Smart Structures and Systems

  • 제27권4호
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  • Pages.607-621
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  • 2021
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  • 1738-1584(pISSN)
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  • 1738-1991(eISSN)
테크노프레스 (Techno-Press)
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Crowd jumping load simulation with generative adversarial networks

  • Xiong, Jiecheng (School of Civil Engineering, Zhengzhou University) ;
  • Chen, Jun (College of Civil Engineering and Architecture, Xinjiang University)
  • 투고 : 2020.03.07
  • 심사 : 2021.01.01
  • 발행 : 2021.04.25
⟨ 이전 논문 다음 논문 ⟩

초록

To mathematically represent crowd jumping loads, the features of the jumping load of each person, including pulse curve patterns, pulse interval sequences, and pulse energy sequences are considered. These features are essentially high-dimensional random variables. However, they have to be represented in a practically simplified model due to the lack of mathematical tools. The recently emerged generative adversarial networks (GANs) can model high-dimensional random variables well, as demonstrated in image synthesis and text generation. Therefore, this study adopts GANs as a new method for modelling crowd jumping loads. Conditional GANs (CGANs) combined with Wasserstein GANs with gradient penalty (WGANs-GP) are used in pulse curve pattern modelling, where a multi-layer perceptron and convolutional neural network are selected as the discriminator and generator, respectively. For the pulse energy sequence and pulse interval sequence modelling, similar GANs are used, where recurrent neural networks are selected as both the generator and discriminator. Finally, crowd jumping loads can be simulated by connected the pulse samples based on the pulse energy sequence samples and interval sequence samples, generated by the three proposed GANs. The experimental individual and crowd jumping load records are utilized in training GANs to ensure their output can simulate real load records well. Finally, the feasibility of the proposed GANs was verified by comparing the measured structural responses of an existing floor to the predicted structural responses.

키워드

과제정보

This work was supported by the National Natural Science Foundation of China (51778465, U1711264, 5200837) and Project ZYJKFW201811009. Moreover, the authors would like to thank all the test subjects for participating in the project and making the data collection possible.

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