Empirical Failure Pressure Prediction Equations for Pipelines with Longitudinal Interacting Corrosion Defects Based on Artificial Neural Network

Suria Devi Vijaya Kumar; Michael Lo; Saravanan Karuppanan; Mark Ovinis

doi:10.3390/jmse10060764

Empirical Failure Pressure Prediction Equations for Pipelines with Longitudinal Interacting Corrosion Defects Based on Artificial Neural Network

Suria Devi Vijaya Kumar^*, Michael Lo, Saravanan Karuppanan, Mark Ovinis

^*Corresponding author for this work

Universiti Teknologi Petronas

Research output: Contribution to journal › Article › peer-review

9 Citations (SciVal)

Original language	English
Article number	764
Journal	Journal of Marine Science and Engineering
Volume	10
Issue number	6
DOIs	https://doi.org/10.3390/jmse10060764
Publication status	Published (VoR) - Jun 2022

Funding

Funding: This work was supported by Yayasan Universiti Teknologi PETRONAS, Malaysia (015LC0-110) and the Ministry of Higher Education, Malaysia (FRGS/1/2018/TK03/UTP/02/1).

Funders	Funder number
Ministry of Higher Education, Malaysia	FRGS/1/2018/TK03/UTP/02/1
Yayasan UTP	015LC0-110

Keywords

artificial neural network
failure pressure prediction
pipeline corrosion

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10.3390/jmse10060764

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title = "Empirical Failure Pressure Prediction Equations for Pipelines with Longitudinal Interacting Corrosion Defects Based on Artificial Neural Network",

keywords = "artificial neural network, failure pressure prediction, pipeline corrosion",

author = "{Vijaya Kumar}, {Suria Devi} and Michael Lo and Saravanan Karuppanan and Mark Ovinis",

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year = "2022",

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doi = "10.3390/jmse10060764",

language = "English",

volume = "10",

journal = "Journal of Marine Science and Engineering",

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T1 - Empirical Failure Pressure Prediction Equations for Pipelines with Longitudinal Interacting Corrosion Defects Based on Artificial Neural Network

AU - Vijaya Kumar, Suria Devi

AU - Lo, Michael

AU - Karuppanan, Saravanan

AU - Ovinis, Mark

PY - 2022/6

Y1 - 2022/6

KW - artificial neural network

KW - failure pressure prediction

KW - pipeline corrosion

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U2 - 10.3390/jmse10060764

DO - 10.3390/jmse10060764

M3 - Article

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SN - 2077-1312

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JO - Journal of Marine Science and Engineering

JF - Journal of Marine Science and Engineering

IS - 6

M1 - 764

ER -

Empirical Failure Pressure Prediction Equations for Pipelines with Longitudinal Interacting Corrosion Defects Based on Artificial Neural Network

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Keywords

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