On an Application of Graph Neural Networks in Population-Based SHM

Abstract

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.

Hierarchical Bayesian modeling for knowledge transfer across engineering fleets via multitask learning

“…Multitask neural networks, in particular, show promise when the size (or features) of monitoring data permits their application; for example, Zhang et al (2020) design a deep architecture for guided wave data sets. Similarly, Tsialiamanis et al (2022) successfully investigate neural networks for knowledge transfer by mapping measurements from multiple structures onto a common manifold, to learn a shared representation.…”

“…Similarly, Tsialiamanis et al. (2022) successfully investigate neural networks for knowledge transfer by mapping measurements from multiple structures onto a common manifold, to learn a shared representation.…”