Design-condition-informed shear wall layout design based on graph neural networks

The construction industry, traditionally labor-intensive, has now been evolving towards automation and the incorporation of intelligence. Notably, the shear wall layout has been a critical component in structural construction, where neural networks have promoted the emergence of sophisticated design methods. These methods integrate graph neural networks (GNNs), successfully mitigating the computational resource demands and challenges in capturing the topological features, which are the impediments in pixel image-based methods. However, the existing GNN-based methods marginally accommodate structural design conditions and underperform in fulfilling practical engineering requirements. Specifically, these methods overlook influential factors such as the peak ground acceleration (PGA) of the design basis earthquake (DBE), characteristic ground period, and building height, all of which are crucial to the shear wall layout design. To address this research gap, this study proposes an innovative GNN-based design method that duly incorporates design conditions-including the PGA of the DBE, characteristic ground period, and building height-and rigorously evaluates its advantages over previous approaches. The findings confirm the efficiency and reliability of the proposed design-conditioninformed method and highlight its capability to accurately correlate shear wall layouts with design conditions.