Pre-bcc: a novel integrated machine learning framework for predicting mechanical and durability properties of blended cement concrete

Partially replacing ordinary Portland cement (OPC) with low-carbon supplementary cementitious materials (SCMs) in blended cement concrete (BCC) is perceived as the most promising route for sustainable concrete production. Despite having a lower environmental impact, BCC could exhibit performance inf...

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Authors: Hafez, H.S., Teirelbar, Ahmed, Kurda, Reben, Tošić, Nikola|||0000-0003-0242-8804, Fuente Antequera, Albert de la|||0000-0002-8016-1677
Format: article
Publication Date:2022
Country:España
Institution:Universitat Politècnica de Catalunya (UPC)
Repository:UPCommons. Portal del coneixement obert de la UPC
Language:English
OAI Identifier:oai:upcommons.upc.edu:2117/372694
Online Access:https://hdl.handle.net/2117/372694
https://dx.doi.org/10.1016/j.conbuildmat.2022.129019
Access Level:Open access
Keyword:Concrete -- Testing
Supplementary cementitious materials
Blended cement concrete
Strength prediction
Durability prediction
Regression model
Formigó -- Proves
Àrees temàtiques de la UPC::Enginyeria civil::Materials i estructures::Materials i estructures de formigó
id ES_8e83403df3199a7da57fe2bcdd20f3ce
oai_identifier_str oai:upcommons.upc.edu:2117/372694
network_acronym_str ES
network_name_str España
repository_id_str
spelling Pre-bcc: a novel integrated machine learning framework for predicting mechanical and durability properties of blended cement concreteHafez, H.S.Teirelbar, AhmedKurda, RebenTošić, Nikola|||0000-0003-0242-8804Fuente Antequera, Albert de la|||0000-0002-8016-1677Concrete -- TestingSupplementary cementitious materialsBlended cement concreteStrength predictionDurability predictionRegression modelFormigó -- ProvesÀrees temàtiques de la UPC::Enginyeria civil::Materials i estructures::Materials i estructures de formigóPartially replacing ordinary Portland cement (OPC) with low-carbon supplementary cementitious materials (SCMs) in blended cement concrete (BCC) is perceived as the most promising route for sustainable concrete production. Despite having a lower environmental impact, BCC could exhibit performance inferior to OPC in design-governing functional properties. Hence, concrete manufacturers and scientists have been seeking methods to predict the performance of BCC mixes in order to reduce the cost and time of experimentally testing all alternatives. Machine learning algorithms have been proven in other fields for treating large amounts of data drawing meaningful relationships between data accurately. However, the existing prediction models in the literature come short in covering a wide range of SCMs and/or functional properties. Considering this, in this study, a non-linear multi-layered machine learning regression model was created to predict the performance of a BCC mix for slump, strength, and resistance to carbonation and chloride ingress based on any of five prominent SCMs: fly ash, ground granulated blast furnace slag, silica fume, lime powder and calcined clay. A database from>150 peer-reviewed sources containing>1650 data points was created to train and test the model. The statistical performance was found to be comparable to that of existing models (R = 0.94–0.97). For the first time, the model, Pre-bcc, was also made available online for users to conduct their own prediction studies.Peer Reviewed20222022-10-0120222022-09-13journal articlehttp://purl.org/coar/resource_type/c_6501VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/2117/372694https://dx.doi.org/10.1016/j.conbuildmat.2022.129019reponame:UPCommons. Portal del coneixement obert de la UPCinstname:Universitat Politècnica de Catalunya (UPC)Inglésengopen accesshttp://purl.org/coar/access_right/c_abf2Attribution 4.0 Internationalhttp://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessoai:upcommons.upc.edu:2117/3726942026-05-27T15:37:01Z
dc.title.none.fl_str_mv Pre-bcc: a novel integrated machine learning framework for predicting mechanical and durability properties of blended cement concrete
title Pre-bcc: a novel integrated machine learning framework for predicting mechanical and durability properties of blended cement concrete
spellingShingle Pre-bcc: a novel integrated machine learning framework for predicting mechanical and durability properties of blended cement concrete
Hafez, H.S.
