Interactive Design by Integrating a Large Pre-Trained Language Model and Building Information Modeling
Publication: Computing in Civil Engineering 2023
ABSTRACT
This study explores the potential of generative artificial intelligence (AI) models, specifically OpenAI’s generative pre-trained transformer (GPT) series, when integrated with building information modeling (BIM) tools as an interactive design assistant for architectural design. The research involves the development and implementation of three key components: (1) BIM2XML, a component that translates BIM data into extensible markup language (XML) format; (2) natural language-based architectural detailing through interaction with AI (NADIA), a component that refines the input design in XML by identifying designer intent, relevant objects, and their attributes, using pre-trained language models; and (3) XML2BIM, a component that converts AI-generated XML data back into a BIM tool. This study validated the proposed approach through a case study involving design detailing, using the GPT series and Revit. Our findings demonstrate the effectiveness of state-of-the-art language models in facilitating dynamic collaboration between architects and AI systems, highlighting the potential for further advancements.
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Published In
Computing in Civil Engineering 2023
Pages: 291 - 299
History
Published online: Jan 25, 2024
ASCE Technical Topics:
- Architectural engineering
- Architecture
- Artificial intelligence and machine learning
- Building design
- Building information modeling
- Building management
- Business management
- Case studies
- Computer programming
- Computing in civil engineering
- Decision making
- Decision support systems
- Design (by type)
- Dynamic models
- Engineering fundamentals
- Methodology (by type)
- Models (by type)
- Practice and Profession
- Research and development
- Research methods (by type)
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