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On an application of graph neural networks in population based SHM by Tsialiamanis, G.; Mylonas, C.; Chatzi, E.; Wagg, D. J.; Dervilis, N.; Worden, K. is a scholarly article available to read on EtoBox.

What is On an application of graph neural networks in population based SHM about?

Attempts have been made recently in the field of population-based structural health monitoring (PBSHM), to transfer knowledge between SHM models of different structures. The attempts have been focussed on homogeneous and heterogeneous populations. A more general approach to transferring knowledge between structures, is by considering all plausible structures as points on a multidimensional base manifold and building a fibre bundle. The idea is quite powerful, since, a mapping between points in the base manifold and their fibres, the potential states of any arbitrary structure, can be learnt. A smaller scale problem, but still useful, is that of learning a specific point of every fibre, i.e. that corresponding to the undamaged state of structures within a population. Under the framework of PBSHM, a data-driven approach to the aforementioned problem is developed. Structures are converted into graphs and inference is attempted within a population, using a graph neural network (GNN) algorithm. The algorithm solves a major problem existing in such applications. Structures comprise different sizes and are defined as abstract objects, thus attempting to perform inference within a heteroge

Author

Tsialiamanis, G.; Mylonas, C.; Chatzi, E.; Wagg, D. J.; Dervilis, N.; Worden, K.

Published

2022

Language

EN