Study of the nonlinear behavior of prestressed concrete girders by a neural network

W. Yabuki, T. Mazda, H. Otsuka

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)


For the performance design of a bridge, improvement of the hysteresis model of prestressed concrete (PC) members is necessary in order to carry out checking and design in considering the superstructure nonlinearity. However, new functions and parameters must be investigated to appropriately express features of the PC superstructure hysteresis loop, for example 'the origin directionality by the prestress' and 'the asymmetry of the configuration of the strands', therefore, the modeling is very complicated. Then, it was shown that nonlinear hysteretic behavior of a PC member could be simply modeled using the ability of a neural network to approximate function in this study. The cyclic loading test using 1/8 test specimens of the superstructure of an actual bridge was conducted to acquire the data for learning of a neural network.

ジャーナルAdvances in Earthquake Engineering
出版ステータス出版済み - 12月 1 2001
イベントThird International Conference on Earthquake Resistant Engineering Structures, ERES III - Malaga, スペイン
継続期間: 9月 4 20019月 6 2001

All Science Journal Classification (ASJC) codes

  • 土木構造工学
  • 地盤工学および土木地質学


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