| Title |
Hysteretic Model using Artificial Neural Network |
| Authors |
Yi Waon-Ho ; Lee Hak-Su ; Lee Seung-Chang |
| Keywords |
Artificial Neural Network(ANN) ; Neuron ; Transfer function ; Hysteretic characteristic ; Nonlinear analysis |
| Abstract |
Artificial Neural Network (ANN) is a computational model inspired by the structure and operations of the brain. It is massively parallel system, consisting of a large number of highly interconnected and simple processing units. The purpose of this paper is to verify the applicability of ANN to predict experimental results through the use of measured experimental data. Although there have been accumulated data based on hysteretic characteristics of structural element with cyclic loading tests, it is difficult to directly apply them for the analysis of the elastic and plastic response. Thus simple models with mathematical formula, such as Bi-Linear model, Ramberg-Osgood model, Degrading Tri model, Takeda model, Slip type model, and etc, have been used. To verify the practicality and capability of this study, ANN is adapted to several models with mathematical formula using numerical data. To show the efficiency of ANN in the nonlinear analysis, it is important to determine the adequate input and output variables of hysteretic models and to minimize an error in the ANN process. An application example is the Beam-Column joint test using the ANN in modeling of the linear and nonlinear hysteretic behavior of the structure. |