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...

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Main Authors: Ongpeng, Jason Maximino C., Oreta, Andres Winston C.
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Published: Animo Repository 2005
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Online Access:https://animorepository.dlsu.edu.ph/faculty_research/3664
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Institution: De La Salle University
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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
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