Beam layout design of shear wall structures based on graph neural networks

Beam placement in shear wall systems is crucial in transferring vertical loads from floors to shear walls, ensuring structural integrity and optimal performance. Existing solutions using deep generative algorithms rely on pixel images and involve many model parameters, resulting in high computational costs. To address this issue, this paper presents a method based on graph neural networks (GNNs) with robust topological feature extraction capabilities. The method generates potential beam layout scenarios by incorporating architectural layouts, devising scheme design inputs and leveraging engineering experience. Adopting the proposed approach reduces the number of beam layout scenarios and significantly improves computation efficiency. The efficacy is demonstrated through various test cases, suggesting that the beam layouts designed by the proposed method closely resemble those by engineers.