Laboratory Study of IoT-Based Real-Time Culvert Blockage Detection

Extreme weather events are increasing the risk of flood-related infrastructure failures in areas where culverts play a critical role in water management. Blockages caused by sediment and debris can significantly reduce hydraulic capacity, leading to flooding and structural damage. Existing monitoring solutions such as visual inspection, conventional sensors, and closed-circuit television (CCTV)-based systems are often labor-intensive, costly, energy-demanding, resource-intensive, or insufficiently scalable for widespread application, especially in rural or resource-limited areas. This study presents a novel, low-cost Internet of Things (IoT)-enabled system for real-time culvert blockage detection that overcomes these limitations by eliminating reliance on visual data and complex computing infrastructure. By leveraging low-cost ultrasonic sensors and a geometry-independent decision-rule-based classification algorithm embedded in the microcontroller, the system demonstrated average detection accuracy of 87.0% (steady state) and 84.0% (surge) across 100 steady-state and 75 surge condition experiments in a controlled laboratory environment. The system successfully categorized blockage levels (0%, 25%, 50%, 75%, and 100%) and showed consistent performance under varying flow conditions. With a setup cost of approximately USD 35 per unit, the system offers high scalability, low power requirements, and practical deployable across large culvert networks, positioning it as a transformative tool for proactive culvert management and flood mitigation. Future work will focus on field trials under varied conditions for enhanced reliability, with potential applications in standardizing culvert monitoring practices and supporting proactive maintenance strategies.