Gated Multi-GPT Fusion for Unsupervised Structural Health Monitoring of Bridge Structures

Abstract

Smart infrastructure systems require monitoring frameworks that can function autonomously, remain effective under varying operational conditions, and issue dependable warnings without requiring labeled damage information. This study presents an unsupervised structural health monitoring (SHM) framework for transportation infrastructure that integrates LLM-inspired representation learning with a gated multi-GPT fusion autoencoder for robust intact-only anomaly detection using networked strain measurements. In the proposed pipeline, raw multi-channel strain-gauge signals are first processed through an adversarial autoencoder (AAE) to extract compact and informative feature representations, which are then fed into the downstream model. Multiple GPT-based reconstruction experts analyze these segmented AAE-derived features in parallel, while a compact gating network adaptively combines their outputs. The proposed workflow supports calibration using only intact-condition data, adaptive threshold setting, and continuous monitoring under non-stationary loading scenarios. The framework is validated on a laboratory pedestrian bridge instrumented with multichannel strain gauges and exposed to walking-induced excitation, where it achieves high anomaly detection performance across two damage levels. Robustness studies under both white and colored noise, together with threshold sensitivity analysis, confirm stable class separability and reliable operational performance. Comparative evaluation against leading unsupervised anomaly detection methods further highlights improved robustness to correlated noise and a reduced number of false alarms, demonstrating a scalable sensing-to-decision solution for resilient SHM.