhance the predictive capabilities of the machine-learning model throughout the population.

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