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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:

Prediction of Compressive Strength of Fly-Ash-Based Concrete Using Ensemble and Non-Ensemble Supervised Machine-Learning Approaches
Yang Song, Jun Zhao, Krzysztof Adam Ostrowski, et al.
Applied Sciences (2021) Vol. 12, Iss. 1, pp. 361-361
Open Access | Times Cited: 55

Showing 1-25 of 55 citing articles:

Prediction of concrete and FRC properties at high temperature using machine and deep learning: A review of recent advances and future perspectives
Nizar Faisal Alkayem, Lei Shen, Ali Mayya, et al.
Journal of Building Engineering (2023) Vol. 83, pp. 108369-108369
Closed Access | Times Cited: 104
Compressive strength of concrete material using machine learning techniques
Satish Paudel, Anil Pudasaini, Rajesh Kumar Shrestha, et al.
Cleaner Engineering and Technology (2023) Vol. 15, pp. 100661-100661
Open Access | Times Cited: 57
Predictive modeling for compressive strength of 3D printed fiber-reinforced concrete using machine learning algorithms
Mana Alyami, Majid Khan, Muhammad Fawad, et al.
Case Studies in Construction Materials (2023) Vol. 20, pp. e02728-e02728
Open Access | Times Cited: 55
Machine learning and interactive GUI for concrete compressive strength prediction
Mohamed Kamel Elshaarawy, Mostafa M. Alsaadawi, Abdelrahman Kamal Hamed
Scientific Reports (2024) Vol. 14, Iss. 1
Open Access | Times Cited: 41
Predicting the mechanical properties of plastic concrete: An optimization method by using genetic programming and ensemble learners
Usama Asif, Muhammad Faisal Javed, Maher Abuhussain, et al.
Case Studies in Construction Materials (2024) Vol. 20, pp. e03135-e03135
Open Access | Times Cited: 36
Stacked-based machine learning to predict the uniaxial compressive strength of concrete materials
Abdelrahman Kamal Hamed, Mohamed Kamel Elshaarawy, Mostafa M. Alsaadawi
Computers & Structures (2025) Vol. 308, pp. 107644-107644
Closed Access | Times Cited: 8
Artificial intelligence-based estimation of ultra-high-strength concrete's flexural property
Qichen Wang, Abasal Hussain, Muhammad Usman Farooqi, et al.
Case Studies in Construction Materials (2022) Vol. 17, pp. e01243-e01243
Open Access | Times Cited: 56
Comparative Study of Experimental and Modeling of Fly Ash-Based Concrete
Kaffayatullah Khan, Ayaz Ahmad, Muhammad Nasir Amin, et al.
Materials (2022) Vol. 15, Iss. 11, pp. 3762-3762
Open Access | Times Cited: 45
A Comparison of Machine Learning Tools That Model the Splitting Tensile Strength of Self-Compacting Recycled Aggregate Concrete
Jesús de‐Prado‐Gil, Covadonga Palencia, P. Jagadesh, et al.
Materials (2022) Vol. 15, Iss. 12, pp. 4164-4164
Open Access | Times Cited: 44
Application of Machine Learning Approaches to Predict the Strength Property of Geopolymer Concrete
Rongchuan Cao, Zheng Fang, Man Jin, et al.
Materials (2022) Vol. 15, Iss. 7, pp. 2400-2400
Open Access | Times Cited: 42
Prediction of Mechanical Properties of Fly-Ash/Slag-Based Geopolymer Concrete Using Ensemble and Non-Ensemble Machine-Learning Techniques
Muhammad Nasir Amin, Kaffayatullah Khan, Muhammad Faisal Javed, et al.
Materials (2022) Vol. 15, Iss. 10, pp. 3478-3478
Open Access | Times Cited: 41
Enhancing compressive strength prediction in self-compacting concrete using machine learning and deep learning techniques with incorporation of rice husk ash and marble powder
Muhammad Sarmad Mahmood, Ayub Elahi, Osama Zaid, et al.
Case Studies in Construction Materials (2023) Vol. 19, pp. e02557-e02557
Open Access | Times Cited: 25
