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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:

A unified causation prediction model for aboveground onshore oil and refined product pipeline incidents using artificial neural network
Pallavi Kumari, Qingsheng Wang, Faisal Khan, et al.
Process Safety and Environmental Protection (2022) Vol. 187, pp. 529-540
Open Access | Times Cited: 16

Showing 16 citing articles:

Prediction of external corrosion rate for buried oil and gas pipelines: a novel deep learning with DNN and attention mechanism method
Yu Guang, Wenhe Wang, Hongwei Song, et al.
International Journal of Pressure Vessels and Piping (2024) Vol. 209, pp. 105218-105218
Closed Access | Times Cited: 14
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
Knowledge Mapping for Fire Risk Assessment: A Scientometric Analysis Based on VOSviewer and CiteSpace
Zhixin Tang, Tianwei Zhang, Lizhi Wu, et al.
Fire (2024) Vol. 7, Iss. 1, pp. 23-23
Open Access | Times Cited: 4
Interpretable machine learning models for failure cause prediction in imbalanced oil pipeline data
Bright Awuku, Ying Huang, Nita Yodo, et al.
Measurement Science and Technology (2024) Vol. 35, Iss. 7, pp. 076006-076006
Closed Access | Times Cited: 4
Predicting and Understanding Emergency Shutdown Durations Level of Pipeline Incidents Using Machine Learning Models and Explainable AI
Lemlem Asaye, Chau Le, Ying Huang, et al.
Processes (2025) Vol. 13, Iss. 2, pp. 445-445
Open Access
A Meta-Learning Fusion Method for Monitoring Data Prediction of Oil Wells
Yun Shen, Xiang Wang, Yixin Xie, et al.
Geoenergy Science and Engineering (2025), pp. 213895-213895
Closed Access
A Direct Transfer Entropy-Based Multiblock Bayesian Network for Root Cause Diagnosis of Process Faults
Pallavi Kumari, Qingsheng Wang, Faisal Khan, et al.
Industrial & Engineering Chemistry Research (2022)
Closed Access | Times Cited: 11
Yield and Properties Prediction Based on the Multicondition LSTM Model for the Solvent Deasphalting Process
Jian Long, Yifan Chen, Deng‐Ke Cao, et al.
ACS Omega (2023) Vol. 8, Iss. 6, pp. 5437-5450
Open Access | Times Cited: 6
Predicting the External Corrosion Rate of Buried Pipelines Using a Novel Soft Modeling Technique
Zebei Ren, Kun Chen, Dongdong Yang, et al.
Applied Sciences (2024) Vol. 14, Iss. 12, pp. 5120-5120
Open Access | Times Cited: 2
Environmental Risk Assessment Using Neural Network in Liquefied Petroleum Gas Terminal
Lalit Rajaramji Gabhane, Nagamalleswara Rao Kanidarapu
Toxics (2023) Vol. 11, Iss. 4, pp. 348-348
Open Access | Times Cited: 5
Application of machine learning methods for process safety assessments
Tarek Bengherbia, Faisal A. Syed, Jenny Chew, et al.
Process Safety Progress (2023) Vol. 43, Iss. S1
Closed Access | Times Cited: 4
Application of artificial intelligence hybrid models in safety assessment of submarine pipelines: Principles and methods
Shenwen Zhang, Anmin Zhang, Pengxv Chen, et al.
Ocean Engineering (2024) Vol. 312, pp. 119203-119203
Closed Access | Times Cited: 1
Threat and Risk Analysis-Based Neural Network for a Chemical Explosion (TRANCE) Model to Predict Hazards in Petroleum Refinery
Lalit Rajaramji Gabhane, Nagamalleswara Rao Kanidarapu
Toxics (2023) Vol. 11, Iss. 4, pp. 350-350
Open Access | Times Cited: 2
Multi-level Frequent Pattern Mining on Pipeline Incident Data
Connor C.J. Hryhoruk, Carson K. Leung, Jingyuan Li, et al.
Lecture notes on data engineering and communications technologies (2024), pp. 380-392
Closed Access
Uncertainty Quantification Method for Trend Prediction of Oil Well Time Series Data Based on SDMI Loss Function
Yun Shen, Xiang Wang, Yixin Xie, et al.
Processes (2024) Vol. 12, Iss. 12, pp. 2642-2642
Open Access
Accident and Incident Analysis in the Oil and Gas Sector Using Artificial Intelligence and Machine Learning
Asharul Islam Khan, Majid Al Busafi, Amjed Al Thuhli, et al.
Procedia Computer Science (2024) Vol. 251, pp. 295-302
Open Access
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