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：对桥梁护栏等安全关键基础设施的有限元建模需进行高保真非线性动力分析，然而当前有限元建模流程仍存在劳动密集、自动化程度不足等问题。本文提出人机增强环回建模（HELM）框架——一种协作式人机智能体协议，可将长序列有限元建模分解为几何生成、边界条件定义及材料分配等离散且可视觉验证的检查点。该框架通过含20种工况的钢筋混凝土桥梁护栏矩阵（符合MASH TL-4和TL-5侧向加载条件）进行验证，并实现专用智能体与两款主流商业有限元软件（ANSYS和LS-PrePost）的接口对接。实验结果表明，HELM将基础自主建模成功率从20%提升至75%，其中几何与边界条件任务的智能体级通过率提升约一倍。误差分析显示，空间推理与代数逻辑限制是主要失效模式，凸显了结构化人在环干预对建模自动化的价值。完整智能体设计代码及提示词已开源，访问地址：https://github.com/SimAgentDev/Ansys-LSPP-AgentKit。