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    A Machine Learning–Based Predictive Model for Liquefaction Ejecta Manifestation in Stratigraphically Variable Sites

    Source: Journal of Geotechnical and Geoenvironmental Engineering:;2026:;Volume ( 152 ):;issue: 001::page 04025167-1
    Author:
    Brito, Lianne
    ,
    Dashti, Shideh
    ,
    Liel, Abbie B.
    ,
    Wham, Brad P.
    DOI: 10.1061/JGGEFK.GTENG-13770
    Publisher: American Society of Civil Engineers
    Abstract: AbstractThis paper presents a machine learning–based probabilistic predictive model for estimating the potential severity of free-field surface ejecta in stratigraphically variable and interbedded deposits as a measure of liquefaction manifestation and ...
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      A Machine Learning–Based Predictive Model for Liquefaction Ejecta Manifestation in Stratigraphically Variable Sites

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    https://yetl.yabesh.ir/yetl1/handle/yetl/4311473
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    • Journal of Geotechnical and Geoenvironmental Engineering

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    contributor authorBrito, Lianne
    contributor authorDashti, Shideh
    contributor authorLiel, Abbie B.
    contributor authorWham, Brad P.
    date accessioned2026-08-20T10:56:32Z
    date available2026-08-20T10:56:32Z
    date copyright2025/10/29
    date issued2026
    identifier otherJGGEFK.GTENG-13770.pdf
    identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4311473
    description abstractAbstractThis paper presents a machine learning–based probabilistic predictive model for estimating the potential severity of free-field surface ejecta in stratigraphically variable and interbedded deposits as a measure of liquefaction manifestation and ...
    publisherAmerican Society of Civil Engineers
    titleA Machine Learning–Based Predictive Model for Liquefaction Ejecta Manifestation in Stratigraphically Variable Sites
    typeJournal Article
    journal volume152
    journal issue1
    journal titleJournal of Geotechnical and Geoenvironmental Engineering
    identifier doi10.1061/JGGEFK.GTENG-13770
    journal fristpage04025167-1
    journal lastpage04025167-17
    page17
    treeJournal of Geotechnical and Geoenvironmental Engineering:;2026:;Volume ( 152 ):;issue: 001
    contenttypeFulltext
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    DSpace software copyright © 2002-2015  DuraSpace
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