Prediction of energy absorption capability in fiber reinforced self-compacting concrete containing nano-silica particles using artificial neural network
Autor(a) principal: | |
---|---|
Data de Publicação: | 2014 |
Outros Autores: | , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Latin American journal of solids and structures (Online) |
Texto Completo: | http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1679-78252014000600004 |
Resumo: | The main objective of the present work is to utilize feedforward multi-layer perceptron (MLP) type of artificial neural networks (ANN) to find the combined effect of nano-silica and different fibers (steel, polypropylene, glass) on the toughness, flexural strength and fracture energy of concrete is evaluated.For this purpose, 40 mix plot including 4 series A and B and C and D, which contain, respectively, 0, 2, 4 and 6% weight of cement, nano-silica particles were used as a substitute for cement. Each of series includes three types of fibers (metal: 0.2, 0.3 and 0.5% volume and polypropylene: 0.1, 0.15 and 0.2 % volume and glass 0.15 and 0.2 and 0.3% by volume) were tested. The obtained results from the experimental data are used to train the MLP type artificial neural network. The Results of this study show that fibers conjugate presence and optimal percent of nano-silica improved toughness, flexural strength and fracture energy of concrete of Self-compacting concrete (SCC). Results of this study show that fibers conjugate presence and optimal per-cent of nano-silica improved toughness, toughness, fracture ener-gy and flexural strength of SCC. |
id |
ABCM-1_987a029d0e1180217f422537937d0736 |
---|---|
oai_identifier_str |
oai:scielo:S1679-78252014000600004 |
network_acronym_str |
ABCM-1 |
network_name_str |
Latin American journal of solids and structures (Online) |
repository_id_str |
|
spelling |
Prediction of energy absorption capability in fiber reinforced self-compacting concrete containing nano-silica particles using artificial neural networkFiberSelf-compacting concreteNano-silicamechanical propertiesartificial neural networkThe main objective of the present work is to utilize feedforward multi-layer perceptron (MLP) type of artificial neural networks (ANN) to find the combined effect of nano-silica and different fibers (steel, polypropylene, glass) on the toughness, flexural strength and fracture energy of concrete is evaluated.For this purpose, 40 mix plot including 4 series A and B and C and D, which contain, respectively, 0, 2, 4 and 6% weight of cement, nano-silica particles were used as a substitute for cement. Each of series includes three types of fibers (metal: 0.2, 0.3 and 0.5% volume and polypropylene: 0.1, 0.15 and 0.2 % volume and glass 0.15 and 0.2 and 0.3% by volume) were tested. The obtained results from the experimental data are used to train the MLP type artificial neural network. The Results of this study show that fibers conjugate presence and optimal percent of nano-silica improved toughness, flexural strength and fracture energy of concrete of Self-compacting concrete (SCC). Results of this study show that fibers conjugate presence and optimal per-cent of nano-silica improved toughness, toughness, fracture ener-gy and flexural strength of SCC.Associação Brasileira de Ciências Mecânicas2014-11-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S1679-78252014000600004Latin American Journal of Solids and Structures v.11 n.6 2014reponame:Latin American journal of solids and structures (Online)instname:Associação Brasileira de Engenharia e Ciências Mecânicas (ABCM)instacron:ABCM10.1590/S1679-78252014000600004info:eu-repo/semantics/openAccessTavakoli,Hamid RezaOmran,Omid LotfiKutanaei,Saman Soleimanishiade,Masoud Falahtabareng2014-03-13T00:00:00Zoai:scielo:S1679-78252014000600004Revistahttp://www.scielo.br/scielo.php?script=sci_serial&pid=1679-7825&lng=pt&nrm=isohttps://old.scielo.br/oai/scielo-oai.phpabcm@abcm.org.br||maralves@usp.br1679-78251679-7817opendoar:2014-03-13T00:00Latin American journal of solids and structures (Online) - Associação Brasileira de Engenharia e Ciências Mecânicas (ABCM)false |
dc.title.none.fl_str_mv |
Prediction of energy absorption capability in fiber reinforced self-compacting concrete containing nano-silica particles using artificial neural network |
title |
