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• A DBSCAN-based machine learning model outperforms other ML models in accurately detecting anomalous events (bridge accidents) in smart bridge monitoring.result 90%
• The proposed AI-driven anomaly detection model is well-suited for smart bridge monitoring and can enhance public safety by enabling timely detection of unforeseen incidents.result 85%
• The model is developed using real-time sensor data collected by iBridge sensor devices installed on a bridge in Norway.method 95%
• The iBridge sensor device is a compact, smart device equipped with a multifunctional sensor-based system that transmits data to a cloud platform via 4G communication.technical 90%
• Traditional bridge monitoring methods that rely heavily on human visual inspections are time-consuming and prone to subjectivity and error.limitation 85%
• DBSCAN is a density-based clustering algorithm that can define anomalies (outliers) as points that are neither core points nor border points.technical 95%
• The proposed system architecture integrates three main stages: data preprocessing, AI-based anomaly detection, and output of detected anomalies.method 80%

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