| dc.contributor.author |
Barai, Sudhir Kumar |
|
| dc.date.accessioned |
2021-11-11T11:39:19Z |
|
| dc.date.available |
2021-11-11T11:39:19Z |
|
| dc.date.issued |
1997-01 |
|
| dc.identifier.uri |
https://www.sciencedirect.com/science/article/pii/S0965997896000385 |
|
| dc.identifier.uri |
http://dspace.bits-pilani.ac.in:8080/xmlui/handle/123456789/3558 |
|
| dc.description.abstract |
The recent developments in multilayer perceptron using the backpropagation algorithm, has opened up new possibilities in structural identification. Limitation of traditional neural networks (TNN) in dealing with patterns that may vary in time domain has given birth to time-delay neural networks (TDNN). In the present paper the TNN and the TDNN have been implemented in detecting the damage in bridge structure using vibration signature analysis. A comparative study has been carried out for the various cases of complete as well as incomplete measurement data. It has been observed that TDNNs have performed better than TNNs in this application. |
en_US |
| dc.language.iso |
en |
en_US |
| dc.publisher |
Elsiever |
en_US |
| dc.subject |
Civil Engineering |
en_US |
| dc.subject |
Artificial neural networks |
en_US |
| dc.subject |
Railway bridges |
en_US |
| dc.subject |
Damage detection |
en_US |
| dc.title |
Time-delay neural networks in damage detection of railway bridges |
en_US |
| dc.type |
Article |
en_US |