The process of querying building codes has long been time-consuming and labor-intensive, requiring extensive manual effort to repeatedly consult and confirm regulations throughout design and review phases. Existing regulation query systems are limited to simple searches, often resulting in errors and failing to provide intelligent responses to users. Although the emergence of large language models (LLMs) offers potential solutions due to their natural language processing abilities, they face challenges such as insufficient domain knowledge, semantic misalignment, and difficulties in handling complex tabular data. To address these limitations, we propose a novel system, LLMs Query Building Codes (LLM-QueryBC), which integrates LLMs with a semantic network–enhanced retrieval-augmented generation (SN-RAG) for text-based queries and a specialized agent called Agent for Tables in Building Codes (TaBCe) for tabular queries. TaBCe leverages the Reasoning and Acting (ReAct) framework, think-by-structure planning method, RAG, computational tools, and a memory module to enhance its functionality. The system autonomously determines internal logic, invokes external tools, and mitigates hallucination issues associated with LLMs in the building code domain. We evaluated our system using fire safety regulations—a critical domain due to its impact on life safety, property protection, and legal compliance. Experimental results demonstrated significant improvements: textual query accuracy increased by 24%, and tabular query accuracy rose by 25%. Additionally, we conducted a case study involving fire safety code queries for a real-world design of a mixed-use high-rise building, further validating the system’s practical applicability. These advancements offer greater value to users and promote broader adoption of intelligent regulation query systems.
Zhu et al. (Tue,) studied this question.