A novel evolutionary learning to prepare sustainable concrete mixtures with supplementary cementitious materials

Naseri, H; Hosseini, P; Jahanbakhsh, H; Hosseini, P; Gandomi, AH

A novel evolutionary learning to prepare sustainable concrete mixtures with supplementary cementitious materials

Naseri, H Hosseini, P Jahanbakhsh, H Hosseini, P Gandomi, AH

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Publisher:: SPRINGER
Publication Type:: Journal Article
Citation:: Environment, Development and Sustainability, 2022
Issue Date:: 2022-01-01

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Field	Value	Language
dc.contributor.author	Naseri, H
dc.contributor.author	Hosseini, P
dc.contributor.author	Jahanbakhsh, H
dc.contributor.author	Hosseini, P
dc.contributor.author	Gandomi, AH
dc.date.accessioned	2023-03-20T22:29:20Z
dc.date.available	2023-03-20T22:29:20Z
dc.date.issued	2022-01-01
dc.identifier.citation	Environment, Development and Sustainability, 2022
dc.identifier.issn	1387-585X
dc.identifier.issn	1573-2975
dc.identifier.uri	http://hdl.handle.net/10453/167810
dc.description.abstract	Abstract: In this study, sustainable mixture designs of three concrete types, including fly ash concrete, silica fume concrete, and ground granulated blast furnace slag concrete, were investigated. To this end, the compressive strength formulas of each concrete type made with supplementary cementitious materials were obtained by introducing a new machine learning algorithm, called coyote optimization programming. The accuracy of this algorithm proved to be greater than that of conventional and recently developed machine learning methods. An optimization problem is modeled, in which the compressive strengths, price, and environmental impact of the sustainable concrete mixture designs were estimated using global warming potential, energy consumption, and material consumption as the sustainability parameters. Results reveal that increasing the compressive strength reduces the sustainability of concrete, and thus, manufacturing concrete with a higher compressive strength than the one obtained from the design process contradicts the concrete’s performance. Moreover, the 30-MPa sustainable fly ash concrete was proven to be the most sustainable mix with a gray relational grade of 1. This optimal mixture designed in this study can decrease the unit cost, global warming potential, energy consumption, and material consumption by 36.6%, 51%, 43%, and 11%, respectively. Research highlights: A novel machine learning method called coyote optimization programming was introduced in this study.Applying optimization techniques to design concrete mixture proportions can reduce the unit cost by 36.6%.The introduced approach can decrease the global warming potential, energy consumption, and material consumption by 51%, 43%, and 11%, respectively.The application of supplementary cementitious materials in the concrete mixtures significantly enhances sustainability.
dc.language	English
dc.publisher	SPRINGER
dc.relation.ispartof	Environment, Development and Sustainability
dc.relation.isbasedon	10.1007/s10668-022-02283-w
dc.rights	info:eu-repo/semantics/openAccess
dc.rights	This version of the article has been accepted for publication, after peer review (when applicable) and is subject to Springer Nature’s AM terms of use, but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: https://doi.org/10.1007/s10668-022-02283-w
dc.subject	0502 Environmental Science and Management, 0701 Agriculture, Land and Farm Management, 1604 Human Geography
dc.subject.classification	Ecology
dc.title	A novel evolutionary learning to prepare sustainable concrete mixtures with supplementary cementitious materials
dc.type	Journal Article
utslib.for	0502 Environmental Science and Management
utslib.for	0701 Agriculture, Land and Farm Management
utslib.for	1604 Human Geography
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
utslib.copyright.status	open_access	*
dc.date.updated	2023-03-20T22:29:19Z
pubs.publication-status	Published

Abstract:

Abstract: In this study, sustainable mixture designs of three concrete types, including fly ash concrete, silica fume concrete, and ground granulated blast furnace slag concrete, were investigated. To this end, the compressive strength formulas of each concrete type made with supplementary cementitious materials were obtained by introducing a new machine learning algorithm, called coyote optimization programming. The accuracy of this algorithm proved to be greater than that of conventional and recently developed machine learning methods. An optimization problem is modeled, in which the compressive strengths, price, and environmental impact of the sustainable concrete mixture designs were estimated using global warming potential, energy consumption, and material consumption as the sustainability parameters. Results reveal that increasing the compressive strength reduces the sustainability of concrete, and thus, manufacturing concrete with a higher compressive strength than the one obtained from the design process contradicts the concrete’s performance. Moreover, the 30-MPa sustainable fly ash concrete was proven to be the most sustainable mix with a gray relational grade of 1. This optimal mixture designed in this study can decrease the unit cost, global warming potential, energy consumption, and material consumption by 36.6%, 51%, 43%, and 11%, respectively. Research highlights: A novel machine learning method called coyote optimization programming was introduced in this study.Applying optimization techniques to design concrete mixture proportions can reduce the unit cost by 36.6%.The introduced approach can decrease the global warming potential, energy consumption, and material consumption by 51%, 43%, and 11%, respectively.The application of supplementary cementitious materials in the concrete mixtures significantly enhances sustainability.

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http://hdl.handle.net/10453/167810