A Lightweight Multi-Agent Framework for Automated Concrete Barrier Design

arXiv cs.AI Papers
structural-engineering multi-agent-systems large-language-models concrete-barrier-design design-automation autogen lightweight-model

Summary

This paper proposes a lightweight multi-agent framework using AutoGen for automated concrete barrier design, achieving over 98% accuracy and showing that smaller models can outperform massive ones in this domain.

arXiv:2606.12040v1 Announce Type: new Abstract: The design of reinforced concrete highway barriers is a safety-critical process that requires strict compliance with regulatory provisions such as the AASHTO-LRFD bridge design guidelines. Current engineering practice relies heavily on manual, iterative, and heuristic calculations to satisfy complex nonlinear material and mechanics constraints. Although Large Language Models (LLMs) demonstrate strong generative capabilities, their direct application to structural engineering remains limited by hallucination risks and insufficient physical grounding. To address these challenges, this study proposes a novel "generation-evaluation-optimization" closed-loop framework for automated concrete barrier design using the multi-agent orchestration capabilities of AutoGen. Experimental results demonstrate that the proposed agentic framework achieves over 98% design accuracy, significantly outperforming standalone general-purpose LLMs. More importantly, the study reveals that design performance is not necessarily correlated with model scale, where an 8B-parameter lightweight model could outperform unconstrained 631B-parameter flagship models. This finding highlights the potential to substantially reduce computational costs while improving the accessibility of AI-assisted engineering tools for industry applications. The source code for the proposed multi-agent design framework is available at the project GitHub repository: https://github.com/MXY820/barrier-design. Keywords: Structural Engineering; Multi-Agent Systems; Large Language Models; Concrete Barrier Design; AutoGen; Design Automation.
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# A Lightweight Multi-Agent Framework for Automated Concrete Barrier Design
Source: [https://arxiv.org/abs/2606.12040](https://arxiv.org/abs/2606.12040)
[View PDF](https://arxiv.org/pdf/2606.12040)

> Abstract:The design of reinforced concrete highway barriers is a safety\-critical process that requires strict compliance with regulatory provisions such as the AASHTO\-LRFD bridge design guidelines\. Current engineering practice relies heavily on manual, iterative, and heuristic calculations to satisfy complex nonlinear material and mechanics constraints\. Although Large Language Models \(LLMs\) demonstrate strong generative capabilities, their direct application to structural engineering remains limited by hallucination risks and insufficient physical grounding\. To address these challenges, this study proposes a novel "generation\-evaluation\-optimization" closed\-loop framework for automated concrete barrier design using the multi\-agent orchestration capabilities of AutoGen\. Experimental results demonstrate that the proposed agentic framework achieves over 98% design accuracy, significantly outperforming standalone general\-purpose LLMs\. More importantly, the study reveals that design performance is not necessarily correlated with model scale, where an 8B\-parameter lightweight model could outperform unconstrained 631B\-parameter flagship models\. This finding highlights the potential to substantially reduce computational costs while improving the accessibility of AI\-assisted engineering tools for industry applications\. The source code for the proposed multi\-agent design framework is available at the project GitHub repository:[this https URL](https://github.com/MXY820/barrier-design)\. Keywords: Structural Engineering; Multi\-Agent Systems; Large Language Models; Concrete Barrier Design; AutoGen; Design Automation\.

## Submission history

From: Xiye Ma \[[view email](https://arxiv.org/show-email/93c0228f/2606.12040)\] **\[v1\]**Wed, 10 Jun 2026 13:06:11 UTC \(1,995 KB\)

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