Recognition of Nonlinear Hysteretic Behavior by Neural Network using Deep Learning

Taiji Mazda, Yukihide Kajita, Takashi Akedo, Tsubasa Hazama

研究成果: ジャーナルへの寄稿会議記事査読

1 被引用数 (Scopus)


In Japan, large earthquakes have caused many damages to structures. Cracks in the reinforced concrete and plasticization of the steel bar and steel components resulted in increased damage. In the seismic design, the dynamic response analysis is carried out by using a mathematical model that appropriately evaluates the nonlinearity of the material and the components and the seismic performance is confirmed by performing a dynamic response analysis. However, when applying new materials and components, much time and much effort are required to select a mathematical model. In this study, we focused on the high pattern recognition capability of the neural network. We attempt to directly model using a neural network without replacing the complex nonlinear hysteretic behavior using the mathematical model. By improvement of learning data and introduction of deep learning, it was confirmed that the recognition ability for nonlinear hysteretic behavior of neural network was greatly improved, and its applicability as a numerical operation subroutine for time history response analysis was drastically improved.

ジャーナルIOP Conference Series: Materials Science and Engineering
出版ステータス出版済み - 6月 12 2020
イベント2019 5th International Conference on Architecture, Materials and Construction, ICAMC 2019 - Lisbon, ポルトガル
継続期間: 12月 2 201912月 4 2019

!!!All Science Journal Classification (ASJC) codes

  • 材料科学(全般)
  • 工学(全般)


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