  The Population-Based Structural Health Monitoring (PBSHM) paradigm has
recently emerged as a promising approach to enhance data-driven assessment of
engineering structures by facilitating transfer learning between structures
with some degree of similarity. In this work, we apply this concept to the
automated modal identification of structural systems. We introduce a Graph
Neural Network (GNN)-based deep learning scheme to identify modal properties,
including natural frequencies, damping ratios, and mode shapes of engineering
structures based on the Power Spectral Density (PSD) of spatially-sparse
vibration measurements. Systematic numerical experiments are conducted to
evaluate the proposed model, employing two distinct truss populations that
possess similar topological characteristics but varying geometric (size, shape)
and material (stiffness) properties. The results demonstrate that, once
trained, the proposed GNN-based model can identify modal properties of unseen
structures within the same structural population with good efficiency and
acceptable accuracy, even in the presence of measurement noise and sparse
measurement locations. The GNN-based model exhibits advantages over the classic
Frequency Domain Decomposition (FDD) method in terms of identification speed,
as well as against an alternate Multi-Layer Perceptron (MLP) architecture in
terms of identification accuracy, rendering this a promising tool for PBSHM
purposes.
