Physics‐guided graph neural network for cable deployment optimization in frame structures

Deploying cables into the frame structure is an effective method to enhance its structural stiffness. The efficacy of cables is highly dependent on their placement, posing the core challenge of accurately identifying the optimal deployment positions from a vast array of feasible options. However, there exists a significant research gap in the field of structural optimization concerning cable arrangement. In current engineering practice, cable layout primarily relies on experience-based methods grounded in mechanical concepts (such as regions of large deformation), making it difficult to identify a globally optimal solution. To address this, an automatically accurate identification method is proposed to find the optimal deployment of high-performance cables within an exponentially large solution space. Leveraging the graph neural networks (GNNs) architecture, an intelligent generative cable optimal deployment (IGCOD) model is presented, which embeds a finite element physical model. This model utilizes the GNNs as a topology generation and discrimination engine, constructing an end-to-end closed-loop framework through the following steps: topology feature extraction, automated cable generation, and optimal scheme identification. By directly embedding the mechanical response of the physical model into the network prediction, a fully automated design is achieved without labeling the pre-training data. In various topological configurations of frame structures, the IGCOD model accurately identified the optimal cable placement within tens of thousands of feasible solutions, thereby maximizing structural stiffness performance. In the cases of irregular multi-story and high-rise frame structures, the maximum optimization effect of three pairs of cables increased by 40% and 21%, respectively, and the corresponding time cost is 717 and 6384 s. This research presents a systematic and transferable artificial intelligence (AI)-driven paradigm for the high-performance reinforcement of existing buildings, thereby reducing design costs and maximizing structural performance.