ニューラルネットワークによる構造物の塑性履歴挙動のモデリング

概要

論文の詳細を見る
The artificial neural network is a newly developed technique for the identification and modeling of linear and nonlinear dynamic systems through the use of measured experimental data. In this paper, a neural network approach is employed for predicting the hysteretic restoring forces of structures and applied to dynamic response prediction for a single and multi degree of freedom system with restoring force characteristics of Ramberg-Osgood type. A multi-layered backpropagation neural network is chosen as the neural network to determine the restoring force at the next loading step as an output with the displacement responses and the corresponding restoring forces at the past few loading steps as inputs to the neural network. The application examples show the great promise of using the neural network in modeling of the nonlinear hysteretic behavior of structures.
社団法人日本建築学会の論文
1998-08-30