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OpenAlex Citation Counts

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OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

Integrity assessment of corroded oil and gas pipelines using machine learning: A systematic review
Afzal Ahmed Soomro, Ainul Akmar Mokhtar, Jundika C. Kurnia, et al.
Engineering Failure Analysis (2021) Vol. 131, pp. 105810-105810
Closed Access | Times Cited: 111

Showing 1-25 of 111 citing articles:

External corrosion of oil and gas pipelines: A review of failure mechanisms and predictive preventions
Muhammad Wasim, Milos B. Djukic
Journal of Natural Gas Science and Engineering (2022) Vol. 100, pp. 104467-104467
Closed Access | Times Cited: 182
Evolution of corrosion prediction models for oil and gas pipelines: From empirical-driven to data-driven
Qinying Wang, Yuhui Song, Xingshou Zhang, et al.
Engineering Failure Analysis (2023) Vol. 146, pp. 107097-107097
Closed Access | Times Cited: 64
Towards the next generation of machine learning models in additive manufacturing: A review of process dependent material evolution
Mohammad Parsazadeh, Shashank Sharma, Narendra B. Dahotre
Progress in Materials Science (2023) Vol. 135, pp. 101102-101102
Closed Access | Times Cited: 64
Leak detection and localization techniques in oil and gas pipeline: A bibliometric and systematic review
Jie Yuan, Wenjing Mao, Chun Hu, et al.
Engineering Failure Analysis (2023) Vol. 146, pp. 107060-107060
Closed Access | Times Cited: 61
Insights into modern machine learning approaches for bearing fault classification: A systematic literature review
Afzal Ahmed Soomro, Masdi Muhammad, Ainul Akmar Mokhtar, et al.
Results in Engineering (2024) Vol. 23, pp. 102700-102700
Open Access | Times Cited: 18
Review of Prediction of Stress Corrosion Cracking in Gas Pipelines Using Machine Learning
Muhammad Nihal Hussain, Tieling Zhang, Muzaffar Chaudhry, et al.
Machines (2024) Vol. 12, Iss. 1, pp. 42-42
Open Access | Times Cited: 17
Addressing Hydrogen Sulfide Corrosion in Oil and Gas Industries: A Sustainable Perspective
Mohammadtaghi Vakili, Petr Koutnı́k, Jan Kohout
Sustainability (2024) Vol. 16, Iss. 4, pp. 1661-1661
Open Access | Times Cited: 17
A systematic resilience assessment framework for multi-state systems based on physics-informed neural network
Yuxuan He, Enrico Zio, Zhaoming Yang, et al.
Reliability Engineering & System Safety (2025), pp. 110866-110866
Closed Access | Times Cited: 7
A KPCA-BRANN based data-driven approach to model corrosion degradation of subsea oil pipelines
Xinhong Li, Ruichao Jia, Renren Zhang, et al.
Reliability Engineering & System Safety (2021) Vol. 219, pp. 108231-108231
Open Access | Times Cited: 65
Advances in corrosion growth modeling for oil and gas pipelines: A review
Haonan Ma, Weidong Zhang, Yao Wang, et al.
Process Safety and Environmental Protection (2022) Vol. 171, pp. 71-86
Closed Access | Times Cited: 63
The research progress and prospect of data mining methods on corrosion prediction of oil and gas pipelines
Lei Xu, Yunfu Wang, Lin Mo, et al.
Engineering Failure Analysis (2022) Vol. 144, pp. 106951-106951
Closed Access | Times Cited: 48
Residual strength prediction of corroded pipelines using multilayer perceptron and modified feedforward neural network
Zhanfeng Chen, Xuyao Li, Wen Wang, et al.
Reliability Engineering & System Safety (2022) Vol. 231, pp. 108980-108980
Closed Access | Times Cited: 46
