Reinforced concrete (RC) slabs are fundamental structural components in buildings and infrastructure, providing load-bearing capacity and overall stability. Optimizing rebar design for RC slabs is crucial for balancing structural performance, material efficiency, and constructability. Traditional design methods are often oversimplified and overlook the complex spatial distribution of rebar, leading to material waste. Existing optimization methods usually focus on minimizing material consumption, resulting in challenges in practical implements. To address these limitations, this paper proposes a hybrid optimization framework that integrates (GNNs) and metaheuristic algorithms (MAs) under design for manufacture and assembly principles. In the proposed approach, the MA module first generates optimal rebar layouts for multiple slab cases, which are used to construct and train the GNN model. The trained GNN then predicts an initial rebar configuration based on new slab geometries, and the MA subsequently performs localized refinement and code compliance checking. This hybrid approach enhances computational efficiency (40%–70% time saving) and design quality by systematically optimizing rebar amount and distribution. The proposed method is validated through a series of case studies, demonstrating significant improvements in cost-effectiveness, constructability, and computational efficiency compared with conventional approaches.
Li et al. (2026) studied this question.