Title:
Automatic Interpretation of Concrete Design Codes using a Domain-specific Large Language Model (LLM)
Author(s):
Jinxin Chen
Publication:
Web Session
Volume:
ws_S6_JinxinChen.pdf
Issue:
Appears on pages(s):
Keywords:
DOI:
Date:
3/29/2026
Abstract:
Large language models (LLMs) hold promises for automating information-intensive tasks, but their applications in safety-critical design and construction domains remains limited. This research developed a domain-specific framework designed to deliver a ready-to-use application for automatic interpretation of concrete design codes, combining LLMs with retrieval-augmented generation (RAG) tailored to specific codes. The framework has three primary innovations: (1) an advanced retrieval mechanism combining context-aware hierarchical search and fact-grounding to minimize hallucinations, critical for safety-critical applications; (2) training-free RAG architecture enabling convenient deployment without costly training or fine-tuning; and (3) human-computer interactive interface where engineers dynamically adjust parameters (chunk size, search scope, retrieval breadth) to align outputs with specific requirements. Evaluations on 1,000 code queries demonstrate > 95% accuracy, outperforming error-prone general-purpose models (< 2% accuracy). The success of the system in automating compliance verification and structural design tasks establishes it as a deployable tool for intelligent assistance in safety-critical concrete structure design applications.