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Intelligent design for component size generation in reinforced concrete frame structures using heterogeneous graph neural networks
Automation in Construction ( IF 9.6 ) Pub Date : 2025-01-13 , DOI: 10.1016/j.autcon.2025.105967
Sizhong Qin, Wenjie Liao, Yuli Huang, Shulu Zhang, Yi Gu, Jin Han, Xinzheng Lu

Traditional reinforced concrete (RC) frame design depends on extensive engineering experience and iterative verification processes, often resulting in significant inefficiencies. The diversity in the topologies and behaviors of structural components further presents considerable obstacles to effective machine learning applications in design. This paper introduces an approach using heterogeneous graph neural networks (HetGNNs) to automate and optimize the dimensioning of frame components. This method captures the distinct frame topologies by developing a precisely tailored heterogeneous graph node representation. Leveraging a unique dataset derived from engineering drawings, the HetGNN model learns to size the component sections accurately. It is demonstrated that this method offers a transformative improvement in the efficiency, accuracy, and cost-effectiveness of structural design while adhering to design standards. The size design of RC frame structures can be completed in under one second, with an average size deviation of around 50 mm (one module) compared to those designed by engineers.

中文翻译:


使用异构图神经网络在钢筋混凝土框架结构中生成组件尺寸的智能设计



传统的钢筋混凝土 (RC) 框架设计依赖于丰富的工程经验和迭代验证过程,这通常会导致效率严重低下。结构组件拓扑和行为的多样性进一步为设计中的有效机器学习应用带来了相当大的障碍。本文介绍了一种使用异构图神经网络 (HetGNN) 来自动化和优化框架组件尺寸的方法。这种方法通过开发精确定制的异构图形节点表示来捕获不同的帧拓扑。利用从工程图纸中得出的独特数据集,HetGNN 模型学习准确确定组件截面的尺寸。结果表明,这种方法在遵守设计标准的同时,在结构设计的效率、准确性和成本效益方面提供了变革性的改进。RC 框架结构的尺寸设计可以在 1 秒内完成,与工程师设计的相比,平均尺寸偏差约为 50 毫米(一个模块)。
更新日期:2025-01-13
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