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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:

Reviewing machine learning of corrosion prediction in a data-oriented perspective
Leonardo Bertolucci Coelho, Dawei Zhang, Yves Van Ingelgem, et al.
npj Materials Degradation (2022) Vol. 6, Iss. 1
Open Access | Times Cited: 147

Showing 1-25 of 147 citing articles:

A machine learning approach for corrosion small datasets
T. Sutojo, Supriadi Rustad, Muhamad Akrom, et al.
npj Materials Degradation (2023) Vol. 7, Iss. 1
Open Access | Times Cited: 74
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: 67
Artificial-intelligence-led revolution of construction materials: From molecules to Industry 4.0
Xing Quan Wang, Pengguang Chen, Cheuk Lun Chow, et al.
Matter (2023) Vol. 6, Iss. 6, pp. 1831-1859
Open Access | Times Cited: 52
Emerging AI technologies for corrosion monitoring in oil and gas industry: A comprehensive review
Ali Hussein Khalaf, Ying Xiao, Ning Xu, et al.
Engineering Failure Analysis (2023) Vol. 155, pp. 107735-107735
Closed Access | Times Cited: 49
Combined electrochemical, DFT/MD-simulation and hybrid machine learning based on ANN-ANFIS models for prediction of doxorubicin drug as corrosion inhibitor for mild steel in 0.5 M H2SO4 solution
F. E. Abeng, Valentine Chikaodili Anadebe
Computational and Theoretical Chemistry (2023) Vol. 1229, pp. 114334-114334
Closed Access | Times Cited: 46
A critical review of machine learning algorithms in maritime, offshore, and oil & gas corrosion research: A comprehensive analysis of ANN and RF models
Md Mahadi Hasan Imran, Shahrizan Jamaludin, Ahmad Faisal Mohamad Ayob
Ocean Engineering (2024) Vol. 295, pp. 116796-116796
Closed Access | Times Cited: 24
Transfer learning enables prediction of steel corrosion in concrete under natural environments
Haodong Ji, Ye Tian, Chuanqing Fu, et al.
Cement and Concrete Composites (2024) Vol. 148, pp. 105488-105488
Closed Access | Times Cited: 22
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: 20
Unlocking the potential of FTIR for corrosion inhibition prediction exploiting principal component analysis: Machine learning for QSPR modeling
Ahmad‐Reza Sadeghi, M. Shariatmadar, S. Amoozadeh, et al.
Journal of the Taiwan Institute of Chemical Engineers (2025) Vol. 169, pp. 105998-105998
Closed Access | Times Cited: 2
Corrosion challenges in supercritical CO2 transportation, storage, and utilization—a review
Haofei Sun, Haoxiang Wang, Yimin Zeng, et al.
Renewable and Sustainable Energy Reviews (2023) Vol. 179, pp. 113292-113292
Closed Access | Times Cited: 39
Enhancing corrosion-resistant alloy design through natural language processing and deep learning
K.N. Sasidhar, Nima H. Siboni, Jaber Rezaei Mianroodi, et al.
Science Advances (2023) Vol. 9, Iss. 32
Open Access | Times Cited: 30
Machine learning prediction of corrosion rate of steel in carbonated cementitious mortars
Haodong Ji, Hailong Ye
Cement and Concrete Composites (2023) Vol. 143, pp. 105256-105256
Closed Access | Times Cited: 30
Probing the randomness of the local current distributions of 316 L stainless steel corrosion in NaCl solution
Leonardo Bertolucci Coelho, Daniel Torres, Miguel Bernal, et al.
Corrosion Science (2023) Vol. 217, pp. 111104-111104
Open Access | Times Cited: 23
Data-driven machine learning approaches for predicting permeability and corrosion risk in hybrid concrete incorporating blast furnace slag and fly ash
Nishant Kumar, Satya Prakash, Sufyan Ghani, et al.
Asian Journal of Civil Engineering (2024) Vol. 25, Iss. 4, pp. 3263-3275
Closed Access | Times Cited: 10
Harnessing machine learning and virtual sample generation for corrosion studies of 2-alkyl benzimidazole scaffold small dataset with an experimental validation
Ram S Iyer, Narayan S Iyer, Rugmini Ammal P, et al.
Journal of Molecular Structure (2024) Vol. 1306, pp. 137767-137767
Closed Access | Times Cited: 10
Artificial intelligence combined with high-throughput calculations to improve the corrosion resistance of AlMgZn alloy
Yucheng Ji, Xiaoqian Fu, Feng Ding, et al.
Corrosion Science (2024) Vol. 233, pp. 112062-112062
Closed Access | Times Cited: 9
Deep learning and finite element simulation of spatiotemporal multiscale fusion: real time prediction of magnesium alloys corrosion covered with MAO coatings
Xinke Qi, A. Q. Liu, Yuanyuan Li, et al.
Materials Today Communications (2025), pp. 111915-111915
Closed Access | Times Cited: 1
Hybrid model development emulating linear polarization resistance method towards optimizing dosages of corrosion inhibitors
Chamanthi Denisha Jayaweera, Ivaylo Plamenov Hitsov, David Fernandes del Pozo, et al.
Electrochimica Acta (2025), pp. 146116-146116
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: 38
Bridging the Gap between Single Nanoparticle Imaging and Global Electrochemical Response by Correlative Microscopy Assisted By Machine Vision
Louis Godeffroy, Jean‐François Lemineur, Viacheslav Shkirskiy, et al.
Small Methods (2022) Vol. 6, Iss. 9
Open Access | Times Cited: 36
Application of machine learning models to investigate the performance of stainless steel type 904 with agricultural waste
Omotayo Sanni, Oluwatobi Adeleke, Kingsley Ukoba, et al.
Journal of Materials Research and Technology (2022) Vol. 20, pp. 4487-4499
Open Access | Times Cited: 36
Artificial neural network model to estimate the long-term carbonation depth of concrete exposed to natural environments
Arsalan Majlesi, Hamid Khodadadi Koodiani, Oladis Trocónis de Rincón, et al.
Journal of Building Engineering (2023) Vol. 74, pp. 106545-106545
Closed Access | Times Cited: 22
Deep learning framework for uncovering compositional and environmental contributions to pitting resistance in passivating alloys
K.N. Sasidhar, Nima H. Siboni, Jaber Rezaei Mianroodi, et al.
npj Materials Degradation (2022) Vol. 6, Iss. 1
Open Access | Times Cited: 27
Corrosion of Electrochemical Energy Materials: Stability Analyses Beyond Pourbaix Diagrams
Alexandra Zagalskaya, Payal Chaudhary, Vitaly Alexandrov
The Journal of Physical Chemistry C (2023) Vol. 127, Iss. 30, pp. 14587-14598
Closed Access | Times Cited: 15
Estimating pitting descriptors of 316 L stainless steel by machine learning and statistical analysis
Leonardo Bertolucci Coelho, Daniel Torres, Vincent Vangrunderbeek, et al.
npj Materials Degradation (2023) Vol. 7, Iss. 1
Open Access | Times Cited: 15
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