Presentation Information
[450201-01-01]Generative-AI Design and Intelligent Optimization of Building Structures
Prof. Xinzheng Lu (Tsinghua University)
Traditional building structure design methods are inefficient and heavily dependent on engineers' expertise. Emerging generative AI technologies, while promising, require enhancements in the safety and cost-effectiveness of their designs. This study introduces an integrated generative AI and intelligent optimization framework for building structure design. Leveraging generative AI, the intelligent generation algorithm generates feasible design schemes by learning from existing drawings and considering constraints of architectural layout, design conditions, mechanical principles, and empirical rules. Accelerated by data-driven and domain knowledge-enhanced computational models and evaluation functions, the intelligent optimization algorithm refines AI-generated designs swiftly. The proposed method, integrating intelligent generation and optimization, ensures design safety and cost-efficiency, and mitigates the challenges of scarce and poor-quality training data faced by AIs. Case studies demonstrate that the structural designs produced by the proposed method are on par with those of human experts, meeting design criteria and enhancing efficiency. The proposed intelligent design platform has been adopted by over a hundred design and research institutes, impacting thousands of engineering projects.