Maozu Guo

Beijing University of Civil Engineering and Architecture

Papers

Total Citations

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About

Maozu Guo is a leading researcher at the intersection of computational design, structural engineering, and artificial intelligence. His work primarily focuses on developing intelligent algorithms for spatial analysis and structural form-finding, with a particular emphasis on integrating AI with building information modeling (BIM) and tensegrity systems. Guo’s most notable contribution is his pioneering work on indoor pathfinding, where he introduced an accurate and efficient method leveraging BIM data—a breakthrough that addresses the longstanding challenge of generating reliable indoor maps for robotics, automation, and computer graphics. This highly cited paper (69 citations) has become a foundational reference in the field. More recently, Guo has advanced the form-finding of tensegrity structures using graph neural networks, a novel approach that enhances the design of lightweight, high-stiffness structures used in engineering, architecture, and robotics. Though still emerging, this work (7 citations) signals a promising new direction in AI-driven structural optimization. Guo’s research is distinguished by its practical impact, bridging theoretical AI methods with real-world engineering problems, making him a key figure for students and researchers interested in computational design and intelligent infrastructure.