Articles producció científicaEnginyeria Mecànica

Use of operational research techniques for concrete mix design: A systematic review

  • Identification data

    Identifier:  imarina:9296449
    Handle: https://hdl.handle.net/20.500.11797/imarina9296449
    Authors:  Rosa, Ana Carolina; Hammad, Ahmed W A; Boer, Dieter; Haddad, Assed
    Abstract:
    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.

  • Others:

    Author, as appears in the article.: Rosa, Ana Carolina; Hammad, Ahmed W A; Boer, Dieter; Haddad, Assed
    Department: Enginyeria Mecànica
    URV's Author/s: Boer, Dieter-Thomas
    Keywords: Optimisation; Mathematical programming; Machine learning; Concrete mix design; Artificial neural-network; sustainable concrete; silica fume; self-consolidating concrete; optimisation; mechanical-properties; mathematical programming; fly-ash; elastic-modulus; concrete mix design; compressive strength prediction; carbonation depth; autogenous shrinkage
    Abstract: 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.
    Thematic Areas: Multidisciplinary sciences; Multidisciplinary; Medicina i; Ciências biológicas ii; Ciências biológicas i; Biotecnología
    licence for use: https://creativecommons.org/licenses/by/3.0/es/
    Author's mail: dieter.boer@urv.cat
    Record's date: 2025-02-24
    Paper version: info:eu-repo/semantics/publishedVersion
    Link to the original source: https://www.sciencedirect.com/science/article/pii/S2405844023025690
    Licence document URL: https://repositori.urv.cat/ca/proteccio-de-dades/
    Paper original source: Heliyon. 9 (4): e15362-
    APA: Rosa, Ana Carolina; Hammad, Ahmed W A; Boer, Dieter; Haddad, Assed (2023). Use of operational research techniques for concrete mix design: A systematic review. Heliyon, 9(4), e15362-. DOI: 10.1016/j.heliyon.2023.e15362
    Article's DOI: 10.1016/j.heliyon.2023.e15362
    Entity: Universitat Rovira i Virgili
    Journal publication year: 2023
    Publication Type: Journal Publications

  • Keywords:

    Multidisciplinary,Multidisciplinary Sciences
    Optimisation
    Mathematical programming
    Machine learning
    Concrete mix design
    Artificial neural-network
    sustainable concrete
    silica fume
    self-consolidating concrete
    optimisation
    mechanical-properties
    mathematical programming
    fly-ash
    elastic-modulus
    concrete mix design
    compressive strength prediction
    carbonation depth
    autogenous shrinkage
    Multidisciplinary sciences
    Multidisciplinary
    Medicina i
    Ciências biológicas ii
    Ciências biológicas i
    Biotecnología

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