Integrating large language models for automated structural analysis

Abstract

Automated analysis for engineering structures offers considerable potential for boosting efficiency by minimizing repetitive tasks. Although AI-driven methods are increasingly common, no systematic framework yet leverages Large Language Models (LLMs) for automatic structural analysis. This paper proposes a framework that employs domain-specific prompt design and in-context learning strategies to enhance LLM problem-solving capabilities and generative stability, enabling fully automated structural analysis from descriptive text to model outputs. A small-scale benchmark dataset consisting of 20 structural analysis word problems (SAWPs) is also introduced to evaluate the performance of different LLMs within the proposed framework. The results demonstrate that the proposed approach can increase the level of automation in solving SAWPs compared with traditional methods. Quantitatively, the framework built on GPT-5.4 and GPT-4o both achieved 100% accuracy, outperforming GPT-4 (85%), Gemini 1.5 Pro (80%), and Llama-3.3 (30%) on the test examples. Furthermore, integrating domain-specific instructions enhanced performance by 30% on problems with asymmetrical structural configurations.