Prediction of energy absorption capability in fiber reinforced self-compacting concrete containing nano-silica particles using artificial neural network |
spellingShingle |
Prediction of energy absorption capability in fiber reinforced self-compacting concrete containing nano-silica particles using artificial neural network Tavakoli,Hamid Reza Fiber Self-compacting concrete Nano-silica mechanical properties artificial neural network |
title_short |
Prediction of energy absorption capability in fiber reinforced self-compacting concrete containing nano-silica particles using artificial neural network |
title_full |
Prediction of energy absorption capability in fiber reinforced self-compacting concrete containing nano-silica particles using artificial neural network |
title_fullStr |
Prediction of energy absorption capability in fiber reinforced self-compacting concrete containing nano-silica particles using artificial neural network |
title_full_unstemmed |
Prediction of energy absorption capability in fiber reinforced self-compacting concrete containing nano-silica particles using artificial neural network |
title_sort |
Prediction of energy absorption capability in fiber reinforced self-compacting concrete containing nano-silica particles using artificial neural network |
author |
Tavakoli,Hamid Reza |
author_facet |
Tavakoli,Hamid Reza Omran,Omid Lotfi Kutanaei,Saman Soleimani shiade,Masoud Falahtabar |
author_role |
author |
author2 |
Omran,Omid Lotfi Kutanaei,Saman Soleimani shiade,Masoud Falahtabar |
author2_role |
author author author |
dc.contributor.author.fl_str_mv |
Tavakoli,Hamid Reza Omran,Omid Lotfi Kutanaei,Saman Soleimani shiade,Masoud Falahtabar |
dc.subject.por.fl_str_mv |
Fiber Self-compacting concrete Nano-silica mechanical properties artificial neural network |
topic |
Fiber Self-compacting concrete Nano-silica mechanical properties artificial neural network |
description |
The main objective of the present work is to utilize feedforward multi-layer perceptron (MLP) type of artificial neural networks (ANN) to find the combined effect of nano-silica and different fibers (steel, polypropylene, glass) on the toughness, flexural strength and fracture energy of concrete is evaluated.For this purpose, 40 mix plot including 4 series A and B and C and D, which contain, respectively, 0, 2, 4 and 6% weight of cement, nano-silica particles were used as a substitute for cement. Each of series includes three types of fibers (metal: 0.2, 0.3 and 0.5% volume and polypropylene: 0.1, 0.15 and 0.2 % volume and glass 0.15 and 0.2 and 0.3% by volume) were tested. The obtained results from the experimental data are used to train the MLP type artificial neural network. The Results of this study show that fibers conjugate presence and optimal percent of nano-silica improved toughness, flexural strength and fracture energy of concrete of Self-compacting concrete (SCC). Results of this study show that fibers conjugate presence and optimal per-cent of nano-silica improved toughness, toughness, fracture ener-gy and flexural strength of SCC. |
publishDate |
2014 |
dc.date.none.fl_str_mv |
2014-11-01 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1679-78252014000600004 |
url |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1679-78252014000600004 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
10.1590/S1679-78252014000600004 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
text/html |
dc.publisher.none.fl_str_mv |
Associação Brasileira de Ciências Mecânicas |
publisher.none.fl_str_mv |
Associação Brasileira de Ciências Mecânicas |
dc.source.none.fl_str_mv |
Latin American Journal of Solids and Structures v.11 n.6 2014 reponame:Latin American journal of solids and structures (Online) instname:Associação Brasileira de Engenharia e Ciências Mecânicas (ABCM) instacron:ABCM |
instname_str |
Associação Brasileira de Engenharia e Ciências Mecânicas (ABCM) |
instacron_str |
ABCM |
institution |
ABCM |
reponame_str |
Latin American journal of solids and structures (Online) |
collection |
Latin American journal of solids and structures (Online) |
repository.name.fl_str_mv |
Latin American journal of solids and structures (Online) - Associação Brasileira de Engenharia e Ciências Mecânicas (ABCM) |
repository.mail.fl_str_mv |
abcm@abcm.org.br||maralves@usp.br |
_version_ |
1754302887381434368 |