| 000 -LEADER |
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| fixed length control field |
a |
| 003 - CONTROL NUMBER IDENTIFIER |
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| control field |
OSt |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
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| control field |
20230217114906.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
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| fixed length control field |
230217b xxu||||| |||| 00| 0 eng d |
| 040 ## - CATALOGING SOURCE |
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| Original cataloging agency |
AIKTC-KRRC |
| Transcribing agency |
AIKTC-KRRC |
| 100 ## - MAIN ENTRY--PERSONAL NAME |
|---|
| 9 (RLIN) |
14291 |
| Author |
Gopinath, Smitha |
| 245 ## - TITLE STATEMENT |
|---|
| Title |
Artificial neural network based prediction for frp-confined concrete under cyclic loading |
| 250 ## - EDITION STATEMENT |
|---|
| Volume, Issue number |
Vol.103(4), Dec |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. |
|---|
| Place of publication, distribution, etc. |
USA |
| Name of publisher, distributor, etc. |
Springer |
| Year |
2022 |
| 300 ## - PHYSICAL DESCRIPTION |
|---|
| Pagination |
1015-1028p. |
| 520 ## - SUMMARY, ETC. |
|---|
| Summary, etc. |
Artificial neural network (ANN)-based model is developed for predicting the stress and strain enhancement of FRP wrapped concrete. An experimental database is considered in the investigations, which includes confinement using carbon, glass and aramid FRP on concrete. The aspect ratio, form of FRP wrap, number of confining layers, unconfined power, confining pressure and FRP characteristics are used as input parameters. Performance of proposed ANN models was evaluated by considering two indices, coefficient of determination and measure of root mean square error. The hoop strain, which is a main influencing parameter in confining pressure and dilation of FRP-confined concrete, is also predicted using ANN, for which no model predictions are available as of today. The predictive accuracy of some of the currently available models from the literature has been assessed by estimating the stress and strain enhancement due to FRP confinement by evaluating the root mean square error. The findings from the investigations show that ANN-based models can accurately predict the response close to experimental response and competitive enough compared to already existing mathematical models. The results pave way toward opening up the scope of data-driven models for the design of FRP confinement for structures. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| 9 (RLIN) |
4642 |
| Topical term or geographic name entry element |
Humanities and Applied Sciences |
| 700 ## - ADDED ENTRY--PERSONAL NAME |
|---|
| 9 (RLIN) |
20035 |
| Co-Author |
Ramesh Gopal |
| 773 0# - HOST ITEM ENTRY |
|---|
| Place, publisher, and date of publication |
Switzerland Springer |
| International Standard Serial Number |
2250-2149 |
| Title |
Journal of the institution of engineers (India): Series A |
| 856 ## - ELECTRONIC LOCATION AND ACCESS |
|---|
| URL |
https://link.springer.com/article/10.1007/s40030-022-00678-6 |
| Link text |
Click here |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
|---|
| Source of classification or shelving scheme |
|
| Koha item type |
Articles Abstract Database |
