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Seismic Response Modeling of Multi-Story Buildings Using Neural Networks
| Content Provider | Semantic Scholar |
|---|---|
| Author | Conte, Joel P. Durrani, Ahmad J. Shelton, Robert O. |
| Copyright Year | 1994 |
| Abstract | 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. |
| Starting Page | 392 |
| Ending Page | 402 |
| Page Count | 11 |
| File Format | PDF HTM / HTML |
| DOI | 10.1177/1045389X9400500312 |
| Volume Number | 5 |
| Alternate Webpage(s) | http://jaguar.ucsd.edu/publications/refereed_journals/Conte_Durrani_Shelton_Journal%20of%20Intelligent%20Material%20Systems-5(3)-May1994.pdf |
| Alternate Webpage(s) | https://doi.org/10.1177/1045389X9400500312 |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
