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    Prediction of the Sulfate Attack Resistance of Concrete Based on Machine-Learning Algorithms

    Source: Journal of Computing in Civil Engineering:;2024:;Volume ( 038 ):;issue: 006::page 04024043-1
    Author:
    Yue Li
    ,
    Junjie Shi
    ,
    Jiale Shen
    ,
    Kaikai Jin
    ,
    Mengtian Fan
    ,
    Xiaolong Liu
    DOI: 10.1061/JCCEE5.CPENG-6064
    Publisher: American Society of Civil Engineers
    Abstract: The thorough investigation into the evolution of concrete performance under sulfate attack environments holds significant importance for engineering applications in specific conditions. In this paper, a prediction model for the two evaluation indexes of sulfate attack resistance of concrete (SARC), namely compressive strength corrosion resistance coefficient and mass loss rate, is established based on four machine-learning algorithms: Support Vector Regression, Random Forest Regression, Gradient Boosting, and Extreme Gradient Boosting (XGB). A comparison of the various performances showed that the model based on the XGB algorithm had the strongest generalization ability and offered the best prediction of SARC (K test set R2=0.963, MLR test set R2=0.903). Feature importance and partial correlation analyses were performed for the two XGB models separately, and a graphical user interface was designed based on the two predictive models. The results reveal that the number of cycles, water-binder ratio, and cement content significantly influence the SARC. Moderately increasing cement, fly ash, and coarse aggregate content can enhance the SARC. Increasing the number of cycles, drying time, water-binder ratio, sand, and solution concentration will reduce the SARC. Therefore, measures such as moderately increasing the amount of cement, reducing the water-binder ratio, and increasing the fly ash content can be increased to improve the SARC, but overuse has no significant effect.
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      Prediction of the Sulfate Attack Resistance of Concrete Based on Machine-Learning Algorithms

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    contributor authorYue Li
    contributor authorJunjie Shi
    contributor authorJiale Shen
    contributor authorKaikai Jin
    contributor authorMengtian Fan
    contributor authorXiaolong Liu
    date accessioned2025-04-20T09:58:02Z
    date available2025-04-20T09:58:02Z
    date copyright9/14/2024 12:00:00 AM
    date issued2024
    identifier otherJCCEE5.CPENG-6064.pdf
    identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4303745
    description abstractThe thorough investigation into the evolution of concrete performance under sulfate attack environments holds significant importance for engineering applications in specific conditions. In this paper, a prediction model for the two evaluation indexes of sulfate attack resistance of concrete (SARC), namely compressive strength corrosion resistance coefficient and mass loss rate, is established based on four machine-learning algorithms: Support Vector Regression, Random Forest Regression, Gradient Boosting, and Extreme Gradient Boosting (XGB). A comparison of the various performances showed that the model based on the XGB algorithm had the strongest generalization ability and offered the best prediction of SARC (K test set R2=0.963, MLR test set R2=0.903). Feature importance and partial correlation analyses were performed for the two XGB models separately, and a graphical user interface was designed based on the two predictive models. The results reveal that the number of cycles, water-binder ratio, and cement content significantly influence the SARC. Moderately increasing cement, fly ash, and coarse aggregate content can enhance the SARC. Increasing the number of cycles, drying time, water-binder ratio, sand, and solution concentration will reduce the SARC. Therefore, measures such as moderately increasing the amount of cement, reducing the water-binder ratio, and increasing the fly ash content can be increased to improve the SARC, but overuse has no significant effect.
    publisherAmerican Society of Civil Engineers
    titlePrediction of the Sulfate Attack Resistance of Concrete Based on Machine-Learning Algorithms
    typeJournal Article
    journal volume38
    journal issue6
    journal titleJournal of Computing in Civil Engineering
    identifier doi10.1061/JCCEE5.CPENG-6064
    journal fristpage04024043-1
    journal lastpage04024043-16
    page16
    treeJournal of Computing in Civil Engineering:;2024:;Volume ( 038 ):;issue: 006
    contenttypeFulltext
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