arXiv Machine Learning October 5, 2026 By Rahul Jaiswal, Joakim Hellum, Halvor Heiberg

Smart Sensing for Safer Bridges: From Sensor Signals to AI-Driven Anomaly Detection

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The paper presents two complementary methods for detecting anomalies in real‑world bridge sensor data: a signal‑processing approach and a data‑driven Isolation Forest machine‑learning model. Real‑time data were collected from an iBridge sensor on a Norwegian bridge and evaluated through metrics such as anomaly counts, detection time, processing rate, and visualization. A controlled anomaly‑injection study showed distinct detection characteristics and computational demands for each method, underscoring the potential of combining machine learning with signal processing for bridge monitoring.

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