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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:

An ensemble model based on relevance vector machine and multi-objective salp swarm algorithm for predicting burst pressure of corroded pipelines
Hongfang Lü, Tom Iseley, John C. Matthews, et al.
Journal of Petroleum Science and Engineering (2021) Vol. 203, pp. 108585-108585
Closed Access | Times Cited: 47

Showing 1-25 of 47 citing articles:

Review on automated condition assessment of pipelines with machine learning
Yiming Liu, Yi Bao
Advanced Engineering Informatics (2022) Vol. 53, pp. 101687-101687
Closed Access | Times Cited: 104
Ultrasonic guided wave techniques and applications in pipeline defect detection: A review
Xulei Zang, Zhao‐Dong Xu, Hongfang Lü, et al.
International Journal of Pressure Vessels and Piping (2023) Vol. 206, pp. 105033-105033
Closed Access | Times Cited: 79
Prediction and multi-objective optimization of mechanical, economical, and environmental properties for strain-hardening cementitious composites (SHCC) based on automated machine learning and metaheuristic algorithms
Soroush Mahjoubi, Rojyar Barhemat, Pengwei Guo, et al.
Journal of Cleaner Production (2021) Vol. 329, pp. 129665-129665
Closed Access | Times Cited: 77
Novel Data-Driven Framework for Predicting Residual Strength of Corroded Pipelines
Hongfang Lü, Zhao‐Dong Xu, Tom Iseley, et al.
Journal of Pipeline Systems Engineering and Practice (2021) Vol. 12, Iss. 4
Closed Access | Times Cited: 60
Blockchain in oil and gas industry: Applications, challenges, and future trends
Raja Wasim Ahmad, Khaled Salah, Raja Jayaraman, et al.
Technology in Society (2022) Vol. 68, pp. 101941-101941
Open Access | Times Cited: 51
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
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
A new hybrid approach model for predicting burst pressure of corroded pipelines of gas and oil
Haonan Ma, Hantong Wang, Mengying Geng, et al.
Engineering Failure Analysis (2023) Vol. 149, pp. 107248-107248
Closed Access | Times Cited: 23
Predicting failure pressure of corroded gas pipelines: A data-driven approach using machine learning
Rui Xiao, Tarek Zayed, Mohamed A. Meguid, et al.
Process Safety and Environmental Protection (2024) Vol. 184, pp. 1424-1441
Closed Access | Times Cited: 8
Deeppipe: Theory-guided neural network method for predicting burst pressure of corroded pipelines
Yunlu Ma, Jianqin Zheng, Yongtu Liang, et al.
Process Safety and Environmental Protection (2022) Vol. 162, pp. 595-609
Closed Access | Times Cited: 32
A Feature Selection–Based Intelligent Framework for Predicting Maximum Depth of Corroded Pipeline Defects
Hongfang Lü, Haoyan Peng, Zhao‐Dong Xu, et al.
Journal of Performance of Constructed Facilities (2022) Vol. 36, Iss. 5
Closed Access | Times Cited: 28
A graph neural network (GNN) method for assigning gas calorific values to natural gas pipeline networks
Zhaoming Yang, Zhe Liu, Jing Zhou, et al.
Energy (2023) Vol. 278, pp. 127875-127875
Closed Access | Times Cited: 19
A hybrid machine learning model for predicting crater width formed by explosions of natural gas pipelines
Guojin Qin, Ailin Xia, Hongfang Lü, et al.
Journal of Loss Prevention in the Process Industries (2023) Vol. 82, pp. 104994-104994
Closed Access | Times Cited: 16
A data-driven methodology for predicting residual strength of subsea pipeline with double corrosion defects
Xinhong Li, Ruichao Jia, Renren Zhang
Ocean Engineering (2023) Vol. 279, pp. 114530-114530
Closed Access | Times Cited: 15
Data Modeling Techniques for Pipeline Integrity Assessment: A State-of-the-Art Survey
Jiatong Ling, Ke Feng, Teng Wang, et al.
IEEE Transactions on Instrumentation and Measurement (2023) Vol. 72, pp. 1-17
Closed Access | Times Cited: 15
Analysis of machine learning models and data sources to forecast burst pressure of petroleum corroded pipelines: A comprehensive review
Afzal Ahmed Soomro, Ainul Akmar Mokhtar, Hilmi Hussin, et al.
Engineering Failure Analysis (2023) Vol. 155, pp. 107747-107747
Closed Access | Times Cited: 15
Automated Finite Element Analysis of Burst Capacity for Corroded Pipelines
Changqing Gong, X P Xiao, Dan M. Frangopol, et al.
International Journal of Pressure Vessels and Piping (2025), pp. 105489-105489
Closed Access
Theory and Machine Learning Modeling for Burst Pressure Estimation of Pipeline with Multipoint Corrosion
Hongfang Lü, Haoyan Peng, Zhao‐Dong Xu, et al.
Journal of Pipeline Systems Engineering and Practice (2023) Vol. 14, Iss. 3
Closed Access | Times Cited: 12
Advancing mining maintenance: integrating machine learning for proactive corrosion management
James J. LePage, Kaveh Ghouchani, Javad Mohammadpour, et al.
Elsevier eBooks (2025), pp. 179-194
Closed Access
Machine learning methods for predicting residual strength in corroded oil and gas steel pipes
Q. Wang, Hongfang Lü
npj Materials Degradation (2025) Vol. 9, Iss. 1
Open Access
Combined relevance vector machine technique and subset simulation importance sampling for structural reliability
Bin Xie, Chong Peng, Yanzhong Wang
Applied Mathematical Modelling (2022) Vol. 113, pp. 129-143
Closed Access | Times Cited: 18
Application and Potential of Drone Technology in Oil Palm Plantation: Potential and Limitations
Zailani Khuzaimah, Nazmi Mat Nawi, Siti Nooradzah Adam, et al.
Journal of Sensors (2022) Vol. 2022, pp. 1-18
Open Access | Times Cited: 17
Research and Application for Corrosion Rate Prediction of Natural Gas Pipelines Based on a Novel Hybrid Machine Learning Approach
Lei Xu, Jin Yu, Zhenyu Zhu, et al.
Coatings (2023) Vol. 13, Iss. 5, pp. 856-856
Open Access | Times Cited: 9
A novel stacking ensemble learner for predicting residual strength of corroded pipelines
Qiankun Wang, Hongfang Lü
npj Materials Degradation (2024) Vol. 8, Iss. 1
Open Access | Times Cited: 3
Hybrid machine learning for pullback force forecasting during horizontal directional drilling
Hongfang Lü, Tom Iseley, John C. Matthews, et al.
Automation in Construction (2021) Vol. 129, pp. 103810-103810
Closed Access | Times Cited: 22
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