标题： 将大型语言模型用于自动结构分析 显示英文标题

标题： Integrating Large Language Models for Automated Structural Analysis

摘要： 自动化分析工程结构可以显著提高效率，通过减少重复任务。虽然基于人工智能的方法越来越常见，但目前尚无系统框架利用大型语言模型（LLMs）进行自动结构分析。为解决这一问题，我们提出了一种新颖的框架，将LLMs与结构分析软件相结合。LLMs作为核心引擎：它们从文本中解析结构描述，并将其转换为可执行的Python脚本。此外，该框架将LLMs的生成能力与基于代码的有限元（FE）工具如OpenSeesPy相结合。它采用领域特定的提示设计和上下文学习策略，以增强LLMs的问题解决能力和生成稳定性，从而实现从描述性文本到模型输出的完全自动化结构分析。在我们的实验中，我们引入了一个精心整理的小规模基准数据集，包含20个结构分析单词问题（SAWPs）及其真实解，并评估了不同LLMs在我们的框架中解决这些SAWPs的性能。还研究了由结构工程师设计的系统指令的作用，以了解它们对LLM驱动的结构分析的影响。此外，还考察了我们框架的生成稳定性。通过对基准的多次验证实验，我们的结果表明，与传统方法相比，所提出的框架可以显著提高解决SAWPs的自动化水平。定量上，基于GPT-4o的框架在测试示例中达到了100%的准确率，超过了GPT-4（85%）、Gemini 1.5 Pro（80%）和Llama-3.3（30%）。此外，在具有非对称结构配置的问题上，集成领域特定指令使性能提高了30%。 显示英文摘要

摘要： 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. To address this gap, we propose a novel framework that integrates LLMs with structural analysis software. LLMs serve as the core engine: they parse structural descriptions from text and translate them into executable Python scripts. Moreover, the framework integrates the generative capabilities of LLMs with code-based finite element (FE) tools like OpenSeesPy. It employs domain-specific prompt design and in-context learning strategies to enhance the LLM's problem-solving capabilities and generative stability, enabling fully automated structural analysis from descriptive text to model outputs. In our experiments, we introduce a well-curated small-scale benchmark dataset of 20 structural analysis word problems (SAWPs) with ground-truth solutions and evaluate the performance of different LLMs within our framework in solving these SAWPs. The role of system instructions, crafted by structural engineers, is also investigated to understand their impact on LLM-driven structural analysis. Additionally, the generative stability of our framework is examined. Through multiple validation experiments on the benchmark, our results demonstrate that the proposed framework can substantially increase the level of automation in solving SAWPs compared to traditional methods. Quantitatively, the framework, built on GPT-4o, achieved 100% accuracy, surpassing 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.