Human-Enhanced Loop Modeling (HELM): Agent-Based Finite Element Modeling of Concrete Bridge Barriers
arXiv:2606.12025v1 Announce Type: new 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
The increasing complexity of infrastructure modeling and the maturation of AI agent technology are converging to address long-standing automation gaps.
This development allows for high-fidelity, safety-critical infrastructure modeling to become more efficient, reducing human labor and potential errors.
The labor-intensive finite element modeling process for critical infrastructure can now be partially automated and enhanced by AI agents, improving accuracy and speed.
- · Civil engineering firms
- · Infrastructure development companies
- · AI agent developers
- · Safety regulators
- · Traditional FE modeling service providers without AI integration
- · Manual FE modelers
Improved design and safety of new and existing critical infrastructure through more rigorous and efficient modeling.
Reduced project timelines and costs for large-scale infrastructure projects due to accelerated design and validation phases.
Enhanced resilience of national infrastructure against natural disasters and other stressors due to superior predictive modeling capabilities.
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Read at arXiv cs.AI