Comparison of machine learning techniques to predict the compressive strength of concrete and considerations on model generalization
Autor(a) principal: | |
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Data de Publicação: | 2022 |
Outros Autores: | , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Revista IBRACON de Estruturas e Materiais |
Texto Completo: | http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1983-41952022000500200 |
Resumo: | Abstract The compressive strength of concrete is an essential property to ensure the safety of a concrete structure. However, estimating this value is usually a laborious and uncertain process since the mix design is based on empirical methods and its confirmation in the laboratory demands time and resources. In this context, this work aims to evaluate Machine Learning (ML) models to predict the compressive strength of concrete from its constituents. For this purpose, a dataset from the literature was used as input to four ML models: Extreme Gradient Boosting (XGBoost), Support Vector Regression (SVR), Artificial Neural Networks (ANN) and Gaussian Process Regression (GPR). The accuracy of the models was evaluated through 10-fold cross-validation, and quantified by R2, Mean Absolute Error (MAE), and Root-Mean-Square Error (RMSE) metrics. Subsequently, a new dataset was put together with mixtures from the literature and used to validate the previous models. In the model creation step, all algorithms obtained similar and positive results, with MAE between 1.96-2.26 MPa and R2 varying from 0.79 to 0.83. However, in the validation step, the accuracy of the models dropped sharply, with MAE growing to 3.04-4.04 MPa and R2 decreasing to 0.37-0.59. ANN and GPR showed the best results, while SVR had the worst predictions. This work showed that ML tools are promising techniques to predict the compressive strength of concrete. However, care must be taken with the input data to guarantee that models are not overfitted to a given region, set of materials, or type of concrete. |
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Comparison of machine learning techniques to predict the compressive strength of concrete and considerations on model generalizationmachine learningconcrete mix designgeneralization abilitycompressive strengthconcrete database Abstract The compressive strength of concrete is an essential property to ensure the safety of a concrete structure. However, estimating this value is usually a laborious and uncertain process since the mix design is based on empirical methods and its confirmation in the laboratory demands time and resources. In this context, this work aims to evaluate Machine Learning (ML) models to predict the compressive strength of concrete from its constituents. For this purpose, a dataset from the literature was used as input to four ML models: Extreme Gradient Boosting (XGBoost), Support Vector Regression (SVR), Artificial Neural Networks (ANN) and Gaussian Process Regression (GPR). The accuracy of the models was evaluated through 10-fold cross-validation, and quantified by R2, Mean Absolute Error (MAE), and Root-Mean-Square Error (RMSE) metrics. Subsequently, a new dataset was put together with mixtures from the literature and used to validate the previous models. In the model creation step, all algorithms obtained similar and positive results, with MAE between 1.96-2.26 MPa and R2 varying from 0.79 to 0.83. However, in the validation step, the accuracy of the models dropped sharply, with MAE growing to 3.04-4.04 MPa and R2 decreasing to 0.37-0.59. ANN and GPR showed the best results, while SVR had the worst predictions. This work showed that ML tools are promising techniques to predict the compressive strength of concrete. However, care must be taken with the input data to guarantee that models are not overfitted to a given region, set of materials, or type of concrete.IBRACON - Instituto Brasileiro do Concreto2022-01-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S1983-41952022000500200Revista IBRACON de Estruturas e Materiais v.15 n.5 2022reponame:Revista IBRACON de Estruturas e Materiaisinstname:Instituto Brasileiro do Concreto (IBRACON)instacron:IBRACON10.1590/s1983-41952022000500003info:eu-repo/semantics/openAccessPaixão,Rafael Christian Fonseca daPenido,Rúben El-KatibCury,Alexandre AbrahãoMendes,Júlia Castroeng2022-03-07T00:00:00Zoai:scielo:S1983-41952022000500200Revistahttp://www.revistas.ibracon.org.br/index.php/riemhttps://old.scielo.br/oai/scielo-oai.phpeditores.riem@gmail.com||arlene@ibracon.org.br1983-41951983-4195opendoar:2022-03-07T00:00Revista IBRACON de Estruturas e Materiais - Instituto Brasileiro do Concreto (IBRACON)false |
dc.title.none.fl_str_mv |
