Artificial Intelligence Applications in Structural Health Monitoring: Comparing Japan and South Korea
DOI:
https://doi.org/10.54518/jaei.3.1.2025.1329Keywords:
Artificial Intelligence, Japan, Machine Learning, South Korea, Structural Health MonitoringAbstract
Artificial Intelligence (AI) has significantly transformed Structural Health Monitoring (SHM) by enabling intelligent damage detection, condition assessment, predictive maintenance, and real-time infrastructure monitoring. Recent advancements in machine learning, deep learning, computer vision, and sensor technologies have accelerated the adoption of AI-based SHM systems, particularly in Japan and South Korea, two countries recognized for their technological innovation and resilient infrastructure development. This study aims to systematically review research published over the last five years to compare AI applications in Structural Health Monitoring in both countries. Using a Systematic Literature Review (SLR) approach, the study synthesizes recent developments, implementation strategies, application areas, benefits, challenges, and future research directions. The findings reveal that Japan emphasizes AI for disaster-resilient infrastructure and seismic monitoring, whereas South Korea focuses on smart infrastructure management and automated inspection systems. Despite significant progress, both countries continue to face challenges related to data integration, model reliability, implementation costs, and skilled workforce availability. This review provides evidence-based insights to support future AI-driven SHM research and sustainable infrastructure management.
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