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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:

Structural assessment of Basalt FRP reinforced self-compacting geopolymer concrete using artificial neural network (ANN) modelling
Sherin Khadeeja Rahman, Riyadh Al-Ameri
Construction and Building Materials (2023) Vol. 397, pp. 132464-132464
Open Access | Times Cited: 23

Showing 23 citing articles:

Modified particle packing approach for optimizing waste marble powder as a cement substitute in high-performance concrete
Ahmed Essam, Sahar A. Mostafa, Mehran Khan, et al.
Construction and Building Materials (2023) Vol. 409, pp. 133845-133845
Closed Access | Times Cited: 32
Synergistic effects of fly ash and graphene oxide composites at high temperatures and prediction using ANN and RSM approach
I Venkata Ramana, N. Parthasarathi
Scientific Reports (2025) Vol. 15, Iss. 1
Open Access | Times Cited: 1
Exploratory literature review and scientometric analysis of artificial intelligence applied to geopolymeric materials
Aldo Ribeiro de Carvalho, Romário Parreira Pita, Thaís Mayra de Oliveira, et al.
Engineering Applications of Artificial Intelligence (2025) Vol. 145, pp. 110210-110210
Closed Access | Times Cited: 1
A systematic literature review of AI-based prediction methods for self-compacting, geopolymer, and other eco-friendly concrete types: Advancing sustainable concrete
Tariq Ali, Mohamed Hechmi El Ouni, Muhammad Zeeshan Qureshi, et al.
Construction and Building Materials (2024) Vol. 440, pp. 137370-137370
Closed Access | Times Cited: 8
A robust carbonation depth model in recycled aggregate concrete (RAC) using neural network
Nolan C. Concha
Expert Systems with Applications (2023) Vol. 237, pp. 121650-121650
Closed Access | Times Cited: 16
Predicting 28-day compressive strength of fibre-reinforced self-compacting concrete (FR-SCC) using MEP and GEP
Waleed Bin Inqiad, Muhammad Shahid Siddique, Mujahid Ali, et al.
Scientific Reports (2024) Vol. 14, Iss. 1
Open Access | Times Cited: 5
Predicting the compressive strength of rubberized concrete incorporating brick powder based on MLP and RBF neural networks
David Sinkhonde, Destine Mashava, Tajebe Bezabih, et al.
Waste Management Bulletin (2025)
Open Access
Machine Learning and Sustainable Geopolymer Materials: A Systematic Review
Ho Anh Thu Nguyen, Duy Hoang Pham, Yonghan Ahn, et al.
Materials Today Sustainability (2025) Vol. 30, pp. 101095-101095
Open Access
Machine Learning as an Innovative Engineering Tool for Controlling Concrete Performance: A Comprehensive Review
Fatemeh Mobasheri, Masoud Hosseinpoor, Ammar Yahia, et al.
Archives of Computational Methods in Engineering (2025)
Closed Access
Predicting High-Strength Concrete’s Compressive Strength: A Comparative Study of Artificial Neural Networks, Adaptive Neuro-Fuzzy Inference System, and Response Surface Methodology
Tianlong Li, Jianyu Yang, Pengxiao Jiang, et al.
Materials (2024) Vol. 17, Iss. 18, pp. 4533-4533
Open Access | Times Cited: 3
Performance Comparison of Artificial Neural Network and Random Forest Models for Predicting the Compressive Strength of Fibre-Reinforced GGBS-Based Geopolymer Concrete Composites
Shimol Philip, M. Nidhi
Materials Circular Economy (2024) Vol. 6, Iss. 1
Closed Access | Times Cited: 2
An artificial neural network approach to predict particle shape characteristics of clay brick powder under various milling conditions
David Sinkhonde, Destine Mashava
Results in Materials (2024), pp. 100650-100650
Open Access | Times Cited: 2
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: 4
Performance Assessment of One-Part Self-Compacted Geopolymer Concrete Containing Recycled Concrete Aggregate: A Critical Comparison Using Artificial Neural Network (ANN) and Linear Regression Models
Bahareh Nikmehr, Bidur Kafle, Riyadh Al-Ameri
Recycling (2024) Vol. 9, Iss. 5, pp. 73-73
Open Access | Times Cited: 1
Revisiting the surface characteristics of fly ash-teff straw ash-based geopolymer mortars in the context of fractal theory
Tajebe Bezabih, David Sinkhonde, Derrick Mirindi
Results in Surfaces and Interfaces (2024), pp. 100292-100292
Open Access | Times Cited: 1
Prediction of flexural strength of concrete with eggshell and glass powders: Advanced cutting-edge approach for sustainable materials
Xiaofei Liu, Ali H. AlAteah, Ali Alsubeai, et al.
REVIEWS ON ADVANCED MATERIALS SCIENCE (2024) Vol. 63, Iss. 1
Open Access | Times Cited: 1
A color prediction model for mending materials of the Yuquan Iron Pagoda based on machine learning
Xuegang Liu, Yuhang Liu, K. Wang, et al.
Research Square (Research Square) (2024)
Open Access
Mechanical performance degradation investigation on FRP reinforced concrete based on neural network design method
Wenyuan Xu, Wei Li, Dayang Wang, et al.
Scientific Reports (2024) Vol. 14, Iss. 1
Open Access
Artificial Intelligence Models for Predicting the Compressive Strength of Geopolymer Cements
Cut Rahmawati, Siti Aisyah, Sanusi Sanusi, et al.
Civil Engineering Journal (2024) Vol. 10, pp. 37-50
Open Access
On the surface roughness properties of fly ash-based geopolymer mortars with teff straw ash from the image analysis viewpoint
Tajebe Bezabih, David Sinkhonde, Derrick Mirindi
Green Technologies and Sustainability (2024) Vol. 3, Iss. 1, pp. 100127-100127
Open Access
Evaluation of Machine Learning and Traditional Methods for Estimating Compressive Strength of UHPC
Tianlong Li, Pengxiao Jiang, Yunfeng Qian, et al.
Buildings (2024) Vol. 14, Iss. 9, pp. 2693-2693
Open Access
Hybrid weights structure model based on Lagrangian principle to handle big data challenges for identification of oil well production: A case study on the North Basra oilfield, Iraq
Raad Z. Homod, A. S. Albahri, Basil Sh. Munahi, et al.
Engineering Applications of Artificial Intelligence (2024) Vol. 138, pp. 109465-109465
Closed Access
Axial Capacity Prediction of Concrete Externally Strengthened by Low-Cost Natural Fiber Reinforced Polymer Composite Utilizing Machine Learning Intelligence
Josiah Davidson Patricio Antonino, Dante Laroza Silva
(2024), pp. 36-42
Closed Access
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