Dongmin Lee
Papers
2
Total Citations
147
H-Index
2
About
Dongmin Lee is a leading researcher at the intersection of construction automation, robotics, and artificial intelligence. His work focuses on developing intelligent systems that can adaptively manage complex construction tasks, with a particular emphasis on digital twin technology and reinforcement learning. Lee's most impactful contribution is his pioneering work on digital twin-driven deep reinforcement learning for adaptive task allocation in robotic construction, which has garnered 127 citations and represents a significant advance in autonomous construction systems. He has also made important contributions to understanding the research landscape of construction automation through keyword network analysis, mapping the evolution and interconnections within this rapidly changing field. Lee's research addresses critical challenges in integrating AI with physical construction processes, enabling more efficient and flexible robotic systems that can respond to dynamic site conditions. His work bridges the gap between theoretical AI methods and practical construction applications, positioning him as a key innovator in the ongoing transformation of the construction industry through automation and intelligent robotics.
Research Focus
Key Achievements
Top Papers
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- 2