Advanced Search

Journal Navigation

Journal Home

Subscriptions

Archive

Contact Us

Table of Contents

SAGETRACK

Sign In to gain access to subscriptions and/or personal tools.
Journal of Intelligent Material Systems and Structures
This Article
Right arrow Full Text (PDF)
Right arrow References
Right arrow Alert me when this article is cited
Right arrow Alert me if a correction is posted
Services
Right arrow Email this article to a friend
Right arrow Similar articles in this journal
Right arrow Alert me to new issues of the journal
Right arrow Add to Saved Citations
Right arrow Download to citation manager
Right arrowRequest Permissions
Right arrow Request Reprints
Right arrow Add to My Marked Citations
Citing Articles
Right arrow Citing Articles via Google Scholar
Right arrow Citing Articles via Scopus
Google Scholar
Right arrow Articles by Conte, J. P.
Right arrow Articles by Shelton, R. O.
Right arrow Search for Related Content
Social Bookmarking
Add to CiteULike   Add to Complore   Add to Connotea   Add to Del.icio.us   Add to Digg   Add to Reddit   Add to Technorati   Add to Twitter  
What's this?

Seismic Response Modeling of Multi-Story Buildings Using Neural Networks

Joel P. Conte

Civil Engineering Department Rice University Houston, TX 77251-1892

Ahmad J. Durrani

Civil Engineering Department Rice University Houston, TX 77251-1892

Robert O. Shelton

Software Technology Branch NASA Johnson Space Center Houston, TX 77058

A neural network based approach to model the seismic response of multi-story frame buildings is presented. The seismic response of frames is emulated using multi-layer feedforward neural networks with a backpropagation learning algorithm. Actual earthquake accelerograms and corresponding structural response obtained from analytical models of buildings are used in training the neural networks. The application of the neural network model is demonstrated by studying one to six story high building frames subjected to seismic base excitation. Furthermore, the learning ability of the network is examined for the case of multiple inputs where lateral forces at floor levels are included simultaneously with the base excitation. The effects of the network parameters on learn ing and accuracy of predictions are discussed. Based on this study, it is found that appropriately con figured neural network models can successfully learn and simulate the linear elastic dynamic be havior of multi-story buildings.

Journal of Intelligent Material Systems and Structures, Vol. 5, No. 3, 392-402 (1994)
DOI: 10.1177/1045389X9400500312
Add to CiteULike CiteULike   Add to Complore Complore   Add to Connotea Connotea   Add to Del.icio.us Del.icio.us   Add to Digg Digg   Add to Reddit Reddit   Add to Technorati Technorati   Add to Twitter Twitter    What's this?