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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:

Ensemble Machine Learning approach for evaluating the material characterization of carbon nanotube-reinforced cementitious composites
Faramarz Bagherzadeh, Torkan Shafighfard
Case Studies in Construction Materials (2022) Vol. 17, pp. e01537-e01537
Open Access | Times Cited: 43

Showing 1-25 of 43 citing articles:

Data-driven compressive strength prediction of steel fiber reinforced concrete (SFRC) subjected to elevated temperatures using stacked machine learning algorithms
Torkan Shafighfard, Faramarz Bagherzadeh, Rana Abdollahi Rizi, et al.
Journal of Materials Research and Technology (2022) Vol. 21, pp. 3777-3794
Open Access | Times Cited: 86
Prediction of maximum tensile stress in plain-weave composite laminates with interacting holes via stacked machine learning algorithms: A comparative study
Faramarz Bagherzadeh, Torkan Shafighfard, Raja Muhammad Awais Khan, et al.
Mechanical Systems and Signal Processing (2023) Vol. 195, pp. 110315-110315
Closed Access | Times Cited: 66
Scope of machine learning in materials research—A review
Md Hosne Mobarak, Mariam Akter Mimona, Md Aminul Islam, et al.
Applied Surface Science Advances (2023) Vol. 18, pp. 100523-100523
Open Access | Times Cited: 54
Chained machine learning model for predicting load capacity and ductility of steel fiber–reinforced concrete beams
Torkan Shafighfard, Farzin Kazemi, Faramarz Bagherzadeh, et al.
Computer-Aided Civil and Infrastructure Engineering (2024) Vol. 39, Iss. 23, pp. 3573-3594
Open Access | Times Cited: 49
Machine-learning methods for estimating compressive strength of high-performance alkali-activated concrete
Torkan Shafighfard, Farzin Kazemi, Neda Asgarkhani, et al.
Engineering Applications of Artificial Intelligence (2024) Vol. 136, pp. 109053-109053
Closed Access | Times Cited: 47
Enhancing wastewater treatment efficiency through machine learning-driven effluent quality prediction: A plant-level analysis
Maria Alice Prado Cechinel, Juliana Neves, João Vitor Rios Fuck, et al.
Journal of Water Process Engineering (2024) Vol. 58, pp. 104758-104758
Closed Access | Times Cited: 15
RAGN-R: A multi-subject ensemble machine-learning method for estimating mechanical properties of advanced structural materials
Farzin Kazemi, Aybike Özyüksel Çiftçioğlu, Torkan Shafighfard, et al.
Computers & Structures (2025) Vol. 308, pp. 107657-107657
Closed Access | Times Cited: 2
Graph neural network-based bearing fault diagnosis using Granger causality test
Zhewen Zhang, Lifeng Wu
Expert Systems with Applications (2023) Vol. 242, pp. 122827-122827
Closed Access | Times Cited: 26
Machine learning for an explainable cost prediction of medical insurance
Ugochukwu Orji, Elochukwu Ukwandu
Machine Learning with Applications (2023) Vol. 15, pp. 100516-100516
Open Access | Times Cited: 21
Characterization and evaluation of flour's physico-chemical, functional, and nutritional quality attributes from edible and non-edible parts of papaya
Mahfujul Alam, Mir Meahadi Hasan, Mrinal Kanti Debnath, et al.
Journal of Agriculture and Food Research (2024) Vol. 15, pp. 100961-100961
Open Access | Times Cited: 8
Application of Machine Learning (ML)-based multi-classifications to identify corrosion fatigue cracking phenomena in Naval steel weldments
Vivek Srivastava, B. N. Basu, N. Prabhu
Materials Today Communications (2024) Vol. 39, pp. 108591-108591
Closed Access | Times Cited: 7
Machine learning for prediction of the uniaxial compressive strength within carbonate rocks
Mohamed Abdelhedi, Rateb Jabbar, Ahmed Ben Said, et al.
Earth Science Informatics (2023) Vol. 16, Iss. 2, pp. 1473-1487
Closed Access | Times Cited: 19
Novel base predictive model of resilient modulus of compacted subgrade soils by using interpretable approaches with graphical user interface
Loai Alkhattabi, Kiran Arif
Materials Today Communications (2024) Vol. 40, pp. 109764-109764
Closed Access | Times Cited: 6
Unpacking predictive relationships in graphene oxide-reinforced cementitious nanocomposites: An explainable ensemble learning approach for augmented data
Hossein Adel, Majid Ilchi Ghazaan, Asghar Habibnejad Korayem
Engineering Applications of Artificial Intelligence (2025) Vol. 144, pp. 110123-110123
Closed Access
Applications of machine learning methods for design and characterization of high-performance fiber-reinforced cementitious composite (HPFRCC): a review
Pengwei Guo, Seyed Amirhossein Moghaddas, Yiming Liu, et al.
Journal of Sustainable Cement-Based Materials (2025), pp. 1-24
Closed Access
Advancements in CNT research: Integrating machine learning with microscopic simulations, macroscopic techniques, and application of performance prediction and functional optimization
Dianming Chu, Chenyu Gao, Zongchao Ji, et al.
Materials Today Chemistry (2025) Vol. 45, pp. 102616-102616
Closed Access
Utilizing big data and categorical boosting modeling methodology to interpret the load-deflection behavior of steel fiber-reinforced concrete beams
Ahmet Tüken, Yassir M. Abbas, Nadeem A. Siddiqui
Engineering Applications of Artificial Intelligence (2025) Vol. 148, pp. 110377-110377
Closed Access
Design optimization of high-performance, cost-efficient concrete enhanced with nano-CNTs: A hybrid approach using machine learning and NSGA-II
A. A. Ebrahim
Materials Today Communications (2025), pp. 112251-112251
Closed Access
Prediction of the flexural strength and elastic modulus of cementitious materials reinforced with carbon nanotubes: An approach with artificial intelligence
Mahyar Ramezani, Doeun Choe, A. Rasheed
Engineering Applications of Artificial Intelligence (2025) Vol. 150, pp. 110544-110544
Closed Access
Deep H2O: Cyber attacks detection in water distribution systems using deep learning
Md Nazmul Kabir Sikder, Minh B.T. Nguyen, E. Donald Elliott, et al.
Journal of Water Process Engineering (2023) Vol. 52, pp. 103568-103568
Closed Access | Times Cited: 13
Machine-learning-aided prediction and optimization of struvite recovery from synthetic wastewater
Lijian Leng, Bingyan Kang, Donghai Xu, et al.
Journal of Water Process Engineering (2024) Vol. 58, pp. 104896-104896
Closed Access | Times Cited: 4
Machine Learning as a “Catalyst” for Advancements in Carbon Nanotube Research
Guohai Chen, Dai‐Ming Tang
Nanomaterials (2024) Vol. 14, Iss. 21, pp. 1688-1688
Open Access | Times Cited: 4
Experimental studies and symbolic machine learning aided prediction model of the mechanical properties of recycled waste slurry micropowder mortar
Zhengyu Fei, Shixue Liang, Yiqing Cai
Case Studies in Construction Materials (2024) Vol. 20, pp. e03060-e03060
Open Access | Times Cited: 3
Machine Learning Prediction of Permeability Distribution in the X Field Malay Basin Using Elastic Properties
Zaky Ahmad Riyadi, John Oluwadamilola Olutoki, Maman Hermana, et al.
Results in Engineering (2024), pp. 103421-103421
Open Access | Times Cited: 3
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