🤖 AI Summary
This study addresses the challenge of reliably distinguishing genuine structural anomalies from benign disturbances in infrastructure health monitoring under varying environmental and operational conditions. To this end, the authors propose a physics-informed digital twin framework that performs real-time anomaly inference by quantifying inconsistencies between a healthy-structure twin model and actual sensor responses. The approach integrates an intrinsically compressed dynamic representation, residual-enhanced spectral features, and a persistence-based decision mechanism. Notably, it is the first method to unify the detection of diverse damage mechanisms—including abrupt changes, gradual degradation, nonlinear behavior, and damping loss—within a single framework. Evaluated across seven numerical benchmarks, the method achieves zero sustained false alarms in undamaged states and limited detection latency under damage scenarios, substantially enhancing monitoring robustness, reliability, and generalization capability.
📝 Abstract
Structural health monitoring is moving from damage detection alone towards real-time decision support for ageing and safety-critical infrastructure. This shift requires monitoring methods that can separate true structural change from benign environmental and operational variability, while remaining interpretable to engineers. This paper presents SPECTRA, a physics-informed eigen-compressed digital twin framework for real-time structural anomaly inference. The framework combines a healthy structural twin, eigen-compressed dynamic representation, full-order twin innovation, residual-augmented spectral features, kernel principal component analysis, and a persistent decision rule. The central idea is that structural anomalies are inferred not from statistical features alone, but from disagreement between the measured response and a physics-informed healthy twin. The method is assessed through seven numerical benchmarks: smooth Duffing-type nonlinear drift, sudden stiffness loss, gradual stiffness degradation, bilinear breathing stiffness, environmental and operational variability-confounded local damage, local damping loss, and an operational-only negative-control case. The results show that SPECTRA detects abrupt, gradual, nonlinear and damping-related damage mechanisms, while avoiding persistent false alarms under operational variability alone. Across the accepted benchmark suite, the persistent decision rule gives zero pre-damage persistent false alarms, finite detection delay in damage cases, and zero persistent alarms in the no-damage negative-control case. The framework provides a reproducible route for testing physics-informed anomaly inference before deployment in infrastructure digital twins.