This study investigates the application potential of large language models (LLMs), particularly GPT-4o, in generating geometric parameter suggestions during the early stages of structural design. Design recommendations from the LLM are validated using a finite-element solver (FFE Plus solver), forming the core workflow of the proposed approach. To assess its effectiveness, the LLM’s performance is compared against traditional regression-based surrogate models, which serve as baseline references. A two-hole hanger bracket serves as the case study, evaluating prediction accuracy, data efficiency, generalization capability, and workflow complexity across three materials: 6061-T6, AISI 304, and AISI 1020. The key evaluation indicators include safety factor (SF) and Mass. The results show that the regression models offer high accuracy and interpretability but require extensive amounts of simulation data; in this study, each material required 252 samples to adequately cover the design space. In contrast, GPT-4o produced feasible design suggestions using only 18 initial samples, combining semantic prompting and finite-element analysis. Its prediction accuracy improved significantly with a small number of iterations, demonstrating superior data efficiency and cross-material adaptability. Overall, the findings suggest that, when paired with appropriate prompting strategies and validation mechanisms, LLMs hold great promise as an assistive tool in early-stage structural design optimization.
Chien et al. (2025) studied this question.