Self-Supervised Contrastive Learning for Online Damage Detection in Bridge Acceleration Signals

Abstract

Structural Health Monitoring (SHM) increasingly relies on data-driven methods applied to dense vibration measurements, however, reliable damage detection from acceleration time series remains challenging due to environmental variability, limited labeled damage data, and severe class imbalance. This work investigates self-supervised learning for anomaly detection in bridge acceleration signals. First, we adopt a contrastive framework that captures temporal and contextual dependencies to pretrain an encoder exclusively on fixed-length windows of data collected under healthy conditions. To account for real-world variability, physics-informed data augmentations are introduced to simulate measurement disturbances such as sensor noise and signal variations. Following pretraining, anomalies are identified by measuring deviations of streamed windows from the learned representation of healthy structural behavior, enabling damage detection without explicit labels or predefined damage categories. Experiments on the RT345 multi-scenario bridge benchmark show that the learned representations achieve superior separation between healthy and damaged windows compared to classical baselines and reconstruction-based deep autoencoders. Finally, we evaluate an online post-processing strategy that aggregates consecutive anomaly scores to emulate streaming deployment. While this aggregation improves detection sensitivity (achieving a True Positive Rate above 96%), it can increase false alarms when using a fixed operating point. These findings indicate that the proposed contrastive-learning-based approach enables effective damage detection from vibration signals in an online setting.