Automating Structural Analysis Across Multiple Software Platforms Using Large Language Models

2026-04-10

AI Summary

This study addresses the limitation of existing large language models (LLMs), which support only a single structural analysis software and thus fail to meet engineers’ needs for cross-platform modeling across tools such as ETABS, SAP2000, and OpenSees. To overcome this, the authors propose a two-stage multi-agent architecture: in the first stage, multiple agents collaboratively parse user input and generate a unified JSON representation of structural information; in the second stage, they parallelly produce executable scripts tailored to each target platform. This approach represents the first demonstration of LLM-based universal automated modeling for mainstream structural analysis software. By integrating multi-agent collaborative reasoning, structured information extraction, and code-translation prompt engineering, the method achieves over 90% accuracy across ten repeated trials on 20 representative frame problems, significantly enhancing the generality and reliability of engineering workflows.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Cognitive Modeling & Cognitive Systems: Agent Architectures
• Multiagent Systems: Modeling other Agents

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation

Abstract

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.

Problem

• structural analysis
• large language models
• multi-platform automation
• finite element analysis
• software interoperability

Innovation

• large language models
• multi-agent architecture
• cross-platform automation
• structural analysis
• finite element modeling