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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:

Development of ensemble machine learning approaches for designing fiber-reinforced polymer composite strain prediction model
Abdalrhman Milad, Sadaam Hadee Hussein, Ahlam R. Khekan, et al.
Engineering With Computers (2021) Vol. 38, Iss. 4, pp. 3625-3637
Closed Access | Times Cited: 51

Showing 1-25 of 51 citing articles:

Advances in machine learning-aided design of reinforced polymer composite and hybrid material systems
Christian Emeka Okafor, Sunday Iweriolor, Okwuchukwu Innocent Ani, et al.
Hybrid Advances (2023) Vol. 2, pp. 100026-100026
Open Access | Times Cited: 60
3D printing of biodegradable polymers and their composites – Current state-of-the-art, properties, applications, and machine learning for potential future applications
S. A. V. Dananjaya, Venkata S. Chevali, John P. Dear, et al.
Progress in Materials Science (2024) Vol. 146, pp. 101336-101336
Open Access | Times Cited: 25
Thermal properties of surface-modified nano-Al2O3/Kevlar fiber/epoxy composites
Mustafa Özen, Gökhan Demircan, Murat Kısa, et al.
Materials Chemistry and Physics (2021) Vol. 278, pp. 125689-125689
Closed Access | Times Cited: 61
Evaluation of Artificial Intelligence Methods to Estimate the Compressive Strength of Geopolymers
Yong Zou, Chao Zheng, Abdullah M. Alzahrani, et al.
Gels (2022) Vol. 8, Iss. 5, pp. 271-271
Open Access | Times Cited: 61
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
Buckling response of CNT based hybrid FG plates using finite element method and machine learning method
Ravi Kumar, Ajay Kumar, Divesh Ranjan Kumar
Composite Structures (2023) Vol. 319, pp. 117204-117204
Closed Access | Times Cited: 24
Comparative analysis of machine learning techniques for predicting wear and friction properties of MWCNT reinforced PMMA nanocomposites
Prince Jain, Unnati Joshi, Anand Y. Joshi, et al.
Ain Shams Engineering Journal (2024) Vol. 15, Iss. 9, pp. 102895-102895
Open Access | Times Cited: 10
Machine learning-based modelling, feature importance and Shapley additive explanations analysis of variable-stiffness composite beam structures
Nikolaos Karathanasopoulos, Agyapal Singh, Panagiotis Hadjidoukas
Structures (2024) Vol. 62, pp. 106206-106206
Closed Access | Times Cited: 8
Using machine learning to predict the long-term performance of fibre-reinforced polymer structures: A state-of-the-art review
Chiara Machello, Milad Bazli, Ali Rajabipour, et al.
Construction and Building Materials (2023) Vol. 408, pp. 133692-133692
Open Access | Times Cited: 19
Assessing the efficacy of machine learning models in hydroxyapatite nano-powder assisted electro discharge machining of Ti-6Al-4 V Grade-5 alloy
Prince Jain, Ayanesh Y. Joshi, Anand Y. Joshi
International Journal on Interactive Design and Manufacturing (IJIDeM) (2024)
Closed Access | Times Cited: 6
Prediction of the compressive strength and carpet plot for cross-material CFRP laminate based on deep transfer learning
Zhicen Song, Yunwen Feng, Cheng Lu
Materials Science and Engineering A (2025) Vol. 924, pp. 147792-147792
Closed Access
Data‐driven machine learning regression methods to predict the residual strength in FRP composites subjected to fatigue
Anand Gaurav
Polymer Composites (2025)
Closed Access
Data-driven resilient model development and feature selection for rock compressive strength prediction using machine learning and transformer techniques
Md Mamunur Rahaman, Mohammad Islam Miah
Earth Science Informatics (2025) Vol. 18, Iss. 3
Closed Access
State-of-the-Art Review of the Performance of Fiber-Reinforced-Composite-Confined Concrete Columns at Ambient Temperatures
Zhixin Liu, Chaochao Sun, Jili Qu, et al.
Materials (2025) Vol. 18, Iss. 5, pp. 1151-1151
Open Access
Investigating the Bond Strength of FRP Laminates with Concrete Using LIGHT GBM and SHAPASH Analysis
Muhammad Nasir Amin, Babatunde Abiodun Salami, Muhammad Zahid, et al.
Polymers (2022) Vol. 14, Iss. 21, pp. 4717-4717
Open Access | Times Cited: 25
Properties prediction of composites based on machine learning models: A focus on statistical index approaches
Barshan Dev, Md Ashikur Rahman, Md. Jahidul Islam, et al.
Materials Today Communications (2023) Vol. 38, pp. 107659-107659
Closed Access | Times Cited: 15
Structural Performance of EB-FRP-Strengthened RC T-Beams Subjected to Combined Torsion and Shear Using ANN
Ahad Amini Pishro, Zhengrui Zhang, Mojdeh Amini Pishro, et al.
Materials (2022) Vol. 15, Iss. 14, pp. 4852-4852
Open Access | Times Cited: 21
Perspectives on development of biomedical polymer materials in artificial intelligence age
Shijin Xie
Journal of Biomaterials Applications (2023) Vol. 37, Iss. 8, pp. 1355-1375
Closed Access | Times Cited: 11
Using meta-heuristic optimization in ANFIS models to estimate compressive strength for recycled aggregate concrete
Xuedi Hong, Jing Wang
Multiscale and Multidisciplinary Modeling Experiments and Design (2024) Vol. 7, Iss. 4, pp. 3355-3374
Closed Access | Times Cited: 3
Prediction of fracture toughness of concrete using the machine learning approach
Alireza Bagher Shemirani
Theoretical and Applied Fracture Mechanics (2024) Vol. 134, pp. 104749-104749
Closed Access | Times Cited: 3
Development of a Hybrid Machine Learning Model for Asphalt Pavement Temperature Prediction
Abdalrhman Milad, Ibrahim Adwan, Sayf A. Majeed, et al.
IEEE Access (2021) Vol. 9, pp. 158041-158056
Open Access | Times Cited: 24
Weighted ensembles of artificial neural networks based on Gaussian mixture modeling for truck productivity prediction at open-pit mines
Chengkai Fan, Na Zhang, Bei Jiang, et al.
Mining Metallurgy & Exploration (2023) Vol. 40, Iss. 2, pp. 583-598
Closed Access | Times Cited: 9
Ensemble machine learning for predicting the homogenized elastic properties of unidirectional composites: A SHAP-based interpretability analysis
Wenzhao Wang, Yunmei Zhao, Yan Li
Acta Mechanica Sinica (2023) Vol. 40, Iss. 3
Closed Access | Times Cited: 9
Evaluating the Performance of Ensemble Machine Learning Algorithms Over Traditional Machine Learning Algorithms for Predicting Fire Resistance in FRP Strengthened Concrete Beams
H.R. Kumarawadu, Pasindu Weerasinghe, Jude Shalitha Perera
Electronic Journal of Structural Engineering (2024) Vol. 24, Iss. 3, pp. 47-53
Closed Access | Times Cited: 2
Data-driven modeling for thermo-elastic properties of vacancy-defective graphene reinforced nanocomposites with its application to functionally graded beams
Shaoyu Zhao, Yingyan Zhang, Yihe Zhang, et al.
Engineering With Computers (2022) Vol. 39, Iss. 4, pp. 3023-3039
Closed Access | Times Cited: 13
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