Advanced Data-Driven Structural Health Monitoring (SHM) Systems: Integrating IoT, Big Data Analytics, And Machine Learning For Real-Time Structural Integrity Assessment And Predictive Maintenance

Structural Health Monitoring (SHM) is crucial for ensuring the safety, reliability, and sustainability of critical infrastructure such as bridges, buildings, and dams. Historically, SHM relied on manual inspections and mechanical sensors, offering limited insights and detecting issues only after significant damage had occurred. The emergence of advanced technologies, including the Internet of Things (IoT), Big Data Analytics, and Machine Learning (ML), has transformed SHM into a proactive, data-driven approach capable of real-time monitoring and predictive maintenance. IoT facilitates continuous data collection through interconnected sensors, capturing parameters such as stress, vibration, and temperature, thereby providing engineers with comprehensive insights into structural conditions. Big Data Analytics processes this vast influx of data, uncovering patterns and anomalies, while ML algorithms refine predictions, learn from historical records, and autonomously detect risks. These integrated systems enable predictive maintenance, minimizing infrastructure downtime, optimizing costs, and preventing unexpected failures. Despite these benefits, challenges such as cybersecurity threats, interoperability issues, and high initial costs are prevalent, requiring targeted solutions like advanced encryption, standardization protocols, and scalable data processing frameworks. This paper examines the role of these technologies in improving SHM systems, highlights their advantages, addresses implementation challenges, and explores future directions for integrating IoT, Big Data, and ML into global infrastructure management. Overall, these advancements are reshaping SHM, ensuring safer, smarter, and more sustainable practices for managing critical assets.

کلیدواژه ها:

Structural Health Monitoring (SHM) ، Internet of Things (IoT) ، Big Data Analytics ، Machine Learning (ML) ، Predictive Maintenance