Abstract
In architectural and engineering design, minimizing weight, deflection, and strain energy requires navigating complex, non-linear interactions among competing objectives, making the optimization of concrete thin shell constructions particularly challenging. Traditional multi-objective optimization (MOO) methods frequently encounter difficulties in effectively exploring design spaces, which often necessitate substantial computational resources and result in suboptimal solutions. This paper presents a novel approach for enhancing topology and thickness optimization. Graph-constrained conditional Generative Adversarial Networks (GANs) and the Non-Dominated Sorting Genetic Algorithm II (NSGA-II) are used in the study. The hybrid approach addresses fundamental limitations in current optimization techniques by combining the generative capabilities of deep learning with the refinement processes of evolutionary algorithms. NSGA-II enhances the algorithm by employing evolutionary processes to generate various structural designs that adhere to topological constraints. Specialized graph-constrained GANs accomplish this. The implementation of the system in a concrete thin shell structure at the Shenzhen Qianhai Smart Community resulted in significant performance improvements: a 33.3% reduction in total weight, a 50% decrease in maximum deflection, and a 20% reduction in strain energy compared to baseline models. A comparative comparison of traditional NSGA-II techniques shows substantial benefits, including a 50% enhancement in convergence speed and notable advancements in solution diversity and quality. We confirmed structural integrity through extensive finite element analysis and practical prototyping, achieving performance variations under 3.5%. This work illustrates the potential of sophisticated machine learning and evolutionary algorithms to produce innovative, high-performance architectural solutions, thereby providing a new methodology for structural optimization.