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http://dx.doi.org/10.5000/EESK.2004.8.4.063

A Study on the Nonlinear Modeling of Lead Rubber Bearings by a Neural Network Theory  

Huh, Young-Cheol (한국기계연구원 구조연구부)
Kim, Young-Joong (한국기계연구원 구조연구부)
Kim, Byung-Hyun (한국기계연구원 구조연구부)
Publication Information
Journal of the Earthquake Engineering Society of Korea / v.8, no.4, 2004 , pp. 63-69 More about this Journal
Abstract
In this paper, a nonlinear modeling of lead rubber bearings(LRBs) was presented by a neural network theory. An shaking table test for a scaled frame model, of which base was isolated by the LRBs, was performed to verify numerical accuracies of the neural network model. White noise and three types of seismic records were adoped as base loads of the shaking table in order to train and generalize the neural network in case of seismic loads, numerical results of the neural network model were evaluated according to different magnitudes of PGA. As results, it is concluded that the presented neural network model has given a good agreement with the experimental data in details and can be useful to a nonlinear modeling of LRBs within prescribed domains.
Keywords
neural network theory; nonlinear modeling; lead rubber bearing; shaking table test;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
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