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    Optimized Generative Modeling and Interpretable Machine Learning for Predicting the Compressive Strength of High-Performance Concrete

    Source: Journal of Materials in Civil Engineering:;2026:;Volume ( 038 ):;issue: 007::page 04026168-1
    Author:
    Wang, Yufei
    ,
    Sun, Junbo
    ,
    Zou, Zefeng
    ,
    Wang, Xiangyu
    ,
    Li, Shengping
    ,
    Zhao, Hongyu
    ,
    Shang, Jiajie
    DOI: 10.1061/JMCEE7.MTENG-22159
    Publisher: American Society of Civil Engineers
    Abstract: AbstractThis study provides a comprehensive analysis of ultrahigh-performance concrete (UHPC) compressive strength, focusing on data augmentation, predictive modeling, and model interpretability. The research utilized 808 experimental data points with 16 ...
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      Optimized Generative Modeling and Interpretable Machine Learning for Predicting the Compressive Strength of High-Performance Concrete

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    https://yetl.yabesh.ir/yetl1/handle/yetl/4312654
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    contributor authorWang, Yufei
    contributor authorSun, Junbo
    contributor authorZou, Zefeng
    contributor authorWang, Xiangyu
    contributor authorLi, Shengping
    contributor authorZhao, Hongyu
    contributor authorShang, Jiajie
    date accessioned2026-08-20T11:46:38Z
    date available2026-08-20T11:46:38Z
    date copyright2026/04/19
    date issued2026
    identifier otherJMCEE7.MTENG-22159.pdf
    identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4312654
    description abstractAbstractThis study provides a comprehensive analysis of ultrahigh-performance concrete (UHPC) compressive strength, focusing on data augmentation, predictive modeling, and model interpretability. The research utilized 808 experimental data points with 16 ...
    publisherAmerican Society of Civil Engineers
    titleOptimized Generative Modeling and Interpretable Machine Learning for Predicting the Compressive Strength of High-Performance Concrete
    typeJournal Article
    journal volume38
    journal issue7
    journal titleJournal of Materials in Civil Engineering
    identifier doi10.1061/JMCEE7.MTENG-22159
    journal fristpage04026168-1
    journal lastpage04026168-16
    page16
    treeJournal of Materials in Civil Engineering:;2026:;Volume ( 038 ):;issue: 007
    contenttypeFulltext
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