Automated detailing of exterior walls using NADIA: Natural-language-based architectural detailing through interaction with AI

Learning building information modeling (BIM) systems has always been a challenge for BIM adoption. Although groundbreaking performances of large language models (LLMs) have inspired many researchers to consider an LLM as a potential BIM control method using natural language, a specific method of utilizing LLMs for automated BIM model detailing has not yet been proposed. This paper proposes an LLM-BIM chaining framework to enable architectural design detailing using natural language, instead of using menu-based user interfaces, named “Natural-language-based Architectural Detailing through Interaction with AI (NADIA)”. The NADIA framework is based on three main approaches: 1) separating the specification of the wall layers from the creation of the wall layers; 2) appropriate instruction prompting to guide the LLM to minimize irrational responses and produce engineering rational details; and 3) LLM-BIM chaining to seamlessly link a BIM authoring tool and an LLM. The effectiveness of NADIA was validated based on two main aspects: its accuracy in generating details that adhere to specified design requirements from users—as a design assistant—and its compliance with general engineering requirements—as a design consultant. The validation was achieved through tasks that involved generating 240 and 1,920 exterior wall details, respectively. NADIA achieved an average accuracy of 83.33% in generating logically coherent details in line with the required design conditions. For thermal performance requirements, it demonstrated a mean accuracy of 98.54% in complying with the American Society of Heating, Refrigerating, and Air-Conditioning Engineers (ASHRAE) 20.1–2019 standard. Despite being in its early stages, NADIA's potential for developing and refining architectural details through natural language-based interactions between architects and machines is promising.