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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:

Assessment of Artificial Intelligence Strategies to Estimate the Strength of Geopolymer Composites and Influence of Input Parameters
Kaffayatullah Khan, Waqas Ahmad, Muhammad Nasir Amin, et al.
Polymers (2022) Vol. 14, Iss. 12, pp. 2509-2509
Open Access | Times Cited: 32

Showing 1-25 of 32 citing articles:

Machine learning interpretable-prediction models to evaluate the slump and strength of fly ash-based geopolymer
Sohaib Nazar, Jian Yang, Muhammad Nasir Amin, et al.
Journal of Materials Research and Technology (2023) Vol. 24, pp. 100-124
Open Access | Times Cited: 62
Compressive strength prediction of one-part alkali activated material enabled by interpretable machine learning
Syed Farasat Ali Shah, Bing Chen, Muhammad Zahid, et al.
Construction and Building Materials (2022) Vol. 360, pp. 129534-129534
Closed Access | Times Cited: 50
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: 30
Evaluating the effectiveness of waste glass powder for the compressive strength improvement of cement mortar using experimental and machine learning methods
Kaffayatullah Khan, Waqas Ahmad, Muhammad Nasir Amin, et al.
Heliyon (2023) Vol. 9, Iss. 5, pp. e16288-e16288
Open Access | Times Cited: 29
An evolutionary machine learning-based model to estimate the rheological parameters of fresh concrete
Sohaib Nazar, Jian Yang, Muhammad Faisal Javed, et al.
Structures (2023) Vol. 48, pp. 1670-1683
Closed Access | Times Cited: 28
Experimental and machine learning approaches to investigate the effect of waste glass powder on the flexural strength of cement mortar
Muhammad Nasir Amin, Hassan Ali Alkadhim, Waqas Ahmad, et al.
PLoS ONE (2023) Vol. 18, Iss. 1, pp. e0280761-e0280761
Open Access | Times Cited: 26
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: 23
Critical review on the application of artificial intelligence techniques in the production of geopolymer-concrete
George Uwadiegwu Alaneme, Kolawole Adisa Olonade, Ebenezer Esenogho
SN Applied Sciences (2023) Vol. 5, Iss. 8
Open Access | Times Cited: 23
Crack width prediction of self-healing engineered cementitious composite using multi-expression programming
Fadi Althoey, Nadhim Hamah Sor, Haitham Hadidi, et al.
Journal of Materials Research and Technology (2023) Vol. 24, pp. 918-927
Open Access | Times Cited: 22
Strength Estimation and Feature Interaction of Carbon Nanotubes-Modified Concrete Using Artificial Intelligence-Based Boosting Ensembles
Fei Zhu, Xiangping Wu, Yijun Lü, et al.
Buildings (2024) Vol. 14, Iss. 1, pp. 134-134
Open Access | Times Cited: 13
A comparative analysis of tree-based machine learning algorithms for predicting the mechanical properties of fibre-reinforced GGBS geopolymer concrete
Shimol Philip, M. Nidhi, Hemn Unis Ahmed
Multiscale and Multidisciplinary Modeling Experiments and Design (2024) Vol. 7, Iss. 3, pp. 2555-2583
Closed Access | Times Cited: 11
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
Predicting the crack width of the engineered cementitious materials via standard machine learning algorithms
Xiongzhou Yuan, Qingyu Cao, Muhammad Nasir Amin, et al.
Journal of Materials Research and Technology (2023) Vol. 24, pp. 6187-6200
Open Access | Times Cited: 18
Sustainable strategy of eggshell waste usage in cementitious composites: An integral testing and computational study for compressive behavior in aggressive environment
Nanlan Wang, Zhengjun Xia, Muhammad Nasir Amin, et al.
Construction and Building Materials (2023) Vol. 386, pp. 131536-131536
Closed Access | Times Cited: 18
Proposition of geopolymers obtained through the acid activation of iron ore tailings with phosphoric acid
Aldo Ribeiro de Carvalho, Bianca Rafaela da Silva Calderón-Morales, José Carlos Borba Júnior, et al.
Construction and Building Materials (2023) Vol. 403, pp. 133078-133078
Closed Access | Times Cited: 16
Artificial intelligence prediction of the mechanical properties of banana peel-ash and bagasse blended geopolymer concrete
George Uwadiegwu Alaneme, Kolawole Adisa Olonade, Ebenezer Esenogho, et al.
Scientific Reports (2024) Vol. 14, Iss. 1
Open Access | Times Cited: 5
Boosting-Based Machine Learning Applications in Polymer Science: A Review
Ivan Malashin, В С Тынченко, Andrei Gantimurov, et al.
Polymers (2025) Vol. 17, Iss. 4, pp. 499-499
Open Access
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
Analyzing the Behavior of Geopolymer Concrete with Different Novel Machine-Learning Algorithms
Sanjog Chhetri Sapkota, Dipak Dahal, Ajay Kumar Yadav, et al.
Journal of structural design and construction practice. (2025) Vol. 30, Iss. 3
Closed Access
Low-carbon embodied alkali-activated materials for sustainable construction: A comparative study of single and ensemble learners
Muhammad Nasir Amin, Suleman Ayub Khan, Ahmed A. Alawi Al-Naghi, et al.
REVIEWS ON ADVANCED MATERIALS SCIENCE (2024) Vol. 63, Iss. 1
Open Access | Times Cited: 4
Promoting the suitability of rice husk ash concrete in the building sector via contemporary machine intelligence techniques
Muhammad Nasir Amin, Suleman Ayub Khan, Kaffayatullah Khan, et al.
Case Studies in Construction Materials (2023) Vol. 19, pp. e02357-e02357
Open Access | Times Cited: 9
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: 14
Comparison of Models to Predict Mechanical Properties of FR-AM Composites and a Fractographical Study
Juan León-Becerra, Octavio Andrés González‐Estrada, Heller Sánchez-Acevedo
Polymers (2022) Vol. 14, Iss. 17, pp. 3546-3546
Open Access | Times Cited: 12
An integral approach for testing and computational analysis of glass powder in cementitious composites
Muhammad Nasir Amin, Sohaib Nazar, Mohammed Najeeb Al-Hashem, et al.
Case Studies in Construction Materials (2023) Vol. 18, pp. e02063-e02063
Open Access | Times Cited: 7
Investigating the rheological characteristics of alkali-activated concrete using contemporary artificial intelligence approaches
Muhammad Nasir Amin, Ahmed A. Alawi Al-Naghi, Roz‐Ud‐Din Nassar, et al.
REVIEWS ON ADVANCED MATERIALS SCIENCE (2024) Vol. 63, Iss. 1
Open Access | Times Cited: 2
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