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Construction of Generalized Neural Network System for Recognizing Several Non-Linear Behaviors

[+] Author Affiliations
Taiji Mazda, Hisanori Otsuka, Wataru Yabuki, Kensuke Iwamoto

Kyushu University, Fukuoka City, Fukuoka, Japan

Paper No. PVP2003-2114, pp. 143-151; 9 pages
doi:10.1115/PVP2003-2114
From:
  • ASME 2003 Pressure Vessels and Piping Conference
  • Seismic Engineering
  • Cleveland, Ohio, USA, July 20–24, 2003
  • Conference Sponsors: Pressure Vessels and Piping Division
  • ISBN: 0-7918-4157-X
  • Copyright © 2003 by ASME

abstract

Generally, in formulating a spring-mass model for hysteric behavior of materials and members with inelastic characteristic, a mathematical model based on load-deformation experimental results is considered. The model must approximate the inelastic hysteresis of the material. However, assumption of material’s behavior using mathematical models is crucial, since it may cause serious errors if inappropriate model is applied for a particular situation. This paper describes multiple layered neural network to simulate the non-linear hysteretic behavior like Ramberg-Osgood model, modified bilinear model and Takeda model. In this study, based on the pattern recognition ability of neural network, non-linear hysteretic behavior was modeled by the network directly without replacing it with a mathematical model. The effectiveness and applicability of the network in numerical analysis were evaluated. Generalized multiple layered neural network to evaluate non-linear hysteretic curve was constructed. The network can recognize well the three types of hysteretic curve. The network is available as a subroutine of non-linear spring in dynamic response analysis.

Copyright © 2003 by ASME

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