Uncertainty-aware Graph Learning Approach for Sparse-Sensing Structural Health Monitoring

Objective

Structural health monitoring (SHM) emerged from the critical need to continuously assess the integrity and performance of aging infrastructure to ensure public safety. Current sparse-sensing schemes leave vast portions of structures unmonitored and vulnerable to undetected damage, which necessitates full-field response reconstruction to gain systematic structural insight. Classic machine learning (ML) approaches struggle with the ‘black-box’ issue and topological blindness in complex connectivity patterns, which limit their prediction accuracy and model applicability. The proposed project, ‘Uncertainty-aware Graph Learning Approach for Sparse-Sensing Structural Health Monitoring’ (U-GLASS), aims to develop a reliable physics-informed graph learning framework while accounting for uncertainty that addresses fundamental sparse-sensing limitations plaguing current SHM. To implement the project, four sub-objectives guided by the framework include: 1) Develop a reduced-order model (ROM) with parametric uncertainties and reduction errors quantification; 2) Construct a GNN embedded with ROM-derived physics and topological knowledge; 3) Develop a training protocol for GNN to reconstruct full-field responses from sparse measurements; and 4) Quantify structural damage severity/location using reconstructed responses on multi-scale systems. The framework will be first implemented on multi-scale simulation/laboratory cases and then applied to real-world systems such as Swiss railway bridges. By harmonizing uncertainty-aware ROM/topological physics with ML, the goal is to enable physics-enhanced full-field response reconstruction from sparse sensor data, transforming limited sensing data into actionable engineering insights. The magnitude of U-GLASS’s contribution is ultimately measured not just in technical innovation, but in its potential to fundamentally reshape how society manages critical infrastructure systems for future generations.