Towards Risk-Reduced Bridge-Cable Maintenance: Integrating Cable-Crawling Robot, AI, and a Metaverse-Based SHM

Bridges with long cables require regular inspection; however, their height, length, and accessibility constraints make maintenance both risky and complex. To address these challenges, this study proposes a cable-inspection-robot-centered structural health monitoring (SHM) methodology that enhances operational safety and efficiency in hazardous bridge environments. The proposed methodology comprises four key components: (i) a cableclimbing robot that acquires visual data from the cable surface, (ii) an AI-based damage detection module, (iii) a metaverse-based digital twin and extended-reality (XR) viewer, and (iv) a localization system that fuses Real-Time Kinematic (RTK) GPS with Visual-Inertial Odometry (VIO). A preliminary evaluation demonstrates promising results, including improved localization accuracy and reliable defect identification, highlighting the system’s potential for practical application. Future work will focus on integrating all components and validating the complete framework in real-world bridge maintenance scenarios.