Bridge monitoring improves with AI and signal processing detection
Smart Sensing for Safer Bridges: From Sensor Signals to AI-Driven Anomaly Detection
Machine Learning
Summary
Bridges need to be carefully watched to make sure they are safe. This paper looks at ways to spot unusual signals from sensors on a real bridge in Norway. The researchers tested two methods: traditional signal processing and a machine learning model called Isolation Forest. They compared how well each method finds problems quickly and accurately, showing the benefits of using AI alongside classic techniques to catch early signs of trouble.
What this means in practice
- •For bridge maintenance teams: Detect unusual sensor data patterns to identify potential structural problems early and improve inspection scheduling.
- •For infrastructure monitoring service providers: Combine AI-driven anomaly detection with signal processing to offer advanced real-time bridge health monitoring solutions.$Commercial implications: Enables development of AI-based monitoring products enhancing bridge safety for infrastructure clients.
Tested on one dataset.
Authors
Rahul Jaiswal, Joakim Hellum, Halvor Heiberg
Abstract
Bridges contribute significantly to transportation connectivity and urban development. Therefore, reliable bridge monitoring is crucial for protecting public safety and detecting anomalous behavior in bridge sensor data that may provide early indications of abnormal structural conditions. This paper investigates anomaly detection in real-world bridge sensor data using two different complementary approaches, namely signal processing and the data-driven machine learning model Isolation Forest. The real-time bridge sensor data is collected from an iBridge sensor device installed on a bridge in Norway. The methods are evaluated using anomaly counts, anomaly detection time, processing rate, anomaly rates, visualization, and temporal agreement. Moreover, a controlled anomaly-injection analysis is performed to evaluate the sensitivity of each method. Numerical results demonstrate distinct detection characteristics and computational requirements, highlighting the potential of machine learning, particularly the data-driven Isolation Forest, alongside signal processing for identifying anomalies in bridge sensor measurements.