Machine and Deep Learning Methods for Concrete Strength Prediction: A Bibliometric and Content Analysis Review of Research Trends and Future Directions
Raman Kumar, Essam Althaqafi, S. Gopal Krishna Patro, et al.
Applied Soft Computing (2024) Vol. 164, pp. 111956-111956
Closed Access | Times Cited: 14
A novel data-driven machine learning techniques to predict compressive strength of fly ash and recycled coarse aggregates based self-compacting concrete
Surbhi Gupta Aggarwal, Rajwinder Singh, Ayush Rathore, et al.
Materials Today Communications (2024) Vol. 39, pp. 109294-109294
Closed Access | Times Cited: 10
Prediction of ultimate strength and strain in FRP wrapped oval shaped concrete columns using machine learning
Li Shang, Haytham F. Isleem, Walaa J K Almoghayer, et al.
Scientific Reports (2025) Vol. 15, Iss. 1
Open Access | Times Cited: 1
Predicting the Splitting Tensile Strength of Recycled Aggregate Concrete Using Individual and Ensemble Machine Learning Approaches
Zhu Yongzhong, Ayaz Ahmad, Waqas Ahmad, et al.
Crystals (2022) Vol. 12, Iss. 5, pp. 569-569
Open Access | Times Cited: 37
Use of Artificial Intelligence for Predicting Parameters of Sustainable Concrete and Raw Ingredient Effects and Interactions
Muhammad Nasir Amin, Waqas Ahmad, Kaffayatullah Khan, et al.
Materials (2022) Vol. 15, Iss. 15, pp. 5207-5207
Open Access | Times Cited: 37
Utilizing machine learning approaches within concrete technology offers an intelligent perspective towards sustainability in the construction industry: a comprehensive review
Suhaib Rasool Wani, Manju Suthar
Multiscale and Multidisciplinary Modeling Experiments and Design (2024) Vol. 8, Iss. 1
Closed Access | Times Cited: 8
Application of novel deep neural network on prediction of compressive strength of fly ash based concrete
Rahul Biswas, Manish Kumar, Divesh Ranjan Kumar, et al.
Nondestructive Testing And Evaluation (2024), pp. 1-31
Closed Access | Times Cited: 8
Optimizing machine learning techniques and SHapley Additive exPlanations (SHAP) analysis for the compressive property of self-compacting concrete
Zhiyuan Wang, Huihui Liu, Muhammad Nasir Amin, et al.
Materials Today Communications (2024) Vol. 39, pp. 108804-108804
Closed Access | Times Cited: 7
Intelligent Design of Building Materials: Development of an AI-Based Method for Cement-Slag Concrete Design
Fei Zhu, Xiangping Wu, Mengmeng Zhou, et al.
Materials (2022) Vol. 15, Iss. 11, pp. 3833-3833
Open Access | Times Cited: 26
Interpretable Dynamic Ensemble Selection Approach for the Prediction of Road Traffic Injury Severity: A Case Study of Pakistan’s National Highway N-5
Afaq Khattak, Hamad Almujibah, Ahmed S. Elamary, et al.
Sustainability (2022) Vol. 14, Iss. 19, pp. 12340-12340
Open Access | Times Cited: 21
Experimental and machine learning approaches to investigate the application of sugarcane bagasse ash as a partial replacement of fine aggregate for concrete production
Rajwinder Singh, Mahesh Patel
Journal of Building Engineering (2023) Vol. 76, pp. 107168-107168
Closed Access | Times Cited: 12
Data-driven approaches for strength prediction of alkali-activated composites
Mohammed Awad Abuhussain, Ayaz Ahmad, Muhammad Nasir Amin, et al.
Case Studies in Construction Materials (2024) Vol. 20, pp. e02920-e02920
Open Access | Times Cited: 4
Application of Soft-Computing Methods to Evaluate the Compressive Strength of Self-Compacting Concrete
Muhammad Nasir Amin, Mohammed Najeeb Al-Hashem, Ayaz Ahmad, et al.
Materials (2022) Vol. 15, Iss. 21, pp. 7800-7800
Open Access | Times Cited: 18
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