做桥梁护栏等安全关键基础设施有限元分析的工程师，HELM把建模成功率从20%拉到75%，省下大量手动调试时间，建议直接试开源代码。
本文提出Human-Enhanced Loop Modeling (HELM)框架，通过将长序列有限元建模分解为离散、可视觉验证的检查点，实现人机协作自动化。在20个钢筋混凝土桥梁护栏案例中，HELM将基线自主建模成功率从20%提升至75%，几何和边界条件任务的通过率翻倍。错误分析显示空间推理和代数逻辑限制是主要失败模式。框架已开源，支持ANSYS和LS-PrePost软件。
Human-Enhanced Loop Modeling (HELM): Agent-Based Finite Element Modeling of Concrete Bridge Barriers
Finite element (FE) modeling of safety-critical infrastructure such as bridge barriers requires high-fidelity nonlinear dynamic analysis, yet the current FE modeling process remains labor-intensive and lacks automation. This paper presents the Human-Enhanced Loop Modeling (HELM) framework, a collaborative human-agent protocol that decomposes long-sequence finite element modeling into discrete, visually verifiable checkpoints across geometry generation, boundary condition definition, and material assignment. The framework is demonstrated through a 20-case matrix of reinforced concrete bridge barriers under MASH TL-4 and TL-5 lateral loading conditions, interfacing specialized agents with two widely used commercial FE softwares, i.e., ANSYS and LS-PrePost. Experimental results show that HELM improves the baseline autonomous modeling success rate from 20% to 75%, with agent-level pass rates for geometry and boundary condition tasks approximately doubling. Error analysis reveals that spatial reasoning and algebraic logic limitations constitute the primary failure modes, underscoring the value of structured human-in-the-loop intervention for modeling automation. The complete agent design code and prompts are open-sourced and can be accessed at: https://github.com/SimAgentDev/Ansys-LSPP-AgentKit.