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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:

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

Showing 1-25 of 37 citing articles:

Smart materials and technologies for sustainable concrete construction
Jonny Nilimaa
Developments in the Built Environment (2023) Vol. 15, pp. 100177-100177
Open Access | Times Cited: 166
A data-driven approach to predict the compressive strength of alkali-activated materials and correlation of influencing parameters using SHapley Additive exPlanations (SHAP) analysis
Xinliang Zheng, Yi Xie, Xujiao Yang, et al.
Journal of Materials Research and Technology (2023) Vol. 25, pp. 4074-4093
Open Access | Times Cited: 34
Experimental investigation and AI prediction modelling of ceramic waste powder concrete – An approach towards sustainable construction
Jianyu Yang, Pengxiao Jiang, Roz‐Ud‐Din Nassar, et al.
Journal of Materials Research and Technology (2023) Vol. 23, pp. 3676-3696
Open Access | Times Cited: 28
A comprehensive GEP and MEP analysis of a cement-based concrete containing metakaolin
Muhammad Iftikhar Faraz, Siyab Ul Arifeen, Muhammad Nasir Amin, et al.
Structures (2023) Vol. 53, pp. 937-948
Closed Access | Times Cited: 24
A machine learning-based genetic programming approach for the sustainable production of plastic sand paver blocks
Bawar Iftikhar, Sophia C. Alih, Mohammadreza Vafaei, et al.
Journal of Materials Research and Technology (2023) Vol. 25, pp. 5705-5719
Open Access | Times Cited: 24
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
Engineered geopolymer composites: a comprehensive state-of-the-art review on materials’ perspective
K. K. Yaswanth, Komma Hemanth Kumar Reddy, N. Anusha, et al.
Archives of Civil and Mechanical Engineering (2024) Vol. 24, Iss. 3
Closed Access | Times Cited: 9
A critical analysis of compressive strength prediction of glass fiber and carbon fiber reinforced concrete over machine learning models
K. K. Yaswanth, V. S. Vani, Krupasindhu Biswal, et al.
Multiscale and Multidisciplinary Modeling Experiments and Design (2025) Vol. 8, Iss. 3
Closed Access | Times Cited: 1
Sustainable use of waste eggshells in cementitious materials: An experimental and modeling-based study
Fahad Alsharari, Kaffayatullah Khan, Muhammad Nasir Amin, et al.
Case Studies in Construction Materials (2022) Vol. 17, pp. e01620-e01620
Open Access | Times Cited: 33
Machine learning models to predict the relationship between printing parameters and tensile strength of 3D Poly (lactic acid) scaffolds for tissue engineering applications
Duygu Ege, Seda Sertturk, Berk Acarkan, et al.
Biomedical Physics & Engineering Express (2023) Vol. 9, Iss. 6, pp. 065014-065014
Open Access | Times Cited: 20
Application of machine learning algorithms to evaluate the influence of various parameters on the flexural strength of ultra-high-performance concrete
Yunfeng Qian, Muhammad Sufian, Ahmad Hakamy, et al.
Frontiers in Materials (2023) Vol. 9
Open Access | Times Cited: 15
Predicting parameters and sensitivity assessment of nano-silica-based fiber-reinforced concrete: a sustainable construction material
Muhammad Nasir Amin, Kaffayatullah Khan, Muhammad Sufian, et al.
Journal of Materials Research and Technology (2023) Vol. 23, pp. 3943-3960
Open Access | Times Cited: 15
Comparing the efficacy of GEP and MEP algorithms in predicting concrete strength incorporating waste eggshell and waste glass powder
Dong Wang, Muhammad Nasir Amin, Kaffayatullah Khan, et al.
Developments in the Built Environment (2024) Vol. 17, pp. 100361-100361
Open Access | Times Cited: 6
Prediction of compressive strength of recycled concrete using gradient boosting models
Amira Hamdy Ali Ahmed, Jin‐Ming Wu, Mosaad Ali Hussein Ali
Ain Shams Engineering Journal (2024) Vol. 15, Iss. 9, pp. 102975-102975
Open Access | Times Cited: 6
Eco-Friendly Design and Sustainability Assessments of Fibre-Reinforced High-Strength Concrete Structures Automated by Data-Driven Machine Learning Models
Xia Qin, Sakdirat Kaewunruen
Sustainability (2023) Vol. 15, Iss. 8, pp. 6640-6640
Open Access | Times Cited: 12
Implications of Construction 4.0 technologies to enhancing well-being: a fuzzy TOPSIS evaluation
Hazwani Shafei, Rahimi A. Rahman, Yong Siang Lee, et al.
Construction Innovation (2024)
Closed Access | Times Cited: 4
Predictive performance assessment of recycled coarse aggregate concrete using artificial intelligence: A review
P. Anitha Kumari, Sagar Paruthi, Ahmad Alyaseen, et al.
Cleaner Materials (2024) Vol. 13, pp. 100263-100263
Open Access | Times Cited: 4
Machine learning-based price prediction for thermal insulation materials: A holistic approach integrating thermophysical, technical, and environmental attributes in the Greek construction market
Konstantinos Papachatzis
Energy and Buildings (2024) Vol. 324, pp. 114899-114899
Closed Access | Times Cited: 4
In-Depth Analysis of Cement-Based Material Incorporating Metakaolin Using Individual and Ensemble Machine Learning Approaches
Abdulrahman Mohamad Radwan Bulbul, Kaffayatullah Khan, Afnan Nafees, et al.
Materials (2022) Vol. 15, Iss. 21, pp. 7764-7764
Open Access | Times Cited: 15
A Review on Pollution Treatment in Cement Industrial Areas: From Prevention Techniques to Python-Based Monitoring and Controlling Models
Xinghan Zhu, Jinzhong Yang, Qifei Huang, et al.
Processes (2022) Vol. 10, Iss. 12, pp. 2682-2682
Open Access | Times Cited: 15
Significant applications of Artificial Intelligence towards attaining Sustainability
Abid Haleem, Mohd Javaid, Ibrahim Haleem Khan, et al.
Journal of Industrial Integration and Management (2023) Vol. 08, Iss. 04, pp. 489-520
Closed Access | Times Cited: 8
Data-Driven Techniques for Evaluating the Mechanical Strength and Raw Material Effects of Steel Fiber-Reinforced Concrete
Mohammed Najeeb Al-Hashem, Muhammad Nasir Amin, Waqas Ahmad, et al.
Materials (2022) Vol. 15, Iss. 19, pp. 6928-6928
Open Access | Times Cited: 12
Machine learning-based evaluation of parameters of high-strength concrete and raw material interaction at elevated temperatures
Gongmei Chen, Salman Ali Suhail, Alireza Bahrami, et al.
Frontiers in Materials (2023) Vol. 10
Open Access | Times Cited: 7
Strength predictive models of cementitious matrix by hybrid intrusion of nano and micro silica: Hyper-tuning with ensemble approaches
Guoliang Liu, Hongzhi Zhao, Muhammad Nasir Amin, et al.
Journal of Materials Research and Technology (2023) Vol. 26, pp. 1808-1832
Open Access | Times Cited: 6
Comparative analysis of intelligent models for predicting compressive strength in recycled aggregate concrete
Amira Hamdy Ali Ahmed, Jin Wu, Mosaad Ali Hussein Ali
Modeling Earth Systems and Environment (2024) Vol. 10, Iss. 4, pp. 5273-5291
Closed Access | Times Cited: 2
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