Shih-Yuan Wang
Papers
5
Total Citations
34
H-Index
3
About
Shih-Yuan Wang is a rising researcher at the intersection of construction robotics, digital fabrication, and artificial intelligence. His work focuses on solving fundamental challenges in automated construction—specifically, how to precisely predict, localize, and fabricate complex structures in dynamic, real-world environments. Wang’s most impactful contribution is a deep learning framework using LSTM networks to predict the trajectory of crane-lifted loads, achieving 13 citations by bridging the gap between simulated and real-world scenarios. He also pioneered a cost-effective method for fabricating large, irregularly shaped concrete architectural elements (12 citations), addressing a critical bottleneck in freeform construction. In a notably innovative 2023 study, Wang combined HoloLens augmented reality with displacement sensors to achieve real-time, high-fidelity 3D surface reconstruction for printing on freeform surfaces—a breakthrough that directly confronts the material-induced errors inherent in additive manufacturing. His work on AGV indoor localization using drawstring displacement sensors (2024) further demonstrates his commitment to practical, sensor-driven automation. Though early in his career, Wang’s research agenda—merging neural network prediction, sensor fusion, and adaptive fabrication—positions him as a key voice in the future of intelligent construction.
Research Focus
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Top Papers
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