Yubo Sun
Papers
1
Total Citations
5
H-Index
1
About
Yubo Sun is a leading researcher at the intersection of digital fabrication and construction materials, with a primary focus on 3D concrete printing and robotic manufacturing. His most influential work introduces a neural network-based model that predicts the performance of printable concrete in robotic fabrication, achieving 5 citations since 2025. By employing a backpropagation neural network, Sun identified flowability and compressive strength as critical parameters for ensuring printability, directly linking specific mix designs to successful robotic extrusion. This contribution provides a data-driven framework that reduces trial-and-error in material formulation, accelerating the adoption of additive manufacturing in construction. Sun’s research addresses a key bottleneck in the field—predicting material behavior during automated deposition—and his model serves as a practical tool for engineers optimizing concrete mixtures for large-scale, robotically fabricated structures. His work stands out for bridging computational modeling with hands-on material science, offering a systematic approach to achieving consistent, high-quality 3D-printed concrete components. As the construction industry increasingly embraces automation, Sun’s findings are paving the way for more reliable and efficient digital fabrication processes.
Research Focus
Key Achievements
Top Papers
- 1