Confinement effect of steel ties and/or carbon FRP in circular RC columns using neural network
Carbon fiber reinforced polymer (CFRP) is now commonly used in structural repair, strengthening and retrofitting of reinforced concrete (RC) members such as columns and beams. In retrofitting and strengthening RC columns, CFRP is wrapped around existing columns, which contain both longitudinal steel...
Saved in:
Main Authors: | , |
---|---|
Format: | text |
Published: |
Animo Repository
2005
|
Subjects: | |
Online Access: | https://animorepository.dlsu.edu.ph/faculty_research/3664 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Institution: | De La Salle University |
id |
oai:animorepository.dlsu.edu.ph:faculty_research-4666 |
---|---|
record_format |
eprints |
spelling |
oai:animorepository.dlsu.edu.ph:faculty_research-46662021-09-21T08:05:36Z Confinement effect of steel ties and/or carbon FRP in circular RC columns using neural network Ongpeng, Jason Maximino C. Oreta, Andres Winston C. Carbon fiber reinforced polymer (CFRP) is now commonly used in structural repair, strengthening and retrofitting of reinforced concrete (RC) members such as columns and beams. In retrofitting and strengthening RC columns, CFRP is wrapped around existing columns, which contain both longitudinal steel bars and lateral steel ties or hoops. Empirical models to predict the effect of confinement due to CFRP in the enhancement of the compressive strength of RC columns have been developed. However, an understanding of the interaction between the lateral steel ties and the CFRP on confinement and enhancement of compressive strength has still to be explored. Using the experimental data available in the literature, parameters such as ρs ρcc, ρCFRP, L, d, D, fyh, fyCFRP, and f’c were considered. The study used artificial neural network (ANN) modeling to predict the confined ultimate compressive strength, f’cc, produced by wrapping CFRP externally from circular sections reinforced with steel ties and longitudinal bars. Training different architectures of ANN models, and testing the derived models from existing models were done to arrive at an acceptable model. With the acceptable ANN model, the possible interaction between the two confining materials in the enhancement or increase of the compressive strength of circular columns was studied. © 2005 EUCENTRE. All rights reserved. 2005-01-01T08:00:00Z text https://animorepository.dlsu.edu.ph/faculty_research/3664 Faculty Research Work Animo Repository Carbon fiber-reinforced plastics Reinforced concrete Neural networks (Computer science) Civil Engineering |
institution |
De La Salle University |
building |
De La Salle University Library |
continent |
Asia |
country |
Philippines Philippines |
content_provider |
De La Salle University Library |
collection |
DLSU Institutional Repository |
topic |
Carbon fiber-reinforced plastics Reinforced concrete Neural networks (Computer science) Civil Engineering |
spellingShingle |
Carbon fiber-reinforced plastics Reinforced concrete Neural networks (Computer science) Civil Engineering Ongpeng, Jason Maximino C. Oreta, Andres Winston C. Confinement effect of steel ties and/or carbon FRP in circular RC columns using neural network |
description |
Carbon fiber reinforced polymer (CFRP) is now commonly used in structural repair, strengthening and retrofitting of reinforced concrete (RC) members such as columns and beams. In retrofitting and strengthening RC columns, CFRP is wrapped around existing columns, which contain both longitudinal steel bars and lateral steel ties or hoops. Empirical models to predict the effect of confinement due to CFRP in the enhancement of the compressive strength of RC columns have been developed. However, an understanding of the interaction between the lateral steel ties and the CFRP on confinement and enhancement of compressive strength has still to be explored. Using the experimental data available in the literature, parameters such as ρs ρcc, ρCFRP, L, d, D, fyh, fyCFRP, and f’c were considered. The study used artificial neural network (ANN) modeling to predict the confined ultimate compressive strength, f’cc, produced by wrapping CFRP externally from circular sections reinforced with steel ties and longitudinal bars. Training different architectures of ANN models, and testing the derived models from existing models were done to arrive at an acceptable model. With the acceptable ANN model, the possible interaction between the two confining materials in the enhancement or increase of the compressive strength of circular columns was studied. © 2005 EUCENTRE. All rights reserved. |
format |
text |
author |
Ongpeng, Jason Maximino C. Oreta, Andres Winston C. |
author_facet |
Ongpeng, Jason Maximino C. Oreta, Andres Winston C. |
author_sort |
Ongpeng, Jason Maximino C. |
title |
Confinement effect of steel ties and/or carbon FRP in circular RC columns using neural network |
title_short |
Confinement effect of steel ties and/or carbon FRP in circular RC columns using neural network |
title_full |
Confinement effect of steel ties and/or carbon FRP in circular RC columns using neural network |
title_fullStr |
Confinement effect of steel ties and/or carbon FRP in circular RC columns using neural network |
title_full_unstemmed |
Confinement effect of steel ties and/or carbon FRP in circular RC columns using neural network |
title_sort |
confinement effect of steel ties and/or carbon frp in circular rc columns using neural network |
publisher |
Animo Repository |
publishDate |
2005 |
url |
https://animorepository.dlsu.edu.ph/faculty_research/3664 |
_version_ |
1767195952503848960 |