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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:

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

Showing 1-25 of 31 citing articles:

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
Enhancing predictive maintenance strategies for oil and gas equipment through ensemble learning modeling
Mei Wang, Xuesong Su, Huifang Song, et al.
Journal of Petroleum Exploration and Production Technology (2025) Vol. 15, Iss. 3
Open Access | Times Cited: 1
Machine and Deep Learning Methods for Concrete Strength Prediction: A Bibliometric and Content Analysis Review of Research Trends and Future Directions
Raman Kumar, Essam Althaqafi, S. Gopal Krishna Patro, et al.
Applied Soft Computing (2024) Vol. 164, pp. 111956-111956
Closed Access | Times Cited: 10
Enhanced Prediction of Corrosion Rates of pipeline Steels Using Simulated Annealing-Optimized ANFIS Models
Ali Hussein Khalaf, Bing Lin, Ahmed N. Abdalla, et al.
Results in Engineering (2024), pp. 102853-102853
Open Access | Times Cited: 7
An Integrated Deep Learning Model for Intelligent Recognition of Long-distance Natural Gas Pipeline Features
Lin Wang, Wannian Guo, Junyu Guo, et al.
Reliability Engineering & System Safety (2024), pp. 110664-110664
Closed Access | Times Cited: 7
Evolution from near-neutral to high-pH environments susceptible to stress corrosion cracking: The role of sulfate and bicarbonate
Shidong Wang, Hamed Shirazi, Guijiang Diao, et al.
Corrosion Science (2024) Vol. 231, pp. 112000-112000
Closed Access | Times Cited: 5
Application of machine learning to leakage detection of fluid pipelines in recent years: A review and prospect
Jianwu Chen, Xiao Wu, Zhibo Jiang, et al.
Measurement (2025), pp. 116857-116857
Closed Access
AI-Driven Framework for Predicting Oil Pipeline Failure Causes Based on Leak Properties and Financial Impact
Tanzina Afrin, Nita Yodo, Ying Huang
Journal of Pipeline Systems Engineering and Practice (2025) Vol. 16, Iss. 2
Closed Access
An interpretable machine learning model for failure pressure prediction of blended hydrogen natural gas pipelines containing a crack-in-dent defect
Guojin Qin, Chao Zhang, Bohong Wang, et al.
Energy (2025), pp. 135401-135401
Closed Access
Analysis of the Permittivity of the Polyethylene Coating of Steel Pipes: Atomistic Simulation
P. A. Mukhametova, D. D. Ozhgibesova, Julia A. Baimova
Journal of Structural Chemistry (2025) Vol. 66, Iss. 1, pp. 78-86
Closed Access
Study on the effect of wear and accuracy correction of the detecting arm of leaf spring caliper
Hao Wang, Lei Ding, Jing Zhou, et al.
Proceedings of the Institution of Mechanical Engineers Part J Journal of Engineering Tribology (2025)
Closed Access
Integrating FT-ICR MS and Machine Learning to Forecast Acid Content Across Boiling Cuts
Jussara V. Roque, Wilson Cardoso, Deborah V. A. de Aguiar, et al.
Analytical Chemistry (2025)
Open Access
A New Extensible Feature Matching Model for Corrosion Defects Based on Consecutive In-Line Inspections and Data Clustering
Mohamad Shatnawi, Péter Földesi
Applied Sciences (2025) Vol. 15, Iss. 6, pp. 2943-2943
Open Access
Industrial Piping System: Design and Corrosion Protection
David Coverdale Rangel Velasco, Victor Paes Dias Gonçalves, Michel Picanço Oliveira, et al.
Surfaces (2025) Vol. 8, Iss. 1, pp. 18-18
Open Access
Pipeline failure evaluation and prediction using failure probability and neural network based on measured data
Hassan Noroznia, Majid Gandomkar, Javad Nikoukar
Heliyon (2024) Vol. 10, Iss. 5, pp. e26837-e26837
Open Access | Times Cited: 3
Energy-efficient routing protocol for reliable low-latency Internet of Things in oil and gas pipeline monitoring
Sana Nasim Karam, Kashif Bilal, Abdul Nasir Khan, et al.
PeerJ Computer Science (2024) Vol. 10, pp. e1908-e1908
Open Access | Times Cited: 3
Integrated Risk Assessment of Mountainous Long-Distance Oil and Gas Pipelines Based on Multisource Spatial Data
Benji Wang, Zheng Li, Baikang Zhu, et al.
ACS Omega (2024) Vol. 9, Iss. 28, pp. 30492-30507
Open Access | Times Cited: 3
Machine Learning to Assess and Support Safe Drinking Water Supply: A Systematic Review
Feng Feng, Yuanxun Zhang, Zhenru Chen, et al.
Journal of environmental chemical engineering (2024), pp. 114481-114481
Closed Access | Times Cited: 3
Study on the Wear Performance and Wear Prediction of Leaf Spring Calipers under Lubricating Medium Conditions
Hao Wang, Lei Ding, Chengfei Zhao, et al.
Lubricants (2024) Vol. 12, Iss. 2, pp. 41-41
Open Access | Times Cited: 2
New Model for Predicting Production Capacity of Horizontal Well Volume Fracturing in Tight Reservoirs
Zhang Lei, Daihong Gu
ACS Omega (2024) Vol. 9, Iss. 10, pp. 11806-11819
Open Access | Times Cited: 2
Advanced Industrial Fault Detection: A Comparative Analysis of Ultrasonic Signal Processing and Ensemble Machine Learning Techniques
Amirhossein Moshrefi, Frédéric Nabki
Applied Sciences (2024) Vol. 14, Iss. 15, pp. 6397-6397
Open Access | Times Cited: 2
Potential application of generative artificial intelligence and machine learning algorithm in oil and gas sector: Benefits and future prospects
Edward Ochieng, Diana Ominde, Tarila Zuofa
Technology in Society (2024), pp. 102710-102710
Closed Access | Times Cited: 2
Prediction Model for Pipeline Pitting Corrosion Based on Multiple Feature Selection and Residual Correction
Zhenhao Zhu, Qiushuang Zheng, Hongbing Liu, et al.
Journal of Marine Science and Application (2024)
Closed Access | Times Cited: 2
A Discrimination method of three types of defects in insulated cladding components based on the lift-off effect of capacitive imaging (CI) technology
Zhen Li, Xiaokang Yin, Jianqing Han, et al.
Sensors and Actuators A Physical (2024) Vol. 371, pp. 115293-115293
Closed Access | Times Cited: 1
Application of Machine Learning Approaches to Prediction of Corrosion Defects in Energy Pipelines
Muhammad Nihal Hussain, Tieling Zhang, Ishrat Jamil, et al.
Engineering materials (2024), pp. 127-166
Closed Access | Times Cited: 1
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