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    Development of an ANN Model for Predicting Optimum Mix Proportion of Fiber-Reinforced Geopolymer Concrete and Assessment of the Model’s Prediction Efficiency

    Source: Journal of Structural Design and Construction Practice:;2026:;Volume ( 031 ):;issue: 003::page 04026044-1
    Author:
    Kadarkarai, Arunkumar
    ,
    Joseph, Karishma
    ,
    Boomibalan, Sankar
    ,
    Vagestan, Prem Kumar
    ,
    Sadasivam, Savitha
    ,
    Rameshkumar, Deivasigamani
    DOI: 10.1061/JSDCCC.SCENG-2032
    Publisher: American Society of Civil Engineers
    Abstract: AbstractThe environmental footprint of ordinary portland cement production is responsible for nearly 8% of global CO2 emissions, which has raised significant concerns in recent decades. In pursuit of sustainable alternatives, geopolymer concrete produced by ...
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      Development of an ANN Model for Predicting Optimum Mix Proportion of Fiber-Reinforced Geopolymer Concrete and Assessment of the Model’s Prediction Efficiency

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    https://yetl.yabesh.ir/yetl1/handle/yetl/4313266
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    • Journal of Structural Design and Construction Practice

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    contributor authorKadarkarai, Arunkumar
    contributor authorJoseph, Karishma
    contributor authorBoomibalan, Sankar
    contributor authorVagestan, Prem Kumar
    contributor authorSadasivam, Savitha
    contributor authorRameshkumar, Deivasigamani
    date accessioned2026-08-20T12:14:24Z
    date available2026-08-20T12:14:24Z
    date copyright2026/03/31
    date issued2026
    identifier otherJSDCCC.SCENG-2032.pdf
    identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4313266
    description abstractAbstractThe environmental footprint of ordinary portland cement production is responsible for nearly 8% of global CO2 emissions, which has raised significant concerns in recent decades. In pursuit of sustainable alternatives, geopolymer concrete produced by ...
    publisherAmerican Society of Civil Engineers
    titleDevelopment of an ANN Model for Predicting Optimum Mix Proportion of Fiber-Reinforced Geopolymer Concrete and Assessment of the Model’s Prediction Efficiency
    typeJournal Article
    journal volume31
    journal issue3
    journal titleJournal of Structural Design and Construction Practice
    identifier doi10.1061/JSDCCC.SCENG-2032
    journal fristpage04026044-1
    journal lastpage04026044-9
    page9
    treeJournal of Structural Design and Construction Practice:;2026:;Volume ( 031 ):;issue: 003
    contenttypeFulltext
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