Recent Advances in Structural Health Monitoring Technologies for Sustainable Civil Infrastructure Management
DOI:
https://doi.org/10.54518/jaei.1.2.2023.1235Keywords:
Artificial Intelligence, Internet of Things, Machine Learning, Structural Health Monitoring, Systematic Literature ReviewAbstract
The rapid development of modern civil infrastructure requires more accurate, continuous, and reliable structural condition monitoring systems capable of detecting damage at an early stage. Structural Health Monitoring (SHM) has emerged as a key approach for assessing structural integrity by integrating advanced sensor technologies, the Internet of Things (IoT), Wireless Sensor Networks (WSN), Artificial Intelligence (AI), Machine Learning, and Non-Destructive Testing (NDT) techniques. This study aims to review recent advances in Structural Health Monitoring technologies, identify enabling technologies, examine their applications in civil infrastructure, discuss implementation challenges, and explore future research directions. A Systematic Literature Review (SLR) was conducted by analyzing scientific publications published in 2023 and indexed in Google Scholar. The findings indicate that recent advances in smart sensing technologies, optimization algorithms, cloud computing, and AI-based data analytics have significantly improved the capability of SHM systems for real-time damage detection, structural diagnosis, and predictive maintenance. Nevertheless, several challenges remain, including data quality, system interoperability, cybersecurity, computational requirements, and implementation costs. Future SHM development is expected to focus on integrating intelligent digital technologies to support safer, more efficient, and sustainable infrastructure management.
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