A Comprehensive Analysis of In-Line Inspection Tools and Technologies for Steel Oil and Gas Pipelines
Berke Ogulcan Parlak, Hüseyin Ayhan Yavaşoğlu
Sustainability (2023) Vol. 15, Iss. 3, pp. 2783-2783
Open Access | Times Cited: 33
Prediction of oil and gas pipeline failures through machine learning approaches: A systematic review
Abdulnaser M. Al-Sabaeei, Hitham Alhussian, Said Jadid Abdulkadir, et al.
Energy Reports (2023) Vol. 10, pp. 1313-1338
Open Access | Times Cited: 31
Development of machine learning algorithms for predicting internal corrosion of crude oil and natural gas pipelines
Jian Fang, Xiao Cheng, Huilong Gai, et al.
Computers & Chemical Engineering (2023) Vol. 177, pp. 108358-108358
Closed Access | Times Cited: 27
Understanding the factors and consequences of pipeline incidents: An analysis of gas transmission pipelines in the US
Rui Xiao, Tarek Zayed, Mohamed A. Meguid, et al.
Engineering Failure Analysis (2023) Vol. 152, pp. 107498-107498
Closed Access | Times Cited: 23
Stress corrosion cracking failure analysis of AISI 1018 carbon steel reinforcing bars in carbonated and chloride contaminated environment
Ulises Martin, David M. Bastidas
Engineering Failure Analysis (2023) Vol. 147, pp. 107159-107159
Closed Access | Times Cited: 22
A comparative study of YOLOv5 and YOLOv8 for corrosion segmentation tasks in metal surfaces
Edmundo Casas, Leo Ramos, Cristian Romero, et al.
Array (2024) Vol. 22, pp. 100351-100351
Open Access | Times Cited: 10
Predictive modeling for gas transmission pipeline failure cause and consequence analysis
Rui Xiao, Chunhua Xiong
Process Safety and Environmental Protection (2025), pp. 106812-106812
Closed Access | Times Cited: 1
A review on Bayesian modeling approach to quantify failure risk assessment of oil and gas pipelines due to corrosion
Afzal Ahmed Soomro, Ainul Akmar Mokhtar, Jundika C. Kurnia, et al.
International Journal of Pressure Vessels and Piping (2022) Vol. 200, pp. 104841-104841
Closed Access | Times Cited: 37
Recent Advances in Nondestructive Method and Assessment of Corrosion Undercoating in Carbon–Steel Pipelines
Zazilah May, M. K. Alam, Nazrul Anuar Nayan
Sensors (2022) Vol. 22, Iss. 17, pp. 6654-6654
Open Access | Times Cited: 33
Investigation of fault detection and isolation accuracy of different Machine learning techniques with different data processing methods for gas turbine
Waleligne Molla Salilew, Zainal Ambri Abdul Karim, ‪Tamiru Alemu Lemma
Alexandria Engineering Journal (2022) Vol. 61, Iss. 12, pp. 12635-12651
Closed Access | Times Cited: 29
Efficient prediction method of triple failure pressure for corroded pipelines under complex loads based on a backpropagation neural network
Tieyao Zhang, Jian Shuai, Yi Shuai, et al.
Reliability Engineering & System Safety (2022) Vol. 231, pp. 108990-108990
Closed Access | Times Cited: 29
Improving failure modeling for gas transmission pipelines: A survival analysis and machine learning integrated approach
Rui Xiao, Tarek Zayed, Mohamed A. Meguid, et al.
Reliability Engineering & System Safety (2023) Vol. 241, pp. 109672-109672
Closed Access | Times Cited: 18
Recent Advances in Carbon Dioxide Sequestration in Deep Unmineable Coal Seams Using CO2-ECBM Technology: Experimental Studies, Simulation, and Field Applications
Grant Charles Mwakipunda, Yuting Wang, Melckzedeck Michael Mgimba, et al.
Energy & Fuels (2023) Vol. 37, Iss. 22, pp. 17161-17186
Closed Access | Times Cited: 17
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