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作者:

Wang, Senna (Wang, Senna.) | Cheng, Xiaowei (Cheng, Xiaowei.) | Li, Yi (Li, Yi.) (学者:李易) | Yang, Xiaohua (Yang, Xiaohua.) | Zhang, Haoyou (Zhang, Haoyou.) | Guo, Ruijie (Guo, Ruijie.) | Song, Xiaoyan (Song, Xiaoyan.) | Liang, Zihan (Liang, Zihan.)

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EI Scopus SCIE

摘要:

In the progressive collapse, the structural topology changed dynamically as failures propagate. Representing reinforced concrete (RC) frames with graph data leverages the topology of beam-column joints and structural components, enabling graph neural networks (GNNs) to predict the failure propagation. Graph convolutional networks (GCNs), a specific type of GNN, offer superior computational efficiency for graph data to traditional GNNs. However, three challenges must be addressed for GCNs to rapidly assess collapse regions: (1) insufficient data, (2) lack of graph representations for structural collapse, and (3) absence of tailored GCN architectures. To address these gaps, this study focused on RC frames with the following initiatives: (1) an efficient and accurate method for generating progressive collapse data was developed; (2) joint-based representation and componentbased representation were established, accompanied by a method for converting RC frames into these representations; (3) two GCN architectures were designed to identify failure paths and predicting collapse regions. The performance of the GCN models using various convolutional layer configurations was evaluated on the two graph representations, and the CGR-based model using SAGEConv layers exhibited the best performance with an accuracy and F1 score of 0.9839 and 0.8853, respectively. The proposed method exhibited over a 99 % improvement in calculation efficiency and approximately a 75 % reduction in memory usage compared with traditional FEM. Moreover, it captured the contribution of each component to progressive collapse resistance with better accuracy than previous approaches. Finally, this method was employed to predict the internal and external collapse regions of a real-world structure by incorporating a physics engine-based approach from prior studies.

关键词:

Collapse region Progressive collapse RC frame structure GCN Rapid assessment

作者机构:

  • [ 1 ] [Wang, Senna]Beijing Univ Technol, Beijing Key Lab Earthquake Engn & Struct Retrofit, Beijing 100124, Peoples R China
  • [ 2 ] [Cheng, Xiaowei]Beijing Univ Technol, Beijing Key Lab Earthquake Engn & Struct Retrofit, Beijing 100124, Peoples R China
  • [ 3 ] [Li, Yi]Beijing Univ Technol, Beijing Key Lab Earthquake Engn & Struct Retrofit, Beijing 100124, Peoples R China
  • [ 4 ] [Yang, Xiaohua]Beijing Univ Technol, Beijing Key Lab Earthquake Engn & Struct Retrofit, Beijing 100124, Peoples R China
  • [ 5 ] [Zhang, Haoyou]Beijing Univ Technol, Beijing Key Lab Earthquake Engn & Struct Retrofit, Beijing 100124, Peoples R China
  • [ 6 ] [Guo, Ruijie]Beijing Univ Technol, Beijing Key Lab Earthquake Engn & Struct Retrofit, Beijing 100124, Peoples R China
  • [ 7 ] [Song, Xiaoyan]Beijing Univ Technol, Beijing Key Lab Earthquake Engn & Struct Retrofit, Beijing 100124, Peoples R China
  • [ 8 ] [Liang, Zihan]Beijing Univ Technol, Beijing Key Lab Earthquake Engn & Struct Retrofit, Beijing 100124, Peoples R China

通讯作者信息:

  • [Cheng, Xiaowei]Beijing Univ Technol, Beijing Key Lab Earthquake Engn & Struct Retrofit, Beijing 100124, Peoples R China;;[Li, Yi]Beijing Univ Technol, Beijing Key Lab Earthquake Engn & Struct Retrofit, Beijing 100124, Peoples R China;;

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来源 :

ENGINEERING STRUCTURES

ISSN: 0141-0296

年份: 2024

卷: 322

5 . 5 0 0

JCR@2022

被引次数:

WoS核心集被引频次: 2

SCOPUS被引频次: 3

ESI高被引论文在榜: 0 展开所有

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