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    Deep Learning for Geotechnical Reliability Analysis with Multiple Uncertainties 

    Source: Journal of Geotechnical and Geoenvironmental Engineering:;2022:;Volume ( 148 ):;issue: 004:;page 06022001
    Author(s): Ze Zhou Wang
    Publisher: ASCE
    Abstract: Apart from spatial variability of soil properties, a geotechnical system can have many other sources of uncertainties. To efficiently analyze such a system in a probabilistic manner, many strategies have been proposed in ...
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    Metamodel-Based Reliability Analysis in Spatially Variable Soils Using Convolutional Neural Networks 

    Source: Journal of Geotechnical and Geoenvironmental Engineering:;2021:;Volume ( 147 ):;issue: 003:;page 04021003-1
    Author(s): Ze Zhou Wang; Changlin Xiao; Siang Huat Goh; Min-Xuan Deng
    Publisher: ASCE
    Abstract: In recent years, the random field finite-element method (FEM) has been used increasingly in geotechnical engineering to carry out analyses that account for the inherent spatial variability in the physical and mechanical ...
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    Deep Learning–Based Prediction of Tunnel Face Stability in Layered Soils Using Images of Random Fields 

    Source: Journal of Geotechnical and Geoenvironmental Engineering:;2024:;Volume ( 150 ):;issue: 008:;page 04024065-1
    Author(s): Zheming Zhang; Ze Zhou Wang; Siang Huat Goh; Jian Ji
    Publisher: American Society of Civil Engineers
    Abstract: The stability analysis of tunnel faces in multilayered soils presents challenges due to the inherent variability in natural soils. Although the random field finite-element methods offer a reliable approach to address such ...
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    DSpace software copyright © 2002-2015  DuraSpace
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