用AI分析传感器数据，实时发现桥梁异常事件
Detecting the Unexpected: AI-Driven Anomaly Detection in Smart Bridge Monitoring
- 基于实时传感器数据，用DBSCAN算法识别异常
- 该模型在检测桥梁事故上准确率优于其他机器学习方法
- 适合智能交通与基础设施安全监测领域使用
桥梁是国家基础设施和智慧城市的关键组成部分，智能桥梁监测对于保障公共安全、防止灾难性故障或事故至关重要。传统监测方法依赖人工视觉检查，耗时且易受主观性和错误影响。本文提出一种基于人工智能（AI）的智能桥梁监测异常检测方法。具体而言，利用安装在挪威某桥梁上的iBridge传感器设备采集的实时数据，开发了一个简单的机器学习（ML）模型。该模型与多种其他机器学习模型进行了对比评估。实验结果表明，基于密度聚类的应用噪声（DBSCAN）的模型在准确检测异常事件（桥梁事故）方面表现更优。研究结果表明，该模型适用于智能桥梁监测，可通过对意外事件的及时检测提升公共安全。
原文摘要 · Abstract (English)
Bridges are critical components of national infrastructure and smart cities. Therefore, smart bridge monitoring is essential for ensuring public safety and preventing catastrophic failures or accidents. Traditional bridge monitoring methods rely heavily on human visual inspections, which are time-consuming and prone to subjectivity and error. This paper proposes an artificial intelligence (AI)-driven anomaly detection approach for smart bridge monitoring. Specifically, a simple machine learning (ML) model is developed using real-time sensor data collected by the iBridge sensor devices installed on a bridge in Norway. The proposed model is evaluated against different ML models. Experimental results demonstrate that the density-based spatial clustering of applications with noise (DBSCAN)-based model outperforms in accurately detecting the anomalous events (bridge accident). These findings indicate that the proposed model is well-suited for smart bridge monitoring and can enhance public safety by enabling the timely detection of unforeseen incidents.
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