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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:

Machine-learning-based models versus design-oriented models for predicting the axial compressive load of FRP-confined rectangular RC columns
Yehia A.K. Sayed, Alzhraa A. Ibrahim, Ashot Tamrazyan, et al.
Engineering Structures (2023) Vol. 285, pp. 116030-116030
Closed Access | Times Cited: 18

Showing 18 citing articles:

Machine-Learning Methods for Estimating Performance of Structural Concrete Members Reinforced with Fiber-Reinforced Polymers
Farzin Kazemi, Neda Asgarkhani, Torkan Shafighfard, et al.
Archives of Computational Methods in Engineering (2024)
Open Access | Times Cited: 30
Prediction of pull-out behavior of timber glued-in glass fiber reinforced polymer and steel rods under various environmental conditions based on ANN and GEP models
Mostafa Mohammadzadeh Taleshi, Nima Tajik, Alireza Mahmoudian, et al.
Case Studies in Construction Materials (2024) Vol. 20, pp. e02842-e02842
Open Access | Times Cited: 19
Prediction of non-uniform shrinkage of steel-concrete composite slabs based on explainable ensemble machine learning model
Shiqi Wang, Jinlong Liu, Qinghe Wang, et al.
Journal of Building Engineering (2024) Vol. 88, pp. 109002-109002
Closed Access | Times Cited: 11
Explainable machine learning: Compressive strength prediction of FRP-confined concrete column
Tianyu Hu, Hong Zhang, Cheng Cheng, et al.
Materials Today Communications (2024) Vol. 39, pp. 108883-108883
Closed Access | Times Cited: 8
Artificial Intelligence Methods for the Construction and Management of Buildings
Светлана Иванова, Aleksandr Kuznetsov, Roman Zverev, et al.
Sensors (2023) Vol. 23, Iss. 21, pp. 8740-8740
Open Access | Times Cited: 16
Explainable artificial intelligence framework for FRP composites design
Mostafa Yossef, Mohamed Noureldin, Aghyad Alqabbany
Composite Structures (2024) Vol. 341, pp. 118190-118190
Open Access | Times Cited: 5
Transfer learning-based confinement strength prediction of concrete confined by FRP transverse reinforcements
Fei Peng, Yazhong Li, Weichen Xue
Engineering Structures (2024) Vol. 310, pp. 118116-118116
Closed Access | Times Cited: 5
Machine learning-based prediction of optimal GFRP thickness for enhanced circular concrete column confinement
Imane Djafar-Henni, Amina Sadouki
Multiscale and Multidisciplinary Modeling Experiments and Design (2025) Vol. 8, Iss. 3
Closed Access
Data-driven machine-learning models for predicting non-uniform confinement effects of FRP-confined concrete
Jian Xie, Chenhang Jia, Zhe Wang
Structures (2025) Vol. 74, pp. 108555-108555
Closed Access
APPLICATION OF MACHINE LEARNING ALGORITHMS TO PREDICT HOTEL OCCUPANCY
Konstantins Kozlovskis, Liu Yuanyuan, Nataļja Lāce, et al.
Journal of Business Economics and Management (2023) Vol. 24, Iss. 3, pp. 594-613
Open Access | Times Cited: 13
Axial stress-strain behavior of pre-stressed CFRP confined concrete columns
Qiang Wang, Xin Liu, Yuhao Liu, et al.
Construction and Building Materials (2024) Vol. 446, pp. 138003-138003
Closed Access | Times Cited: 4
Stacking Ensemble-Based Machine Learning Model for Predicting Deterioration Components of Steel W-Section Beams
Azadeh Khoshkroodi, Hossein Parvini Sani, Mojtaba Aajami
Buildings (2024) Vol. 14, Iss. 1, pp. 240-240
Open Access | Times Cited: 2
Machine learning-based prediction of compressive strength in circular FRP-confined concrete columns
Ruifu Cui, Huihui Yang, Jiehong Li, et al.
Frontiers in Materials (2024) Vol. 11
Open Access | Times Cited: 1
The Efficiency of Using Machine Learning Techniques in Fiber-Reinforced-Polymer Applications in Structural Engineering
Mohammad Alhusban, Mohannad Alhusban, Ayah A. Alkhawaldeh
Sustainability (2023) Vol. 16, Iss. 1, pp. 11-11
Open Access | Times Cited: 3
Effect of different configurations of end stirrups on the seismic performance of RC columns
Yi-Yao Zhang, Kejian Ma, Enhe Bao, et al.
Structures (2023) Vol. 59, pp. 105766-105766
Closed Access | Times Cited: 1
Predicting the Maximum Load Capacity of Circular RC Columns Confined with Fibre-Reinforced Polymer (FRP) Using Machine Learning Model
Indra Prakash, Thùy Anh Nguyễn
Journal of Science and Transport Technology (2023), pp. 25-43
Open Access | Times Cited: 1
Emotional Responses to Religious Conversion: Insights from Machine Learning
Achmad Maimun, Andi Bahtiar Semma
Islamic Guidance and Counseling Journal (2023) Vol. 6, Iss. 2
Open Access
Damage Prediction and Crashworthiness Optimization of FOBEVs in Positive Crashes for Battery Electric Vehicles
Ke Liu, Yinghua Liao, Hongrui Wang, et al.
SAE technical papers on CD-ROM/SAE technical paper series (2023)
Closed Access
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