چکیده مقاله
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
کلیدواژهها
نویسندگان
Rasoul Ghafari
PhD student in civil engineering majoring in engineering and construction management at the Islamic Azad University of Arak branch, Arak, Iran
Donya Taleshi
PhD student in Geotechnics, Qazvin Branch, Islamic Azad University, Qazvin, Iran
Seyed Reza Samaei
Assistant professor, Faculty of Technical and Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
Mojdeh Arabshahi
M D , Department of Architectural Engineering, Nour Branch, Islamic Azad University, Nour, Iran
Hossein Mahjoub
Bachelor of Science in Civil Engineering, Chalus Branch, Islamic Azad University, Chalus, Iran
Faraz Amin Anaraki
Bachelor Student of Science in Civil Engineering, Tonekabon Branch, Islamic Azad University, Tonekabon, Iran
شیوه ارجاع
Taleshi, Donya and Arabshahi, Mojdeh and Mahjoub, Hossein and Amin Anaraki, Faraz,1404,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,18th International Conference on Mechanical, Construction Industrial & Civil Engineering
ارائهشده در
مجموعه مقالات هجدهمین کنفرانس بین المللی مکانیک، ساخت، صنایع و مهندسی عمران31 اردیبهشت 1404