Yu-Ting Sheng
Papers
5
Total Citations
34
H-Index
3
About
Yu-Ting Sheng is a rising researcher at the intersection of construction robotics, digital fabrication, and artificial intelligence. Her work focuses on solving critical challenges in automated construction, particularly for large-scale and irregularly shaped structures. Sheng’s most impactful contribution is the development of a Long Short-Term Memory (LSTM) network to predict the trajectory of crane-lifted loads, a study that bridges simulated and real-world scenarios and has already garnered 13 citations. She also pioneered a cost-effective method for fabricating large irregular architectural forms using concrete, earning 12 citations for its practical implications. In the realm of 3D printing, Sheng has advanced real-time surface reconstruction by integrating HoloLens with displacement sensors, enabling precise printing on freeform surfaces—a technique that addresses material-induced errors. Her work on AGV indoor localization using drawstring displacement sensors further showcases her versatility in sensor fusion and mapping. Additionally, Sheng has developed an artificial neural network model to predict the physical 3D appearance of large-scale objects. With a growing citation count and a focus on real-world applicability, Sheng is establishing herself as a key innovator in smart construction and digital manufacturing.
Research Focus
Key Achievements
Top Papers
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