arXiv：cs.LG· Rahul Jaiswal, Joakim Hellum, Halvor Heiberg·· 3 小时前AI 评分26
桥梁安全智能感知：从传感器信号到 AI 驱动的异常检测
Smart Sensing for Safer Bridges: From Sensor Signals to AI-Driven Anomaly Detection
AI 导读
研究基于挪威一座桥梁上 iBridge 传感器设备的实时数据，用信号处理与 Isolation Forest 两种方法检测桥梁传感器异常。评估指标包括异常计数、检测时间、处理速率、异常率、可视化与时间一致性，并通过受控异常注入分析检验各方法灵敏度。结果显示两种方法检测特性与算力需求各异，Isolation Forest 在识别桥梁传感器测量异常上具有潜力。
正文
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.
| Comments: | 6 pages, 14 Figures, 4 Tables |
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2610.03082 [cs.LG] |
| (or arXiv:2610.03082v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2610.03082 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Rahul Kumar Jaiswal [view email]
[v1]
Fri, 2 Oct 2026 10:02:25 UTC (6,343 KB)
来源：arXiv：cs.LG · arxiv.org