Intelligent design for component size generation in reinforced concrete frame structures using heterogeneous graph neural networks

研究简介

结构构件尺寸的设计高度依赖于工程师的经验，往往需要反复迭代调整以满足规范要求，耗时耗力。本研究针对框架结构的拓扑特征和不同构件的特异性构建了异构图节点表征方法，实现对结构的通用图谱表达，并进一步搭建异构图神经网络进行尺寸的预测。采用本方法可以在1秒内实现对混凝土框架结构构件尺寸的设计，与工程师设计结果平均偏差在一个模数以内，且满足设计规范要求，有望提升工程师的设计效率，同时保障设计结果的安全性和经济性。

引用格式

QIN S Z, LIAO W J, HUANG Y L, et al. Intelligent design for component size generation in reinforced concrete frame structures using heterogeneous graph neural networks[J/OL]. Automation in Construction, 2025, 171: 105967. DOI:10.1016/j.autcon.2025.105967.

BibTeX

@article{Qin20250113AIC,
  title = {Intelligent design for component size generation in reinforced concrete frame structures using heterogeneous graph neural networks},
  author = {Qin, Sizhong and Liao, Wenjie and Huang, Yuli and Zhang, Shulu and Gu, Yi and Han, Jin and Lu, Xinzheng},
  year = {2025},
  doi = {10.1016/j.autcon.2025.105967},
  journal = {Automation in Construction},
  volume = {171},
  pages = {105967}
}