Prediction of Splitting Tensile Strength of Self-Compacting Recycled Aggregate Concrete Using Novel Deep Learning Methods

[EN] The composition of self-compacting concrete (SCC) contains 60–70% coarse and fine aggregates, which are replaced by construction waste, such as recycled aggregates (RA). However, the complexity of its structure requires a time-consuming mixed design. Currently, many researchers are studying the...

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Autores: Zaid, Osama, Palencia Coto, María Covadonga, Martínez García, Rebeca, Prado Gil, Jesús de
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2022
País:España
Institución:Universidad de León
Repositorio:BULERIA. Repositorio Institucional de la Universidad de León
OAI Identifier:oai:buleria.unileon.es:10612/20679
Acceso en línea:https://hdl.handle.net/10612/20679
Access Level:acceso abierto
Palabra clave:Ingeniería de minas
Artificial Neural Network
Self-compacting Concrete
Recycled Aggregates
Tensile Strength
Levenberg–marquardt
Bayesian Regularization
Scaled Conjugate Gradient Backpropagation
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oai_identifier_str oai:buleria.unileon.es:10612/20679
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repository_id_str
spelling Prediction of Splitting Tensile Strength of Self-Compacting Recycled Aggregate Concrete Using Novel Deep Learning MethodsZaid, OsamaPalencia Coto, María CovadongaMartínez García, RebecaPrado Gil, Jesús deIngeniería de minasArtificial Neural NetworkSelf-compacting ConcreteRecycled AggregatesTensile StrengthLevenberg–marquardtBayesian RegularizationScaled Conjugate Gradient Backpropagation[EN] The composition of self-compacting concrete (SCC) contains 60–70% coarse and fine aggregates, which are replaced by construction waste, such as recycled aggregates (RA). However, the complexity of its structure requires a time-consuming mixed design. Currently, many researchers are studying the prediction of concrete properties using soft computing techniques, which will eventually reduce environmental degradation and other material waste. There have been very limited and contradicting studies regarding prediction using different ANN algorithms. This paper aimed to predict the 28-day splitting tensile strength of SCC with RA using the artificial neural network technique by comparing the following algorithms: Levenberg–Marquardt (LM), Bayesian regularization (BR), and Scaled Conjugate Gradient Backpropagation (SCGB). There have been very limited and contradicting studies regarding prediction by using and comparing different ANN algorithms, so a total of 381 samples were collected from various published journals. The input variables were cement, admixture, water, fine and coarse aggregates, and superplasticizer; the data were randomly divided into three sets—training (60%), validation (10%), and testing (30%)—with 10 neurons in the hidden layer. The models were evaluated by the mean squared error (MSE) and correlation coefficient (R). The results indicated that all three models have optimal accuracy; still, BR gave the best performance (R = 0.91 and MSE = 0.2087) compared with LM and SCG. BR was the best model for predicting TS at 28 days for SCC with RA. The sensitivity analysis indicated that cement (30.07%) was the variable that contributed the most to the prediction of TS at 28 days for SCC with RA, and water (2.39%) contributed the least.SIMDPIExplotacion de MinasEscuela Superior y Tecnica de Ingenieros de Minas2022info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttps://hdl.handle.net/10612/20679reponame:BULERIA. Repositorio Institucional de la Universidad de Leóninstname:Universidad de LeónIngléshttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessoai:buleria.unileon.es:10612/206792026-06-24T12:43:27Z
dc.title.none.fl_str_mv Prediction of Splitting Tensile Strength of Self-Compacting Recycled Aggregate Concrete Using Novel Deep Learning Methods
title Prediction of Splitting Tensile Strength of Self-Compacting Recycled Aggregate Concrete Using Novel Deep Learning Methods
spellingShingle Prediction of Splitting Tensile Strength of Self-Compacting Recycled Aggregate Concrete Using Novel Deep Learning Methods
Zaid, Osama
Ingeniería de minas
Artificial Neural Network
Self-compacting Concrete
Recycled Aggregates
