AI ENABLED A STRUCTURAL HEALTH MONITORING USING LOW POWER COST IOT SENSOR

Abstract

Structural safety is a critical concern for infrastructure such as bridges, buildings, dams, and industrial facilities, where undetected deterioration can lead to catastrophic failures. Traditional inspection methods are time-consuming, labor-intensive, and prone to human error, creating a need for intelligent and continuous monitoring solutions. This research proposes an AI-enabled Structural Health Monitoring (SHM) system based on low-power, cost-efficient IoT sensors capable of real-time measurement and predictive analytics. The system integrates vibration, strain, temperature, and displacement sensors with an edge-processing IoT architecture, transmitting data securely to a cloud-based platform. Machine learning algorithms analyse sensor readings to identify early abnormalities, quantify damage severity, and predict future structural failures. Energy-efficient communication protocols and optimized data compression techniques extend the battery life of sensor nodes, making the system scalable and economically feasible for long-term deployment. The experimental results demonstrate that the proposed model enhances damage detection accuracy, reduces maintenance costs, and supports proactive decisionmaking for infrastructure management. Overall, the AI-enabled low-power SHM framework establishes a robust and intelligent solution for smart infrastructure resilience and public safety.