  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 heterogeneous population
is not trivial. The proposed approach is tested in a simulated population of
trusses. The goal of the application is to predict the first natural frequency
of trusses of different sizes, across different environmental temperatures and
having different bar member types. After training the GNN using part of the
total population, it was tested on trusses that were not included in the
training dataset. Results show that the accuracy of the regression is
satisfactory even in structures with higher number of nodes and members than
those used to train it.

Machine Learning;Systems and Control;Systems and Control