Tensile Strength
Levenberg–marquardt
Bayesian Regularization
Scaled Conjugate Gradient Backpropagation
title_short Prediction of Splitting Tensile Strength of Self-Compacting Recycled Aggregate Concrete Using Novel Deep Learning Methods
title_full Prediction of Splitting Tensile Strength of Self-Compacting Recycled Aggregate Concrete Using Novel Deep Learning Methods
title_fullStr Prediction of Splitting Tensile Strength of Self-Compacting Recycled Aggregate Concrete Using Novel Deep Learning Methods
title_full_unstemmed Prediction of Splitting Tensile Strength of Self-Compacting Recycled Aggregate Concrete Using Novel Deep Learning Methods
title_sort Prediction of Splitting Tensile Strength of Self-Compacting Recycled Aggregate Concrete Using Novel Deep Learning Methods
dc.creator.none.fl_str_mv Zaid, Osama
Palencia Coto, María Covadonga
Martínez García, Rebeca
Prado Gil, Jesús de
author Zaid, Osama
author_facet Zaid, Osama
Palencia Coto, María Covadonga
Martínez García, Rebeca
Prado Gil, Jesús de
author_role author
author2 Palencia Coto, María Covadonga
Martínez García, Rebeca
Prado Gil, Jesús de
author2_role author
author
author
dc.contributor.none.fl_str_mv Explotacion de Minas
Escuela Superior y Tecnica de Ingenieros de Minas
dc.subject.none.fl_str_mv Ingeniería de minas
Artificial Neural Network
Self-compacting Concrete
Recycled Aggregates
Tensile Strength
Levenberg–marquardt
Bayesian Regularization
Scaled Conjugate Gradient Backpropagation
topic Ingeniería de minas
Artificial Neural Network
Self-compacting Concrete
Recycled Aggregates
Tensile Strength
Levenberg–marquardt
Bayesian Regularization
Scaled Conjugate Gradient Backpropagation
description [EN] The composition of self-compacting concrete (SCC) contains 60–70% coarse and fine aggregates, which are replaced by construction waste, such as recycled aggregates (RA). However, the complexity of its structure requires a time-consuming mixed design. Currently, many researchers are studying the prediction of concrete properties using soft computing techniques, which will eventually reduce environmental degradation and other material waste. There have been very limited and contradicting studies regarding prediction using different ANN algorithms. This paper aimed to predict the 28-day splitting tensile strength of SCC with RA using the artificial neural network technique by comparing the following algorithms: Levenberg–Marquardt (LM), Bayesian regularization (BR), and Scaled Conjugate Gradient Backpropagation (SCGB). There have been very limited and contradicting studies regarding prediction by using and comparing different ANN algorithms, so a total of 381 samples were collected from various published journals. The input variables were cement, admixture, water, fine and coarse aggregates, and superplasticizer; the data were randomly divided into three sets—training (60%), validation (10%), and testing (30%)—with 10 neurons in the hidden layer. The models were evaluated by the mean squared error (MSE) and correlation coefficient (R). The results indicated that all three models have optimal accuracy; still, BR gave the best performance (R = 0.91 and MSE = 0.2087) compared with LM and SCG. BR was the best model for predicting TS at 28 days for SCC with RA. The sensitivity analysis indicated that cement (30.07%) was the variable that contributed the most to the prediction of TS at 28 days for SCC with RA, and water (2.39%) contributed the least.
publishDate 2022
dc.date.none.fl_str_mv 2022
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv https://hdl.handle.net/10612/20679
url https://hdl.handle.net/10612/20679
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.rights.none.fl_str_mv http://creativecommons.org/licenses/by-nc-nd/4.0/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc-nd/4.0/
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv MDPI
publisher.none.fl_str_mv MDPI
dc.source.none.fl_str_mv reponame:BULERIA. Repositorio Institucional de la Universidad de León
instname:Universidad de León
instname_str Universidad de León
reponame_str BULERIA. Repositorio Institucional de la Universidad de León
collection BULERIA. Repositorio Institucional de la Universidad de León
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,300719

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