Failure analysis and control of natural gas pipelines under excavation impact based on machine learning scheme

Third-party excavation operations pose a serious threat to the safe operation of natural gas pipelines, and quantifying the failure conditions of pipelines can effectively identify the hazards of excavation operations. Considering the current lack of test data on the full-size critical conditions of...

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Veröffentlicht in:	The International journal of pressure vessels and piping Jg. 201; S. 104870
Hauptverfasser:	Xu, Duo, Chen, Liqiong, Yu, Chang, Zhang, Sen, Zhao, Xiang, Lai, Xin
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Elsevier Ltd 01.02.2023
Schlagworte:	Data-driven predictive model Impact load Machine learning combinatorial algorithms Quantifying failure risk analysis Safety of natural gas pipelines Machine learning combinatorial algorithms Data-driven predictive model Safety of natural gas pipelines Quantifying failure risk analysis Impact load
ISSN:	0308-0161, 1879-3541
Online-Zugang:	Volltext
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