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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:

Evaluating the mechanical strength prediction performances of fly ash-based MPC mortar with artificial intelligence approaches
M. Aminul Haque, Bing Chen, Muhammad Faisal Javed, et al.
Journal of Cleaner Production (2022) Vol. 355, pp. 131815-131815
Closed Access | Times Cited: 43

Showing 1-25 of 43 citing articles:

Comparative measurements of fracture toughgness combined with visual analysis of cracks propagation using the DIC technique of concretes based on cement matrix with a highly diversified composition
Grzegorz Ludwik Golewski
Theoretical and Applied Fracture Mechanics (2022) Vol. 121, pp. 103553-103553
Open Access | Times Cited: 87
An extensive investigations on fracture parameters of concretes based on quaternary binders (QBC) by means of the DIC technique
Grzegorz Ludwik Golewski
Construction and Building Materials (2022) Vol. 351, pp. 128823-128823
Closed Access | Times Cited: 85
Assessing of water absorption on concrete composites containing fly ash up to 30 % in regards to structures completely immersed in water
Grzegorz Ludwik Golewski
Case Studies in Construction Materials (2023) Vol. 19, pp. e02337-e02337
Open Access | Times Cited: 76
Optimization design for alkali-activated slag-fly ash geopolymer concrete based on artificial intelligence considering compressive strength, cost, and carbon emission
Yue Li, Jiale Shen, Hui Lin, et al.
Journal of Building Engineering (2023) Vol. 75, pp. 106929-106929
Closed Access | Times Cited: 68
The Effect of the Addition of Coal Fly Ash (CFA) on the Control of Water Movement within the Structure of the Concrete
Grzegorz Ludwik Golewski
Materials (2023) Vol. 16, Iss. 15, pp. 5218-5218
Open Access | Times Cited: 68
Examination of water absorption of low volume fly ash concrete (LVFAC) under water immersion conditions
Grzegorz Ludwik Golewski
Materials Research Express (2023) Vol. 10, Iss. 8, pp. 085505-085505
Open Access | Times Cited: 47
The study of effect of carbon nanotubes on the compressive strength of cement-based materials based on machine learning
Yue Li, Hongwen Li, Caiyun Jin, et al.
Construction and Building Materials (2022) Vol. 358, pp. 129435-129435
Closed Access | Times Cited: 46
Modeling and analysis of creep in concrete containing supplementary cementitious materials based on machine learning
Yunze Liu, Yue Li, Jinlei Mu, et al.
Construction and Building Materials (2023) Vol. 392, pp. 131911-131911
Closed Access | Times Cited: 26
Development of autogenous shrinkage prediction model of alkali-activated slag-fly ash geopolymer based on machine learning
Jiale Shen, Yue Li, Hui Lin, et al.
Journal of Building Engineering (2023) Vol. 71, pp. 106538-106538
Closed Access | Times Cited: 25
Machine learning based prediction models for spilt tensile strength of fiber reinforced recycled aggregate concrete
Mohammed Alarfaj, Hisham Jahangir Qureshi, Muhammad Zubair Shahab, et al.
Case Studies in Construction Materials (2024) Vol. 20, pp. e02836-e02836
Open Access | Times Cited: 9
Machine learning based modeling for predicting the compressive strength of solid waste material-incorporated Magnesium Phosphate Cement
Xiao Luo, Yue Li, Qiuao Wang, et al.
Journal of Cleaner Production (2024) Vol. 442, pp. 141172-141172
Closed Access | Times Cited: 8
Evaluation of properties of bio-composite with interpretable machine learning approaches: optimization and hyper tuning
Guiying Xu, Gengxin Zhou, Fadi Althoey, et al.
Journal of Materials Research and Technology (2023) Vol. 25, pp. 1421-1446
Open Access | Times Cited: 21
Toughness and strength of PVA-fibre reinforced magnesium phosphate cement (FRMPC) within 24 h
Jie Liu, Changwang Yan, Shuguang Liu, et al.
Construction and Building Materials (2024) Vol. 418, pp. 135339-135339
Closed Access | Times Cited: 7
Alkali-activated binder concrete strength prediction using hybrid-deep learning along with shapely additive explanations and uncertainty analysis
Pobithra Das, Abul Kashem, Mominul Islam, et al.
Construction and Building Materials (2024) Vol. 435, pp. 136711-136711
Closed Access | Times Cited: 5
Design of sustainable mortar incorporating construction and demolition waste through adaptive experiments accelerated by machine learning
Thomas Tawiah Baah, Hang Zeng, Marat I. Latypov, et al.
Results in Engineering (2025), pp. 104264-104264
Open Access
New Types and Dosages for the Manufacture of Low-Energy Cements from Raw Materials and Industrial Waste under the Principles of the Circular Economy and Low-Carbon Economy
Sergio Martínez‐Martínez, Luis Pérez‐Villarejo, Dolores Eliche‐Quesada, et al.
Materials (2023) Vol. 16, Iss. 2, pp. 802-802
Open Access | Times Cited: 15
Positive impact of polymer impregnated on the enhancement of the physico-mechanical characteristics and thermal resistance of high-volume fly-ash blended cement
Sahar M. Ibrahim, Mohamed Heikal, Amal M. Metwally, et al.
Construction and Building Materials (2023) Vol. 381, pp. 131243-131243
Closed Access | Times Cited: 14
Development of prediction models for strength properties of concrete using gene expression programming
Asad U. Khan, Muhammad Faisal Javed, Majid Khan
Innovative Infrastructure Solutions (2025) Vol. 10, Iss. 3
Closed Access
Green Optimization of Vapour-Cured Geopolymer Mortars: Predicting Cost, Mechanical Properties, and Environmental Impact with Artificial Neural Networks
Serhat Çeli̇kten, Bilal Baran, Mustafa Sarıdemir
Cleaner Waste Systems (2025), pp. 100270-100270
Open Access
Research on predicting compressive strength of magnesium silicate hydrate cement based on machine learning
Xiao Luo, Yue Li, Hui Lin, et al.
Construction and Building Materials (2023) Vol. 406, pp. 133412-133412
Closed Access | Times Cited: 12
Optimization of mix proportion and strength prediction of magnesium phosphate cement-based composites based on machine learning
Jing Zhang, Tao Li, Yuanyuan Yao, et al.
Construction and Building Materials (2023) Vol. 411, pp. 134738-134738
Closed Access | Times Cited: 11
The data-driven research on bond strength between fly ash-based geopolymer concrete and reinforcing bars
Yue Li, Jiale Shen, Hui Lin, et al.
Construction and Building Materials (2022) Vol. 357, pp. 129384-129384
Closed Access | Times Cited: 16
Predicting the impermeability and mechanical properties of manufactured sand polymer waterproof mortar using an optimised back-propagation neural network
Haozheng Tian, Hongxia Qiao, Yunsheng Zhang, et al.
Construction and Building Materials (2024) Vol. 441, pp. 137475-137475
Closed Access | Times Cited: 3
Compressive Strength Estimation of Rice Husk Ash-Blended Concrete Using Deep Neural Network Regression with an Asymmetric Loss Function
Nhat‐Duc Hoang
Iranian Journal of Science and Technology Transactions of Civil Engineering (2022) Vol. 47, Iss. 3, pp. 1547-1565
Closed Access | Times Cited: 14
Transient flow analysis for pumping system comprising pressure vessel using unsteady friction model
Kashif Mehmood, Boran Zhang, Fazal E. Jalal, et al.
International Journal of Mechanical Sciences (2022) Vol. 244, pp. 108093-108093
Closed Access | Times Cited: 14
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