000011124 001__ 11124
000011124 005__ 20141205155838.0
000011124 04107 $$aeng
000011124 046__ $$k2008-10-12
000011124 100__ $$aNakano, Yoshiaki
000011124 24500 $$aSubstructure Online Test Using Parallel Hysteresis Modeling by Neural Network

000011124 24630 $$n14.$$pProceedings of the 14th World Conference on Earthquake Engineering
000011124 260__ $$b
000011124 506__ $$arestricted
000011124 520__ $$2eng$$aIn general, hysteresis models that are applied to a numerical analysis part of substructure online tests do not refer directly to the experimental behavior of the members or subassemblage under loading tests. The objective of this study is to develop a new experimental technique for substructure online tests based on nonlinear hysteretic characteristics estimated with a neural network. A new learning algorithm for the network applicable to substructure online tests is proposed, focusing on input layer variables and their scaling method, and its validity is examined through several numerical and experimental investigations. The results show that the proposed testing scheme successfully reproduces the dynamic behavior of the model structure.

000011124 540__ $$aText je chráněný podle autorského zákona č. 121/2000 Sb.
000011124 653__ $$aearthquake response, substructure online test, neural network, Ramberg-Osgood model

000011124 7112_ $$a14th World Conference on Earthquake Engineering$$cBejing (CN)$$d2008-10-12 / 2008-10-17$$gWCEE15
000011124 720__ $$aNakano, Yoshiaki$$iYang, Won-Jik
000011124 8560_ $$ffischerc@itam.cas.cz
000011124 8564_ $$s785550$$uhttps://invenio.itam.cas.cz/record/11124/files/12-01-0185.pdf$$yOriginal version of the author's contribution as presented on CD, Paper ID: 12-01-0185.
000011124 962__ $$r9324
000011124 980__ $$aPAPER