Residual Strength Prediction of a Pipeline with Interacting Corrosion Defects Subjected to Combined Loading Using Artificial Neural Networks

Prasshanth Perumal; Saravanan Karuppanan; Mark Ovinis

doi:10.1007/978-981-19-1939-8_55

Residual Strength Prediction of a Pipeline with Interacting Corrosion Defects Subjected to Combined Loading Using Artificial Neural Networks

Prasshanth Perumal^*, Saravanan Karuppanan, Mark Ovinis

^*Corresponding author for this work

Universiti Teknologi Petronas

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Original language	English
Title of host publication	ICPER 2020 - Proceedings of the 7th International Conference on Production, Energy and Reliability
Editors	Faiz Ahmad, Hussain H. Al-Kayiem, William Pao King Soon
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	705-719
Number of pages	15
ISBN (Print)	9789811919381
DOIs	https://doi.org/10.1007/978-981-19-1939-8_55
Publication status	Published (VoR) - 2023
Event	7th International Conference on Production, Energy and Reliability, ICPER 2020 - Kuching, Malaysia Duration: 14 Jul 2020 → 16 Jul 2020

Publication series

Name	Lecture Notes in Mechanical Engineering
ISSN (Print)	2195-4356
ISSN (Electronic)	2195-4364

Conference

Conference	7th International Conference on Production, Energy and Reliability, ICPER 2020
Country/Territory	Malaysia
City	Kuching
Period	14/07/20 → 16/07/20

Funding

Acknowledgements This work was supported by Yayasan Universiti Teknologi PETRONAS, Malaysia (Grant number 015LC0-110).

Funders	Funder number
Yayasan UTP	015LC0-110

Keywords

Artificial neural network
Failure pressure
Interacting corrosion defects
Pipeline

Access to Document

10.1007/978-981-19-1939-8_55

Cite this

Perumal, P., Karuppanan, S., & Ovinis, M. (2023). Residual Strength Prediction of a Pipeline with Interacting Corrosion Defects Subjected to Combined Loading Using Artificial Neural Networks. In F. Ahmad, H. H. Al-Kayiem, & W. P. King Soon (Eds.), ICPER 2020 - Proceedings of the 7th International Conference on Production, Energy and Reliability (pp. 705-719). (Lecture Notes in Mechanical Engineering). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-981-19-1939-8_55

Perumal, Prasshanth ; Karuppanan, Saravanan ; Ovinis, Mark. / Residual Strength Prediction of a Pipeline with Interacting Corrosion Defects Subjected to Combined Loading Using Artificial Neural Networks. ICPER 2020 - Proceedings of the 7th International Conference on Production, Energy and Reliability. editor / Faiz Ahmad ; Hussain H. Al-Kayiem ; William Pao King Soon. Springer Science and Business Media Deutschland GmbH, 2023. pp. 705-719 (Lecture Notes in Mechanical Engineering).

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title = "Residual Strength Prediction of a Pipeline with Interacting Corrosion Defects Subjected to Combined Loading Using Artificial Neural Networks",

keywords = "Artificial neural network, Failure pressure, Interacting corrosion defects, Pipeline",

author = "Prasshanth Perumal and Saravanan Karuppanan and Mark Ovinis",

note = "Publisher Copyright: {\textcopyright} 2023, Institute of Technology PETRONAS Sdn Bhd.; 7th International Conference on Production, Energy and Reliability, ICPER 2020 ; Conference date: 14-07-2020 Through 16-07-2020",

year = "2023",

doi = "10.1007/978-981-19-1939-8_55",

language = "English",

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series = "Lecture Notes in Mechanical Engineering",

publisher = "Springer Science and Business Media Deutschland GmbH",

pages = "705--719",

editor = "Faiz Ahmad and Al-Kayiem, {Hussain H.} and {King Soon}, {William Pao}",

booktitle = "ICPER 2020 - Proceedings of the 7th International Conference on Production, Energy and Reliability",

}

Perumal, P, Karuppanan, S & Ovinis, M 2023, Residual Strength Prediction of a Pipeline with Interacting Corrosion Defects Subjected to Combined Loading Using Artificial Neural Networks. in F Ahmad, HH Al-Kayiem & WP King Soon (eds), ICPER 2020 - Proceedings of the 7th International Conference on Production, Energy and Reliability. Lecture Notes in Mechanical Engineering, Springer Science and Business Media Deutschland GmbH, pp. 705-719, 7th International Conference on Production, Energy and Reliability, ICPER 2020, Kuching, Malaysia, 14/07/20. https://doi.org/10.1007/978-981-19-1939-8_55

Residual Strength Prediction of a Pipeline with Interacting Corrosion Defects Subjected to Combined Loading Using Artificial Neural Networks. / Perumal, Prasshanth; Karuppanan, Saravanan; Ovinis, Mark.
ICPER 2020 - Proceedings of the 7th International Conference on Production, Energy and Reliability. ed. / Faiz Ahmad; Hussain H. Al-Kayiem; William Pao King Soon. Springer Science and Business Media Deutschland GmbH, 2023. p. 705-719 (Lecture Notes in Mechanical Engineering).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Perumal, Prasshanth

AU - Karuppanan, Saravanan

AU - Ovinis, Mark

PY - 2023

Y1 - 2023

KW - Artificial neural network

KW - Failure pressure

KW - Interacting corrosion defects

KW - Pipeline

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DO - 10.1007/978-981-19-1939-8_55

M3 - Conference contribution

AN - SCOPUS:85140791285

SN - 9789811919381

T3 - Lecture Notes in Mechanical Engineering

SP - 705

EP - 719

BT - ICPER 2020 - Proceedings of the 7th International Conference on Production, Energy and Reliability

A2 - Ahmad, Faiz

A2 - Al-Kayiem, Hussain H.

A2 - King Soon, William Pao

PB - Springer Science and Business Media Deutschland GmbH

T2 - 7th International Conference on Production, Energy and Reliability, ICPER 2020

Y2 - 14 July 2020 through 16 July 2020

ER -

Perumal P, Karuppanan S, Ovinis M. Residual Strength Prediction of a Pipeline with Interacting Corrosion Defects Subjected to Combined Loading Using Artificial Neural Networks. In Ahmad F, Al-Kayiem HH, King Soon WP, editors, ICPER 2020 - Proceedings of the 7th International Conference on Production, Energy and Reliability. Springer Science and Business Media Deutschland GmbH. 2023. p. 705-719. (Lecture Notes in Mechanical Engineering). doi: 10.1007/978-981-19-1939-8_55

Residual Strength Prediction of a Pipeline with Interacting Corrosion Defects Subjected to Combined Loading Using Artificial Neural Networks

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