Enhancing pipeline integrity: a comprehensive review of deep learning-enabled finite element analysis for stress corrosion cracking prediction-Reference-Cited by-同舟云学术

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Enhancing pipeline integrity: a comprehensive review of deep learning-enabled finite element analysis for stress corrosion cracking prediction

Published:2024-01-23 Issue:1 Volume:18 Page:
ISSN:1994-2060
Container-title:Engineering Applications of Computational Fluid Mechanics
language:en
Short-container-title:Engineering Applications of Computational Fluid Mechanics

Author:

Sarwar Umair¹²^ORCID,Mokhtar Ainul Akmar¹^ORCID,Muhammad Masdi¹,Wassan Rano Khan²^ORCID,Soomro Afzal Ahmed¹,Wassan Majid Ali³,Kaka Shuaib²

Affiliation:

1. Department of Mechanical Engineering, Universiti Teknologi Petronas, Seri Iskandar, Malaysia

2. Department of Industrial Engineering and Management, Dawood University of Engineering and Technology, Karachi, Pakistan

3. Department of Mechanical Engineering, Massachusetts Lowell, Lowell, USA

Funder

Yayasan UTP

Publisher

Informa UK Limited

Link

https://www.tandfonline.com/doi/pdf/10.1080/19942060.2024.2302906

Reference121 articles.

1. A Review of Factors Affecting SCC Initiation and Propagation in Pipeline Carbon Steels

2. Ahmad, Z. (2006). Principles of corrosion engineering and corrosion control. Elsevier.

3. Deep learning modelling techniques: current progress, applications, advantages, and challenges

4. Predictive deep learning for pitting corrosion modeling in buried transmission pipelines

5. Application of machine learning to stress corrosion cracking risk assessment

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1. An interpretable spacecraft flywheel system health status assessment method under perturbation;Measurement Science and Technology;2024-06-26

2. A mechanistic deterioration point assignment model for water pipe condition assessment;AQUA — Water Infrastructure, Ecosystems and Society;2024-05-01

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