Abstract
Machine learning has been widely used in recent years for various disciplines, including in the field of structural dynamics. Solutions to problems like structural identification are offered by machine learning methods relying only on the data acquired from structures and minimal knowledge of the physics of the structure which is modelled. A major problem of such approaches is the frequent lack of structural data. Inspired by the recently emerging field of population-based structural health monitoring (PBSHM), and the use of transfer learning in this novel field, the current work attempts to create models that are able to transfer knowledge within populations of structures. The approach followed here is meta-learning, which is developed with a view to creating neural network models which are able to exploit knowledge from a population of various tasks to perform well in newly presented tasks, with minimal training and a small number of data samples from the new task. Essentially, the method attempts to perform transfer learning in an automatic manner within the population of tasks. For the purposes of population-based structural modelling, the different tasks refer to different structures. The method is applied here to a population of simulated structures with a view to predicting their responses as a function of some environmental parameters. The meta-learning approach, which is used herein, is the model-agnostic meta-learning (MAML) approach; it is compared to a traditional data-driven modelling approach, that of Gaussian processes, which is a quite effective alternative when few data samples are available for a problem. It is observed that the models trained using meta-learning approaches are able to outperform conventional machine learning methods regarding inference about structures of the population, for which only a small number of samples are available. Moreover, the models prove to learn part of the physics of the problem, making them more robust than plain machine learning algorithms. Another advantage of the methods is that the structures do not need to be parametrised in order for the knowledge transfer to be performed.
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Acknowledgements
The authors wish to gratefully acknowledge support for this work through grants from the Engineering and Physical Sciences Research Council (EPSRC), UK, via the Programme Grant EP/R006768/. For the purpose of open access, the authors have applied a Creative Commons Attribution (CC BY) licence to any author accepted manuscript version arising.
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Tsialiamanis, G., Dervilis, N., Wagg, D.J., Worden, K. (2023). A Meta-Learning Approach to Population-Based Modelling of Structures. In: Madarshahian, R., Hemez, F. (eds) Data Science in Engineering, Volume 10. SEM 2023. Conference Proceedings of the Society for Experimental Mechanics Series. Springer, Cham. https://doi.org/10.1007/978-3-031-34946-1_8
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