AI-Enhanced Wireless Sensor Network for Proactive Structural Health Monitoring in Large-Scale Infrastructure

This framework helps you build an AI‑enhanced wireless sensor network (WSN) for proactive structural health monitoring (SHM) of large civil structures. Typical monitoring systems use reactive maintenance or slow manual inspections, so they often miss tiny cracks or other problems as they happen. The proposed design uses a distributed network of low‑power microcontrollers, triaxial accelerometers, and strain gauges. These sensors continuously record vibration and deformation data from the structure. Each node runs edge‑computing to clean the data and remove noise before sending it over a low‑power wide‑area network (LPWAN) to a central gateway. At the monitoring station, lightweight machine‑learning models—such as support vector machines or autoencoders—process the time‑series data. They

flag structural anomalies and predict where fatigue might develop. The architecture gives engineering students a hands‑on template for learning sensor calibration, wireless telemetry, and predictive analytics. By combining hardware design with smart data processing, the project guides you toward a robust, scalable, and energy‑efficient solution for proactive infrastructure safety.

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