A Lightweight Large Language Model-Based Multi-Agent System for 2D Frame Structural Analysis
This addresses the problem of labor-intensive structural analysis workflows for engineers, though it is incremental as it applies existing multi-agent methods to a new domain.
The paper tackles automating finite element modeling for 2D frames in structural engineering by developing a lightweight LLM-based multi-agent system, achieving over 80% accuracy in most cases on benchmark problems and outperforming models like Gemini-2.5 Pro and ChatGPT-4o.
Large language models (LLMs) have recently been used to empower autonomous agents in engineering, significantly improving automation and efficiency in labor-intensive workflows. However, their potential remains underexplored in structural engineering, particularly for finite element modeling tasks requiring geometric modeling, complex reasoning, and domain knowledge. To bridge this gap, this paper develops a LLM-based multi-agent system to automate finite element modeling of 2D frames. The system decomposes structural analysis into subtasks, each managed by a specialized agent powered by the lightweight Llama-3.3 70B Instruct model. The workflow begins with a Problem Analysis Agent, which extracts geometry, boundary, and material parameters from the user input. Next, a Geometry Agent incrementally derives node coordinates and element connectivity by applying expert-defined rules. These structured outputs are converted into executable OpenSeesPy code by a Translation Agent and refined by a Model Validation Agent through consistency checks. Then, a Load Agent applies load conditions into the assembled structural model. Experimental evaluations on 20 benchmark problems demonstrate that the system achieves accuracy over 80% in most cases across 10 repeated trials, outperforming Gemini-2.5 Pro and ChatGPT-4o models.