Concrete -- Testing
Supplementary cementitious materials
Blended cement concrete
Strength prediction
Durability prediction
Regression model
Formigó -- Proves
Àrees temàtiques de la UPC::Enginyeria civil::Materials i estructures::Materials i estructures de formigó
title_short Pre-bcc: a novel integrated machine learning framework for predicting mechanical and durability properties of blended cement concrete
title_full Pre-bcc: a novel integrated machine learning framework for predicting mechanical and durability properties of blended cement concrete
title_fullStr Pre-bcc: a novel integrated machine learning framework for predicting mechanical and durability properties of blended cement concrete
title_full_unstemmed Pre-bcc: a novel integrated machine learning framework for predicting mechanical and durability properties of blended cement concrete
title_sort Pre-bcc: a novel integrated machine learning framework for predicting mechanical and durability properties of blended cement concrete
dc.creator.none.fl_str_mv Hafez, H.S.
Teirelbar, Ahmed
Kurda, Reben
Tošić, Nikola|||0000-0003-0242-8804
Fuente Antequera, Albert de la|||0000-0002-8016-1677
author Hafez, H.S.
author_facet Hafez, H.S.
Teirelbar, Ahmed
Kurda, Reben
Tošić, Nikola|||0000-0003-0242-8804
Fuente Antequera, Albert de la|||0000-0002-8016-1677
author_role author
author2 Teirelbar, Ahmed
Kurda, Reben
Tošić, Nikola|||0000-0003-0242-8804
Fuente Antequera, Albert de la|||0000-0002-8016-1677
author2_role author
author
author
author
dc.subject.none.fl_str_mv Concrete -- Testing
Supplementary cementitious materials
Blended cement concrete
Strength prediction
Durability prediction
Regression model
Formigó -- Proves
Àrees temàtiques de la UPC::Enginyeria civil::Materials i estructures::Materials i estructures de formigó
topic Concrete -- Testing
Supplementary cementitious materials
Blended cement concrete
Strength prediction
Durability prediction
Regression model
Formigó -- Proves
Àrees temàtiques de la UPC::Enginyeria civil::Materials i estructures::Materials i estructures de formigó
description Partially replacing ordinary Portland cement (OPC) with low-carbon supplementary cementitious materials (SCMs) in blended cement concrete (BCC) is perceived as the most promising route for sustainable concrete production. Despite having a lower environmental impact, BCC could exhibit performance inferior to OPC in design-governing functional properties. Hence, concrete manufacturers and scientists have been seeking methods to predict the performance of BCC mixes in order to reduce the cost and time of experimentally testing all alternatives. Machine learning algorithms have been proven in other fields for treating large amounts of data drawing meaningful relationships between data accurately. However, the existing prediction models in the literature come short in covering a wide range of SCMs and/or functional properties. Considering this, in this study, a non-linear multi-layered machine learning regression model was created to predict the performance of a BCC mix for slump, strength, and resistance to carbonation and chloride ingress based on any of five prominent SCMs: fly ash, ground granulated blast furnace slag, silica fume, lime powder and calcined clay. A database from>150 peer-reviewed sources containing>1650 data points was created to train and test the model. The statistical performance was found to be comparable to that of existing models (R = 0.94–0.97). For the first time, the model, Pre-bcc, was also made available online for users to conduct their own prediction studies.
publishDate 2022
dc.date.none.fl_str_mv 2022
2022-10-01
2022
2022-09-13
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
VoR
http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://hdl.handle.net/2117/372694
https://dx.doi.org/10.1016/j.conbuildmat.2022.129019
url https://hdl.handle.net/2117/372694
https://dx.doi.org/10.1016/j.conbuildmat.2022.129019
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
Attribution 4.0 International
http://creativecommons.org/licenses/by/4.0/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
Attribution 4.0 International
http://creativecommons.org/licenses/by/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:UPCommons. Portal del coneixement obert de la UPC
instname:Universitat Politècnica de Catalunya (UPC)
instname_str Universitat Politècnica de Catalunya (UPC)
reponame_str UPCommons. Portal del coneixement obert de la UPC
collection UPCommons. Portal del coneixement obert de la UPC
repository.name.fl_str_mv
repository.mail.fl_str_mv
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