土木工程师和结构设计团队终于有了一个靠谱的AI自动化方案——AutoGen多智能体框架让混凝土护栏设计准确率超98%，且8B小模型就能干翻631B大模型，做工程自动化的开发者可以直接用开源代码试试。
该研究提出了一种基于AutoGen的多智能体框架，用于钢筋混凝土公路护栏的自动化设计。该框架采用“生成-评估-优化”闭环流程，严格遵循AASHTO-LRFD桥梁设计规范。实验表明，该框架设计准确率超过98%，远超通用大语言模型。关键发现是，设计性能与模型规模无必然关联，一个8B参数的轻量模型甚至优于未约束的631B旗舰模型。这显著降低了计算成本，提升了AI辅助工程工具的可及性。代码已在GitHub开源。
A Lightweight Multi-Agent Framework for Automated Concrete Barrier Design
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.