YaBeSH Engineering and Technology Library

    • Journals
    • PaperQuest
    • YSE Standards
    • YaBeSH
    • Login
    View Item 
    •   YE&T Library
    • ASCE
    • Journal of Geotechnical and Geoenvironmental Engineering
    • View Item
    •   YE&T Library
    • ASCE
    • Journal of Geotechnical and Geoenvironmental Engineering
    • View Item
    • All Fields
    • Source Title
    • Year
    • Publisher
    • Title
    • Subject
    • Author
    • DOI
    • ISBN
    Advanced Search
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Archive

    Machine Learning–Based Settlement Models for Shallow-Founded Structures on Interbedded Liquefiable Sites Considering Dense Granular Columns

    Source: Journal of Geotechnical and Geoenvironmental Engineering:;2025:;Volume ( 151 ):;issue: 011::page 04025143-1
    Author:
    Bessette, Caroline
    ,
    Dashti, Shideh
    ,
    Liel, Abbie B.
    ,
    Hwang, Yu-Wei
    DOI: 10.1061/JGGEFK.GTENG-13775
    Publisher: American Society of Civil Engineers
    Abstract: AbstractIn this paper, we introduce physics-informed, machine learning (ML)–based predictive models for the permanent settlement of foundations on stratigraphically variable liquefiable deposits. In this paper, we define stratigraphic variability as two-...
    • Download: (3.399Mb)
    • Show Full MetaData Hide Full MetaData
    • Get RIS
    • Item Order
    • Go To Publisher
    • Statistics

      Machine Learning–Based Settlement Models for Shallow-Founded Structures on Interbedded Liquefiable Sites Considering Dense Granular Columns

    URI
    https://yetl.yabesh.ir/yetl1/handle/yetl/4311476
    Collections
    • Journal of Geotechnical and Geoenvironmental Engineering

    Show full item record

    contributor authorBessette, Caroline
    contributor authorDashti, Shideh
    contributor authorLiel, Abbie B.
    contributor authorHwang, Yu-Wei
    date accessioned2026-08-20T10:56:38Z
    date available2026-08-20T10:56:38Z
    date copyright2025/09/15
    date issued2025
    identifier otherJGGEFK.GTENG-13775.pdf
    identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4311476
    description abstractAbstractIn this paper, we introduce physics-informed, machine learning (ML)–based predictive models for the permanent settlement of foundations on stratigraphically variable liquefiable deposits. In this paper, we define stratigraphic variability as two-...
    publisherAmerican Society of Civil Engineers
    titleMachine Learning–Based Settlement Models for Shallow-Founded Structures on Interbedded Liquefiable Sites Considering Dense Granular Columns
    typeJournal Article
    journal volume151
    journal issue11
    journal titleJournal of Geotechnical and Geoenvironmental Engineering
    identifier doi10.1061/JGGEFK.GTENG-13775
    journal fristpage04025143-1
    journal lastpage04025143-22
    page22
    treeJournal of Geotechnical and Geoenvironmental Engineering:;2025:;Volume ( 151 ):;issue: 011
    contenttypeFulltext
    DSpace software copyright © 2002-2015  DuraSpace
    نرم افزار کتابخانه دیجیتال "دی اسپیس" فارسی شده توسط یابش برای کتابخانه های ایرانی | تماس با یابش
    yabeshDSpacePersian
     
    DSpace software copyright © 2002-2015  DuraSpace
    نرم افزار کتابخانه دیجیتال "دی اسپیس" فارسی شده توسط یابش برای کتابخانه های ایرانی | تماس با یابش
    yabeshDSpacePersian