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.

翻译：摘要：桥梁护栏等安全关键基础设施的有限元建模需高保真非线性动力分析，然而当前有限元建模过程仍存在劳动密集且缺乏自动化的问题。本文提出人机增强循环建模（HELM）框架，这是一种协作式人机交互协议，可将长序列有限元建模分解为几何生成、边界条件定义和材料分配等离散的可视化验证检查点。通过20组MASH TL-4和TL-5侧向加载条件下钢筋混凝土桥梁护栏的案例矩阵，该框架实现了专用智能体与两种广泛使用的商业有限元软件（ANSYS和LS-PrePost）的接口对接。实验结果表明，HELM将基准自主建模成功率从20%提升至75%，其中几何和边界条件任务的智能级通过率提升近一倍。错误分析显示空间推理和代数逻辑限制构成主要失效模式，凸显了结构化人机交互干预对建模自动化的价值。完整的智能体设计代码和提示词已开源，可通过https://github.com/SimAgentDev/Ansys-LSPP-AgentKit获取。