This paper presents Text2Structure3D, a graph-based Machine Learning (ML) model that generates equilibrium structures from natural language prompts. Text2Structure3D is designed to support new intuitive ways of design exploration and iteration in the conceptual structural design process. The approach combines latent diffusion with a Variational Graph Auto-Encoder (VGAE) and graph transformers to generate structural graphs that are close to an equilibrium state. Text2Structure3D integrates a residual force optimization post-processing step that ensures generated structures fully satisfy static equilibrium. The model was trained and validated using a cross-typological dataset of funicular form-found and statically determinate bridge structures, paired with text descriptions that capture the formal and structural features of each bridge. Results demonstrate that Text2Structure3D generates equilibrium structures with strong adherence to text-based specifications and greatly improves generalization capabilities compared to parametric model-based approaches. Text2Structure3D represents an early step toward a general-purpose foundation model for structural design, enabling the integration of generative AI into conceptual design workflows.

翻译：本文提出Text2Structure3D，一种基于图的机器学习模型，能够根据自然语言提示生成平衡结构。Text2Structure3D旨在为概念结构设计过程提供新颖直观的设计探索与迭代方式。该方法将潜在扩散与变分图自编码器及图Transformer相结合，以生成接近平衡状态的结构图。Text2Structure3D集成了残差力优化后处理步骤，确保生成的结构完全满足静力平衡。模型使用跨类型的悬链线形找形与静定桥梁结构数据集进行训练与验证，该数据集包含描述每座桥梁形态与结构特征的文本说明。结果表明，Text2Structure3D生成的平衡结构能高度遵循文本规范，且相比基于参数化模型的方法显著提升了泛化能力。Text2Structure3D代表了迈向通用结构设计基础模型的初步探索，为生成式人工智能融入概念设计流程提供了可能。