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

    Bayesian Network Prediction of Stiffness and Shear Strength of Sand

    Source: Journal of Geotechnical and Geoenvironmental Engineering:;2021:;Volume ( 147 ):;issue: 005::page 04021020-1
    Author:
    Man Kong Lo
    ,
    Xiao Wei
    ,
    Siau Chen Chian
    ,
    Taeseo Ku
    DOI: 10.1061/(ASCE)GT.1943-5606.0002505
    Publisher: ASCE
    Abstract: This paper proposes a Bayesian network approach to predict the shear modulus and maximum friction angle of sand. The nonlinear correlations between sand parameters can be incorporated in the probability distribution represented by a Bayesian network. Extensive databases for shear modulus and friction angles of sandy soils are compiled for training the Bayesian network through maximizing the log-likelihood. The trained Bayesian network is applied to a case study in Japan (Yodo River sand). Information from multiple sources (index properties, in situ samples, and modulus logging) can be integrated in a holistic manner to decrease the uncertainty in the prediction of stiffness and shear strength. A Bayesian network also allows the calibration of the global model (model trained from a large global database) by including site-specific samples. In the Yodo River sand case, it is revealed that one to three samples are adequate to reduce the uncertainty of the global model close to the uncertainty of the site-specific model.
    • Download: (2.757Mb)
    • Show Full MetaData Hide Full MetaData
    • Get RIS
    • Item Order
    • Go To Publisher
    • Price: 5000 Rial
    • Statistics

      Bayesian Network Prediction of Stiffness and Shear Strength of Sand

    URI
    http://yetl.yabesh.ir/yetl1/handle/yetl/4271494
    Collections
    • Journal of Geotechnical and Geoenvironmental Engineering

    Show full item record

    contributor authorMan Kong Lo
    contributor authorXiao Wei
    contributor authorSiau Chen Chian
    contributor authorTaeseo Ku
    date accessioned2022-02-01T00:28:43Z
    date available2022-02-01T00:28:43Z
    date issued5/1/2021
    identifier other%28ASCE%29GT.1943-5606.0002505.pdf
    identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4271494
    description abstractThis paper proposes a Bayesian network approach to predict the shear modulus and maximum friction angle of sand. The nonlinear correlations between sand parameters can be incorporated in the probability distribution represented by a Bayesian network. Extensive databases for shear modulus and friction angles of sandy soils are compiled for training the Bayesian network through maximizing the log-likelihood. The trained Bayesian network is applied to a case study in Japan (Yodo River sand). Information from multiple sources (index properties, in situ samples, and modulus logging) can be integrated in a holistic manner to decrease the uncertainty in the prediction of stiffness and shear strength. A Bayesian network also allows the calibration of the global model (model trained from a large global database) by including site-specific samples. In the Yodo River sand case, it is revealed that one to three samples are adequate to reduce the uncertainty of the global model close to the uncertainty of the site-specific model.
    publisherASCE
    titleBayesian Network Prediction of Stiffness and Shear Strength of Sand
    typeJournal Paper
    journal volume147
    journal issue5
    journal titleJournal of Geotechnical and Geoenvironmental Engineering
    identifier doi10.1061/(ASCE)GT.1943-5606.0002505
    journal fristpage04021020-1
    journal lastpage04021020-16
    page16
    treeJournal of Geotechnical and Geoenvironmental Engineering:;2021:;Volume ( 147 ):;issue: 005
    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