| 수록사항 |
한국공간구조학회지 , Vol. 25, No. 4 (통권 102호)(2025-12) |
| 주제어 |
; Large language mode; RC beam design; AI agent; Prompt engineering; AI-assisted structural design |
| 요약2 |
This study develops and evaluates a prompt-driven large language model (LLM) agent for section design of doubly reinforced concrete (RC) beams. Using Google Gemini (Gems), an engineering “expert” that operates without fine-tuning by uploading ACI-318 provisions, sample design documents, and a database of prior beam designs was developed. The agent interprets code clauses, formulas, and constraints from these materials and retrieves similar design cases to propose an initial solution. It then incorporates user-specified natural-language constraints?most notably a strength-ratio cap (design strength ≤ 105% of required strength)?to iteratively refine toward safe and economical designs. Beyond reporting member size and reinforcement details, the agent provides step-by-step computational justifications for moment and shear checks, increasing verifiability and instructional value. We benchmark the LLM-generated designs against results from the commercial program MIDAS/Design+ and observe close agreement. In several scenarios, the constraint-guided LLM solutions are more material-efficient while remaining code-compliant. The workflow also supports batch processing from spreadsheet inputs, enabling practical automation across multiple beams. The approach requires no additional model training or coding making it accessible to non-developer practitioners. Results indicate that a general-purpose LLM, properly grounded with code documents and examples, can achieve practice-level performance with transparent reasoning. This demonstrates a viable approach to AI-assisted structural design that is explainable, interactive, and readily integrated with engineering workflows. |
