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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:

Fly ash-based geopolymer concrete's compressive strength estimation by applying artificial intelligence methods
Gholamreza Pazouki
Measurement (2022) Vol. 203, pp. 111916-111916
Closed Access | Times Cited: 29

Showing 1-25 of 29 citing articles:

Engineering properties, sustainability performance and life cycle assessment of high strength self-compacting geopolymer concrete composites
Balamurali Kanagaraj, N. Anand, U. Johnson Alengaram, et al.
Construction and Building Materials (2023) Vol. 388, pp. 131613-131613
Closed Access | Times Cited: 49
AI-driven critical parameter optimization of sustainable self-compacting geopolymer concrete
Suraj Kumar Parhi, Saswat Dwibedy, Saubhagya Kumar Panigrahi
Journal of Building Engineering (2024) Vol. 86, pp. 108923-108923
Closed Access | Times Cited: 29
A critical review on modeling and prediction on properties of fresh and hardened geopolymer composites
Peng Zhang, Yifan Mao, Weisuo Yuan, et al.
Journal of Building Engineering (2024) Vol. 88, pp. 109184-109184
Closed Access | Times Cited: 23
Development of ANN-based metaheuristic models for the study of the durability characteristics of high-volume fly ash self-compacting concrete with silica fume
Shashikant Kumar, Divesh Ranjan Kumar, Warit Wipulanusat, et al.
Journal of Building Engineering (2024) Vol. 94, pp. 109844-109844
Closed Access | Times Cited: 22
Eco-friendly mix design of slag-ash-based geopolymer concrete using explainable deep learning
R.S.S. Ranasinghe, W.K.V.J.B. Kulasooriya, Udara Sachinthana Perera, et al.
Results in Engineering (2024) Vol. 23, pp. 102503-102503
Open Access | Times Cited: 21
Predictive models in machine learning for strength and life cycle assessment of concrete structures
A. Dinesh, B. Rahul Prasad
Automation in Construction (2024) Vol. 162, pp. 105412-105412
Closed Access | Times Cited: 19
Hybrid portland cement-slag-based geopolymer mortar: Strength, microstructural and environmental assessment
Ceren Kına, Harun Tanyıldızı, Volkan Açik
Process Safety and Environmental Protection (2025) Vol. 195, pp. 106771-106771
Closed Access | Times Cited: 3
Artificial intelligence-based prediction of strengths of slag-ash-based geopolymer concrete using deep neural networks
Solomon Oyebisi, Thamer Alomayri
Construction and Building Materials (2023) Vol. 400, pp. 132606-132606
Open Access | Times Cited: 35
Integrating PZT-enabled active sensing with deep learning techniques for automatic monitoring and assessment of early-age concrete strength
Xiaolong Liao, Qixiang Yan, Haojia Zhong, et al.
Measurement (2023) Vol. 211, pp. 112657-112657
Closed Access | Times Cited: 28
Prediction of Mechanical Properties of Rubberized Concrete Incorporating Fly Ash and Nano Silica by Artificial Neural Network Technique
Musa Adamu, Andaç Batur Çolak, Yasser E. Ibrahim, et al.
Axioms (2023) Vol. 12, Iss. 1, pp. 81-81
Open Access | Times Cited: 24
Evaluation and characteristic analysis of compressive strength and resistivity of EG cement conductive mortar based upon hybrid-BP neural network
Penghui Wang, Biqin Dong, Yuanyuan Zhang
Construction and Building Materials (2023) Vol. 394, pp. 132203-132203
Closed Access | Times Cited: 14
Evaluating the rapid chloride permeability of self-compacting concrete containing fly ash and silica fume exposed to different temperatures: An artificial intelligence framework
Ramin Kazemi, Aliakbar Gholampour
Construction and Building Materials (2023) Vol. 409, pp. 133835-133835
Closed Access | Times Cited: 14
Prediction of compressive strengths of pumice-and diatomite-containing cement mortars with artificial intelligence-based applications
Burak Koçak, İbrahim Pınarcı, Uğur Güvenç, et al.
Construction and Building Materials (2023) Vol. 385, pp. 131516-131516
Closed Access | Times Cited: 13
Application of machine learning algorithm in the internal and external hazards from industrial byproducts
Solomon Oyebisi, H. I. Owamah, Maxwell Omeje
Cleaner Engineering and Technology (2023) Vol. 13, pp. 100629-100629
Open Access | Times Cited: 11
Using artificial intelligence methods to predict the compressive strength of concrete containing sugarcane bagasse ash
Gholamreza Pazouki, Zhong Tao, Nariman Saeed, et al.
Construction and Building Materials (2023) Vol. 409, pp. 134047-134047
Open Access | Times Cited: 10
Artificial Intelligence in Geopolymer Concrete Mix Design: A Comprehensive Review of Techniques and Applications
Malik Mushthofa, John Thedy, Mochamad Teguh, et al.
Iranian Journal of Science and Technology Transactions of Civil Engineering (2025)
Closed Access
Intelligent computing hybrid models on estimating the consolidation settlement of shallow foundations
J. Jagan, Pijush Samui
Multiscale and Multidisciplinary Modeling Experiments and Design (2024) Vol. 7, Iss. 4, pp. 3579-3596
Closed Access | Times Cited: 3
Compressive and tensile strength estimation of sustainable geopolymer concrete using contemporary boosting ensemble techniques
Ji Zhou, Qiong Tian, Ayaz Ahmad, et al.
REVIEWS ON ADVANCED MATERIALS SCIENCE (2024) Vol. 63, Iss. 1
Open Access | Times Cited: 3
Mechanical properties of sustainable metakaolin/Rockwool based geopolymer mortar
Hasan Saadatmand, Behnam Zehtab, Hossein Ghayoor Najafabadi, et al.
Innovative Infrastructure Solutions (2024) Vol. 9, Iss. 7
Closed Access | Times Cited: 3
Potential application of artificial intelligence to the alpha and gamma radiation from agricultural byproducts used as building and construction materials
Solomon Oyebisi, Thamer Alomayri
Scientific African (2023) Vol. 21, pp. e01792-e01792
Open Access | Times Cited: 7
AI-Assisted Geopolymer Concrete Mix Design: A Step Towards Sustainable Construction
Md. Zia ul Haq, Hemant Sood, Rajesh Kumar
Communications in computer and information science (2023), pp. 331-341
Closed Access | Times Cited: 7
Experimenting the influence of corncob ash on the mechanical strength of slag-based geopolymer concrete
Jing Wang, Qian Qu, Suleman Ayub Khan, et al.
REVIEWS ON ADVANCED MATERIALS SCIENCE (2024) Vol. 63, Iss. 1
Open Access | Times Cited: 2
Modeling the strength parameters of agro waste-derived geopolymer concrete using advanced machine intelligence techniques
Ahmed A. Alawi Al-Naghi, Muhammad Nasir Amin, Suleman Ayub Khan, et al.
REVIEWS ON ADVANCED MATERIALS SCIENCE (2024) Vol. 63, Iss. 1
Open Access | Times Cited: 2
Evaluation and prediction of slag-based geopolymer's compressive strength using design of experiment (DOE) approach and artificial neural network (ANN) algorithms
Rami Al‐Sughayer, Hunain Alkhateb, Hakan Yasarer, et al.
Construction and Building Materials (2024) Vol. 440, pp. 137322-137322
Closed Access | Times Cited: 2
Implementation of artificial intelligence to the prediction of the mechanical properties of concrete: A review
A. Dinesh, B. Kamal, M. Akash, et al.
Materials Today Proceedings (2023)
Closed Access | Times Cited: 4
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