Year: 2026
Type: article
Abstract: Ageing infrastructure requires continuous monitoring to identify early signs of structural damage and prevent potential failures. Conventional inspection methods rely primarily on manual observation, making them time-consuming, costly, and difficult to perform frequently. This paper presents an Artificial Intelligence and Internet of Things (AI-IoT)-based structural health monitoring system that integrates computer vision and vibration sensing for automated crack detection and abnormal vibration identification. A camera captures images of the monitored structure, and a lightweight MobileNetV2-based deep learning model classifies surface conditions as crack or no-crack. Simultaneously, an L3G4200D gyroscope interfaced with a Raspberry Pi provides angular velocity readings, which are analysed using a calibrated threshold-based method to identify deviations from baseline vibration conditions. A rule-based fusion engine combines the visual and sensor outputs to classify the overall structural condition into three severity levels: LOW, MEDIUM, and HIGH. An RGB LED provides local status indication, while a secure web dashboard enables remote monitoring of structural conditions. When a HIGH-severity condition is detected, the system generates an automated email alert to facilitate timely inspection and corrective action. The proposed system demonstrates the integration of AI-based vision, IoT sensing, severity assessment, and automated alerts for structural condition monitoring. Further quantitative evaluation and real-world validation are required to establish its reliability and practical applicability
Institution: Bapuji Institute of Engineering and Technology
Language: English
Subfield: Civil and Structural Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W7223661757