Large language models (LLMs) have emerged as powerful foundation models with strong reasoning capabilities across domains. Beyond reactive text generation, agentic LLMs enable autonomous workflow execution through modular task decomposition and coordinated tool use. In structural engineering, recent efforts have developed agentic LLMs for automated analysis of plane frames. However, their extension to 3D frames remains underexplored due to challenges in irregular geometric representation, topological consistency, and long-horizon reasoning. This paper proposes an agentic LLM framework for automated structural analysis of 3D frames from natural language inputs. Irregular 3D frames are represented by projection onto a 2D plan, where orthogonal gridlines define spatial coordinates and a matrix of number of stories encodes vertical extrusion of each grid cell. Building on this representation, the framework establishes a multi-agent pipeline: a problem analysis agent parses input into structured JSON; a floor decomposition agent derives the spatial layout of each floor; the 3D geometry is assembled by node, girder, slab, and column agents; support and load agents assign boundary and loading conditions, and code translation agents generate executable SAP2000 script. Evaluated on ten representative 3D frames, the proposed framework achieves an average accuracy of 90% across repeated trials, demonstrating consistent and reliable performance.

翻译：大语言模型（LLMs）已成为具备跨领域强推理能力的基础模型。除被动文本生成外，主体性LLMs通过模块化任务分解与协同工具使用，可实现自主工作流执行。在结构工程领域，近期研究已开发出针对平面框架自动化分析的主体性LLMs，但其向三维框架的拓展仍因不规则几何表征、拓扑一致性及长时推理等挑战而尚未充分探索。本文提出一种基于主体性LLMs的框架，用于从自然语言输入中自动完成三维框架的结构分析。不规则三维框架通过投影至二维平面进行表征：正交网格线定义空间坐标，层数矩阵编码每个网格单元的竖向拉伸属性。基于该表征方式，框架构建了多智能体流水线：问题分析智能体将输入解析为结构化JSON格式；楼层分解智能体推导各楼层空间布局；三维几何由节点、主梁、楼板和柱智能体协同装配；支座与荷载智能体分配边界条件及荷载工况；代码转换智能体生成可执行的SAP2000脚本。在十个代表性三维框架上的评估表明，所提框架在重复试验中平均准确率达90%，展现出稳定可靠的性能。