Topology and size optimization of trusses using graph neural networks: Towards efficient surrogate modeling

Abstract

Real-time structural optimization of trusses using machine learning techniques, incorporating both topology and size optimization, is more effective in discrete domains than in continuous ones, with Graph Neural Networks (GNNs) showing strong potential. However, the impact of convolutions in GNNs is not yet fully understood, limiting their full applicability. This paper presents a GNN-based surrogate model for real-time structural optimization and identifies the most suitable convolution type for this task. The study assesses the predictive performance of models trained on a dataset of optimized structures spanning a range of loads, boundary conditions, and design domain sizes. The resulting model effectively predicts both optimal topology and member sizes once the design parameters are provided. Eight graph convolution types are investigated to identify the most suitable method, alongside an evaluation of optimal network architectures. Among the tested approaches, Generalised Graph Convolution achieves the highest accuracy, followed by Topology Adaptive Graph Convolution, producing near-ideal topology predictions for most test cases and maintaining section size prediction errors within ±2% across all test data points. This framework demonstrates strong potential for broader applications.