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

dc.contributor.authorWang, Hexian
dc.contributor.authorGuo, Lingyun
dc.contributor.authorChen, Bo
dc.contributor.authorNiffenegger, Markus
dc.date.accessioned2026-08-20T12:05:21Z
dc.date.available2026-08-20T12:05:21Z
dc.date.copyright2025/09/24
dc.date.issued2026
dc.identifier.otherJPSEA2.PSENG-1886.pdf
dc.identifier.urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4313082
dc.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 ...
dc.publisherAmerican Society of Civil Engineers
dc.titleNovel Intelligent Deep Learning Model for Predicting the Burst Pressure of Pipelines with Outer Axial Surface Cracks
dc.typeJournal Article
dc.journal.volume17
dc.journal.issue1
dc.journal.titleJournal of Pipeline Systems Engineering and Practice
dc.identifier.doi10.1061/JPSEA2.PSENG-1886
dc.journal.fristpage04025084-1
dc.journal.lastpage04025084-13
dc.page13
dc.treeJournal of Pipeline Systems Engineering and Practice:;2026:;Volume ( 017 ):;issue: 001
dc.contenttypeFulltext


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