Feng-Wei Kuo
Papers
1
Total Citations
13
H-Index
1
About
Feng-Wei Kuo is an emerging researcher in construction robotics and intelligent systems, with a primary focus on enhancing safety and efficiency in heavy-lifting operations. His work centers on the application of deep learning, particularly Long Short-Term Memory (LSTM) networks, to predict the trajectory of crane-lifted loads—a critical challenge in construction site safety. In his most-cited paper, "Predicting trajectory of crane-lifted load using LSTM network: A comparative study of simulated and real-world scenarios" (2023, 13 citations), Kuo bridges the gap between theoretical models and practical deployment, demonstrating that LSTM-based predictions can effectively generalize from simulated data to real-world conditions. This contribution is notable for its potential to reduce human error and prevent accidents in dynamic construction environments. Though early in his career, Kuo’s work has already garnered attention for its practical relevance, and his comparative methodology offers a replicable framework for future studies. His research sits at the intersection of civil engineering, artificial intelligence, and human-robot interaction, promising to advance autonomous monitoring systems in the built environment.
Research Focus
Key Achievements
Top Papers
- 1