WaveGraphNet: Physics-Consistent Guided-Wave Damage Localization through Coupled Inverse-Forward Graph Learning

2026-05-19

AI Summary

This work addresses the challenge of generalizing data-driven damage localization methods to unseen locations under sparse piezoelectric sensor networks, where limited training coverage hinders performance. The authors propose a graph learning framework that jointly performs inverse localization and forward wave response prediction. By modeling sensor layouts with graph neural networks and incorporating a physics-consistency regularization term, the method ensures that predicted damage locations adhere to guided wave propagation principles. Integrating spectral features with energy deviation prediction, the approach achieves high-precision damage localization on carbon fiber reinforced polymer plates. It significantly outperforms both non-graph and existing graph-based baselines, demonstrating notably enhanced robustness in extrapolation scenarios beyond the training domain.

Abstract

Guided-wave structural health monitoring enables damage localization in composite plates using sparse networks of bonded piezoelectric transducers. However, inferring the spatial location of defects from pitch-catch measurements remains weakly constrained when only a limited set of damage locations is available for training. As a result, models trained to predict defect locations may perform well on seen cases but generalize poorly to unseen regions of the structure. This paper proposes WaveGraphNet, a coupled inverse--forward graph learning framework for guided-wave damage localization in Carbon Fiber Reinforced Polymer (CFRP) plates. The sensing layout is explicitly modeled as a graph, where transducers are represented as nodes and measured propagation paths define the graph connectivity. An inverse branch maps graph-structured spectral descriptors of differential guided-wave responses to a damage location, while a forward branch predicts the path-wise energy-deviation patterns of measured wave responses associated with a candidate location. During training, the forward branch serves as a physics-consistent regularizer, discouraging location estimates that are numerically plausible but inconsistent with the measured redistribution of wave-response energy. This coupling encourages agreement between inferred damage coordinates and the underlying wave propagation behavior. Within this benchmark, the proposed graph-based formulation provides a strong localization model for sparse guided-wave sensing and demonstrates improved robustness in extrapolation to held-out regions compared to both non-graph and graph baselines. These results highlight the potential of coupled inverse-forward graph learning as an effective strategy for guided-wave localization under limited spatial coverage.

Problem

• guided-wave
• damage localization
• structural health monitoring
• generalization
• sparse sensing

Innovation

• graph neural networks
• guided-wave SHM
• physics-consistent learning
• inverse-forward coupling
• damage localization