人机协作提升桥梁护栏有限元建模效率，成功率从20%升至75%
Human-Enhanced Loop Modeling (HELM): Agent-Based Finite Element Modeling of Concrete Bridge Barriers
- 将建模流程拆分为可视觉验证的步骤，人机协同完成几何、边界条件与材料赋值
- 在20种工况下测试，建模成功率从20%提升至75%，几何与边界条件任务通过率翻倍
- 适合结构工程自动化研究者，开源代码可直接复用
针对桥梁护栏等关键基础设施的高保真非线性动态有限元分析，当前建模过程仍高度依赖人工且缺乏自动化。本文提出人机增强循环建模（HELM）框架，通过分解长序列建模为可视觉验证的离散检查点，实现几何生成、边界条件定义和材料赋值的协同。该框架在MASH TL-4与TL-5横向荷载条件下对20种钢筋混凝土护栏案例进行验证，对接主流商业软件ANSYS与LS-PrePost。实验表明，HELM将基线自主建模成功率从20%提升至75%，几何与边界条件任务的代理通过率约翻倍。错误分析显示，空间推理与代数逻辑能力不足是主要失败原因，凸显结构化人机协同对建模自动化的价值。完整代理设计代码与提示词已开源，访问地址：https://github.com/SimAgentDev/Ansys-LSPP-AgentKit。
原文摘要 · Abstract (English)
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.
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