Integrated Real-Time Scour Monitoring System Development

This research paper presents the development of an integrated real-time scour monitoring system that utilizes artificial intelligence (AI) powered cameras, flow velocity sensors, and geophysical detection techniques. The objective is to enhance the accuracy and reliability of scour detection, which is critical for maintaining the structural integrity of railway infrastructure. The system continuously analyzes visual data to detect sediment displacement and structural movement, while flow velocity sensors provide essential hydrodynamic data. Additionally, geophysical methods such as ground penetrating radar (GPR) and electrical resistivity tomography (ERT) are employed for subsurface characterization. The integration of these technologies allows for the creation of a digital twin, enabling real-time assessments and early warnings regarding scour risks. Field trials are being conducted on various structures to validate the system's effectiveness under different environmental conditions. The research aims to transition from reactive manual inspections to a proactive monitoring approach, ultimately improving infrastructure resilience and safety.