Detecting the Unexpected: AI-Driven Anomaly Detection in Smart Bridge Monitoring

TL;DR

This paper presents an AI-driven anomaly detection method using sensor data and machine learning to improve smart bridge monitoring and enhance safety by detecting unforeseen incidents.

Contribution

It introduces a simple ML model based on DBSCAN that outperforms other models in detecting anomalies in real-time bridge sensor data.

Findings

DBSCAN-based model accurately detects anomalous events

The model outperforms other ML models in experiments

Enhances safety by enabling timely incident detection

Abstract

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.