Comparison of machine learning techniques to predict the compressive strength of concrete and considerations on model generalization |
title |
Comparison of machine learning techniques to predict the compressive strength of concrete and considerations on model generalization |
spellingShingle |
Comparison of machine learning techniques to predict the compressive strength of concrete and considerations on model generalization Paixão,Rafael Christian Fonseca da machine learning concrete mix design generalization ability compressive strength concrete database |
title_short |
Comparison of machine learning techniques to predict the compressive strength of concrete and considerations on model generalization |
title_full |
Comparison of machine learning techniques to predict the compressive strength of concrete and considerations on model generalization |
title_fullStr |
Comparison of machine learning techniques to predict the compressive strength of concrete and considerations on model generalization |
title_full_unstemmed |
Comparison of machine learning techniques to predict the compressive strength of concrete and considerations on model generalization |
title_sort |
Comparison of machine learning techniques to predict the compressive strength of concrete and considerations on model generalization |
author |
Paixão,Rafael Christian Fonseca da |
author_facet |
Paixão,Rafael Christian Fonseca da Penido,Rúben El-Katib Cury,Alexandre Abrahão Mendes,Júlia Castro |
author_role |
author |
author2 |
Penido,Rúben El-Katib Cury,Alexandre Abrahão Mendes,Júlia Castro |
author2_role |
author author author |
dc.contributor.author.fl_str_mv |
Paixão,Rafael Christian Fonseca da Penido,Rúben El-Katib Cury,Alexandre Abrahão Mendes,Júlia Castro |
dc.subject.por.fl_str_mv |
machine learning concrete mix design generalization ability compressive strength concrete database |
topic |
machine learning concrete mix design generalization ability compressive strength concrete database |
description |
Abstract The compressive strength of concrete is an essential property to ensure the safety of a concrete structure. However, estimating this value is usually a laborious and uncertain process since the mix design is based on empirical methods and its confirmation in the laboratory demands time and resources. In this context, this work aims to evaluate Machine Learning (ML) models to predict the compressive strength of concrete from its constituents. For this purpose, a dataset from the literature was used as input to four ML models: Extreme Gradient Boosting (XGBoost), Support Vector Regression (SVR), Artificial Neural Networks (ANN) and Gaussian Process Regression (GPR). The accuracy of the models was evaluated through 10-fold cross-validation, and quantified by R2, Mean Absolute Error (MAE), and Root-Mean-Square Error (RMSE) metrics. Subsequently, a new dataset was put together with mixtures from the literature and used to validate the previous models. In the model creation step, all algorithms obtained similar and positive results, with MAE between 1.96-2.26 MPa and R2 varying from 0.79 to 0.83. However, in the validation step, the accuracy of the models dropped sharply, with MAE growing to 3.04-4.04 MPa and R2 decreasing to 0.37-0.59. ANN and GPR showed the best results, while SVR had the worst predictions. This work showed that ML tools are promising techniques to predict the compressive strength of concrete. However, care must be taken with the input data to guarantee that models are not overfitted to a given region, set of materials, or type of concrete. |
publishDate |
2022 |
dc.date.none.fl_str_mv |
2022-01-01 |
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=S1983-41952022000500200 |
url |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1983-41952022000500200 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
10.1590/s1983-41952022000500003 |
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 |
IBRACON - Instituto Brasileiro do Concreto |
publisher.none.fl_str_mv |
IBRACON - Instituto Brasileiro do Concreto |
dc.source.none.fl_str_mv |
Revista IBRACON de Estruturas e Materiais v.15 n.5 2022 reponame:Revista IBRACON de Estruturas e Materiais instname:Instituto Brasileiro do Concreto (IBRACON) instacron:IBRACON |
instname_str |
Instituto Brasileiro do Concreto (IBRACON) |
instacron_str |
IBRACON |
institution |
IBRACON |
reponame_str |
Revista IBRACON de Estruturas e Materiais |
collection |
Revista IBRACON de Estruturas e Materiais |
repository.name.fl_str_mv |
Revista IBRACON de Estruturas e Materiais - Instituto Brasileiro do Concreto (IBRACON) |
repository.mail.fl_str_mv |
editores.riem@gmail.com||arlene@ibracon.org.br |
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1754193606811320320 |