Papers
2
Total Citations
21
H-Index
2
About
Xiangjun Meng is a researcher at the forefront of robotics and structural health monitoring, with a focus on integrating generative artificial intelligence into practical engineering systems. His work spans two key areas: the automated inspection of civil infrastructure and the performance optimization of advanced robotic mechanisms. In a notable 2024 contribution, Meng pioneered the use of generative AI to restore and assess concrete cracks in images degraded by low light, overexposure, or blur—a significant advance for non-destructive evaluation that has already garnered 15 citations. Complementing this, his research on the TBot cable-driven parallel robot (CDPR) provides critical analysis of how modular reconfiguration impacts dynamic performance, laying groundwork for more adaptable high-speed automation in flexible manufacturing. By bridging computer vision with robotics, Meng demonstrates a clear talent for solving real-world challenges where environmental conditions and mechanical constraints intersect. His growing citation record reflects the immediate relevance of his work to both academic researchers and industry practitioners seeking robust, AI-enhanced solutions for inspection and automation.
Research Focus
Key Achievements
Top Papers
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