Automating Structural Analysis Across Multiple Software Platforms Using Large Language Models
Authors: Ziheng Geng, Jiachen Liu, Ian Franklin, Ran Cao, Dan M. Frangopol, Minghui Cheng
Organizations: Department of Civil and Architectural Engineering, University of Miami, Coral Gables, FL 33146, USA · HBC Engineering Company, Miami, FL 33178, USA · College of Civil Engineering, Hunan University, Changsha, 410082, China · Department of Civil and Environmental Engineering, Lehigh University, Bethlehem, PA 18015, USA · School of Architecture, University of Miami, Coral Gables, FL 33146, USA
Recent advances in large language models (LLMs) have shown the promise to significantly accelerate the workflow by automating structural modeling and analysis. However, existing studies primarily focus on enabling LLMs to operate a single structural analysis software platform. In practice, structural engineers often rely on multiple finite element analysis (FEA) tools, such as ETABS, SAP2000, and OpenSees, depending on project needs, user preferences, and company constraints. This limitation restricts the practical deployment of LLM-assisted engineering workflows. To address this gap, this study develops LLMs capable of automating frame structural analysis across multiple software platforms. The LLMs adopt a two-stage multi-agent architecture. In Stage 1, a cohort of agents collaboratively interpret user input and perform structured reasoning to infer geometric, material, boundary, and load information required for finite element modeling. The outputs of these agents are compiled into a unified JSON representation. In Stage 2, code translation agents operate in parallel to convert the JSON file into executable scripts across multiple structural analysis platforms. Each agent is prompted with the syntax rules and modeling workflows of its target software. The LLMs are evaluated using 20 representative frame problems across three widely used platforms: ETABS, SAP2000, and OpenSees. Results from ten repeated trials demonstrate consistently reliable performance, achieving accuracy exceeding 90% across all cases.
Figures & tables
Figure 1: Development of LLMs capable of operating multiple software platforms for automated structural analysis.
Figure 2: A benchmark dataset comprising twenty representative frame structural analysis problems (adapted from Geng et al. , 2025 ).
Figure 3: Textual template for problem description in frame structural analysis.
Figure 4: Multi-agent system architecture for automated frame structural analysis across FEA platforms.
Figure 5: Workflow of Stage 1, where multiple agents interpret user input and perform semantic reasoning to generate a unified JSON representation for FEA analysis.
Figure 6: Workflow of Stage 2, where geometry translation agent and code compilation agent transform the JSON representation into an executable structural analysis script.
Figure 7: Semantic mapping agent that converts JSON files into story-level representations.
Figure 8: Performance of the proposed multi - agent LLMs across three structural analysis platforms.
Figure 9: Visualization of structural analysis results from OpenSees using STKO.
Figure 10: Visualization of structural analysis results from SAP2000.
Figure 11: Visualization of structural analysis results from ETABS.
Figure 12: Performance of GPT-5.2 on frame structural analysis tasks across three FEA platforms
Figure 13: Illustrative examples of semantic reasoning errors in OpenSees scripts generated by GPT-5.2.
Figure 14: Performance of Gemini-3-Pro on frame structural analysis tasks across three FEA platforms
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.
Ziheng Geng, Ian Franklin, Santiago Martinez +3
Department of Civil and Architectural Engineering, University of Miami, Coral Gables, FL 33146, USA · School of Architecture, University of Miami, Coral Gables, FL 33146, USA · HBC Engineering Company, Doral, FL 33178, USA +1
Large language models (LLMs) such as GPT and Gemini have demonstrated remarkable capabilities in contextual understanding and reasoning. The strong performance of LLMs has sparked growing interest in leveraging them to automate tasks traditionally dependent on human expertise. Recently, LLMs have been integrated into intelligent agents capable of operating structural analysis software (e.g., OpenSees) to construct structural models and perform analyses. However, existing LLMs are limited in handling multi-step structural modeling due to frequent hallucinations and error accumulation during long-sequence operations. To this end, this study presents a novel multi-agent architecture to automate the structural modeling and analysis using OpenSeesPy. First, problem analysis and construction planning agents extract key parameters from user descriptions and formulate a stepwise modeling plan. Node and element agents then operate in parallel to assemble the frame geometry, followed by a load assignment agent. The resulting geometric and load information is translated into executable OpenSeesPy scripts by code translation agents. The proposed architecture is evaluated on a benchmark of 20 frame problems over ten repeated trials, achieving 100% accuracy in 18 cases and 90% in the remaining two. The architecture also significantly improves computational efficiency and demonstrates scalability to larger structural systems.
Ziheng Geng, Jiachen Liu, Ran Cao +3
Department of Civil and Architectural Engineering, University of Miami, Coral Gables, FL 33146, USA · HBC Engineering Company, Miami, FL 33178, USA · College of Civil Engineering, Hunan University, Changsha, 410082, China +3
Finite Element Analysis (FEA) serves as the cornerstone of modern engineering design. However, its workflow is inherently complex and relies heavily on domain expertise. Although recent efforts have integrated Large Language Models (LLMs) into FEA, existing approaches face limitations in handling multimodal inputs and executing complex tasks. To address these limitations, we propose VFEAgent, an end-to-end multi-agent system designed to automate FEA modeling and simulation directly from input images and problem descriptions. Our methodology integrates two core components: (1) a multimodal vision-language multi-agent pipeline that employs ReAct-driven reasoning to extract structured FEA specifications from heterogeneous inputs and (2) a verification-first code synthesis framework, incorporating robust self-debugging and fallback mechanisms to ensure executability and physical validity. We systematically evaluated the system across various engineering mechanics scenarios. The results demonstrate that VFEAgent achieves a high success rate in generating complete and physically valid simulations, outperforming LLM-based baseline methods in reliability and correctness. These findings validate the feasibility of automating the complete FEA workflow, highlighting the framework's potential to liberate engineers from tedious manual analysis.
Jiachen Zhang, Junyi Lao, Chenghao Liu +5
Peking University · China Agricultural University · 1Peking University +1