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A Large Language Model as an Assistant for Software- Independent Structural Analytical Model Review and Editing

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Pubblicato in:ISARC. Proceedings of the International Symposium on Automation and Robotics in Construction vol. 42 (2025), p. 57-65
Autore principale: Lee, Justin S
Altri autori: Lee, Ghang
Pubblicazione:
IAARC Publications
Soggetti:
Accuracy
Editing
Prompt engineering
Large language models
Structural analysis
Information retrieval
Software
Language
Design optimization
Artificial intelligence
Architectural engineering
Ontology
Design engineering
Architecture
Structural engineering
Automation
Building information modeling
Engineers
Robotics
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Abstract:While structural analysis software facilitates the modeling of complex structures, it requires significant effort to master these tools. With advancements in large language models (LLMs), this study explores the potential of leveraging an LLM as a software-independent assistant for reviewing and editing structural analytical models. The proposed framework was tested using GPT-40 and an analytical model from Midas Gen, focusing on tasks such as model information extraction and editing. The results demonstrated the frameworks potential, achieving an accuracy of 91% when a system prompt With model data ontology information was used, compared to a 20% accuracy without the use of any prompt engineering.
Fonte:Advanced Technologies & Aerospace Database