🤖 AI Summary
This study addresses the low automation and inefficiency in finite element modeling of safety-critical infrastructure such as bridge barriers, which remains heavily reliant on manual effort. To overcome these limitations, the authors propose HELM, a human–AI collaborative framework that decomposes the modeling process into discrete, visually verifiable steps—including geometry generation, boundary condition definition, and material assignment—and integrates ANSYS with LS-PrePost. By introducing a novel structured collaboration protocol, the framework strategically incorporates human intervention to compensate for AI agents’ deficiencies in spatial reasoning and algebraic logic, substantially enhancing modeling reliability. Experimental results demonstrate that the success rate of complete model generation improves from 20% to 75%, with near-doubling in agent pass rates for geometry and boundary condition tasks. The project’s code and prompt library have been made publicly available.
📝 Abstract
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.