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    Novel Intelligent Deep Learning Model for Predicting the Burst Pressure of Pipelines with Outer Axial Surface Cracks

    Source: Journal of Pipeline Systems Engineering and Practice:;2026:;Volume ( 017 ):;issue: 001::page 04025084-1
    Author:
    Wang, Hexian
    ,
    Guo, Lingyun
    ,
    Chen, Bo
    ,
    Niffenegger, Markus
    DOI: 10.1061/JPSEA2.PSENG-1886
    Publisher: American Society of Civil Engineers
    Abstract: AbstractThis paper proposes a novel pipeline burst pressure prediction model, transformer–bidirectional long short-term memory (BiLSTM)–Gaussian noise (GN) model (TBGN), to address the complex nonlinear coupling of elastic-plastic deformation and fracture ...
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      Novel Intelligent Deep Learning Model for Predicting the Burst Pressure of Pipelines with Outer Axial Surface Cracks

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    https://yetl.yabesh.ir/yetl1/handle/yetl/4313082
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    contributor authorWang, Hexian
    contributor authorGuo, Lingyun
    contributor authorChen, Bo
    contributor authorNiffenegger, Markus
    date accessioned2026-08-20T12:05:21Z
    date available2026-08-20T12:05:21Z
    date copyright2025/09/24
    date issued2026
    identifier otherJPSEA2.PSENG-1886.pdf
    identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4313082
    description abstractAbstractThis paper proposes a novel pipeline burst pressure prediction model, transformer–bidirectional long short-term memory (BiLSTM)–Gaussian noise (GN) model (TBGN), to address the complex nonlinear coupling of elastic-plastic deformation and fracture ...
    publisherAmerican Society of Civil Engineers
    titleNovel Intelligent Deep Learning Model for Predicting the Burst Pressure of Pipelines with Outer Axial Surface Cracks
    typeJournal Article
    journal volume17
    journal issue1
    journal titleJournal of Pipeline Systems Engineering and Practice
    identifier doi10.1061/JPSEA2.PSENG-1886
    journal fristpage04025084-1
    journal lastpage04025084-13
    page13
    treeJournal of Pipeline Systems Engineering and Practice:;2026:;Volume ( 017 ):;issue: 001
    contenttypeFulltext
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