Agentic Large Language Models for Automated Structural Analysis of 3D Frame Systems

• URL: http://arxiv.org/abs/2606.06525v2
• Date: Thu, 11 Jun 2026 13:52:20 GMT
• Title: Agentic Large Language Models for Automated Structural Analysis of 3D Frame Systems
• Abstract summary: Large language models (LLMs) have emerged as powerful foundation models with strong reasoning capabilities across domains.<n>In structural engineering, recent efforts have developed agentic LLMs for automated analysis of plane frames.<n>This paper proposes an agentic LLM framework for automated structural analysis of 3D frames from natural inputs.
• Score: 9.941684692042632
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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