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Use of operational research techniques for concrete mix design: A systematic review
Traditional methods for designing concrete mixtures provide good results; however, they do not guarantee the optimum composition. Consequently, applying operational research techniques is motivated by an increasing need for designers to proportion the concrete's raw materials that satisfy the c...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10161616/ https://www.ncbi.nlm.nih.gov/pubmed/37151679 http://dx.doi.org/10.1016/j.heliyon.2023.e15362 |
_version_ | 1785037530985398272 |
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author | Rosa, Ana Carolina Hammad, Ahmed W.A. Boer, Dieter Haddad, Assed |
author_facet | Rosa, Ana Carolina Hammad, Ahmed W.A. Boer, Dieter Haddad, Assed |
author_sort | Rosa, Ana Carolina |
collection | PubMed |
description | Traditional methods for designing concrete mixtures provide good results; however, they do not guarantee the optimum composition. Consequently, applying operational research techniques is motivated by an increasing need for designers to proportion the concrete's raw materials that satisfy the concrete performance requirements such as mechanical properties, chemical properties, workability, sustainability, and cost. For this reason, many authors have been looking for mathematical programming and machine learning solutions to predict concrete mix properties and optimise concrete mixtures. Therefore, a comprehensive review of operational research techniques concerning the design and proportioning of concrete mixtures and a classification framework are presented herein. |
format | Online Article Text |
id | pubmed-10161616 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
spelling | pubmed-101616162023-05-06 Use of operational research techniques for concrete mix design: A systematic review Rosa, Ana Carolina Hammad, Ahmed W.A. Boer, Dieter Haddad, Assed Heliyon Research Article Traditional methods for designing concrete mixtures provide good results; however, they do not guarantee the optimum composition. Consequently, applying operational research techniques is motivated by an increasing need for designers to proportion the concrete's raw materials that satisfy the concrete performance requirements such as mechanical properties, chemical properties, workability, sustainability, and cost. For this reason, many authors have been looking for mathematical programming and machine learning solutions to predict concrete mix properties and optimise concrete mixtures. Therefore, a comprehensive review of operational research techniques concerning the design and proportioning of concrete mixtures and a classification framework are presented herein. Elsevier 2023-04-11 /pmc/articles/PMC10161616/ /pubmed/37151679 http://dx.doi.org/10.1016/j.heliyon.2023.e15362 Text en © 2023 The Authors https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). |
spellingShingle | Research Article Rosa, Ana Carolina Hammad, Ahmed W.A. Boer, Dieter Haddad, Assed Use of operational research techniques for concrete mix design: A systematic review |
title | Use of operational research techniques for concrete mix design: A systematic review |
title_full | Use of operational research techniques for concrete mix design: A systematic review |
title_fullStr | Use of operational research techniques for concrete mix design: A systematic review |
title_full_unstemmed | Use of operational research techniques for concrete mix design: A systematic review |
title_short | Use of operational research techniques for concrete mix design: A systematic review |
title_sort | use of operational research techniques for concrete mix design: a systematic review |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10161616/ https://www.ncbi.nlm.nih.gov/pubmed/37151679 http://dx.doi.org/10.1016/j.heliyon.2023.e15362 |
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