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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:

A systematic review and assessment of concrete strength prediction models
Mylvaganam Nithurshan, Yogarajah Elakneswaran
Case Studies in Construction Materials (2023) Vol. 18, pp. e01830-e01830
Open Access | Times Cited: 30

Showing 1-25 of 30 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
Sustainable mix design and carbon emission analysis of recycled aggregate concrete based on machine learning and big data methods
Boqun Zhang, Lei Pan, X. C. Chang, et al.
Journal of Cleaner Production (2025) Vol. 489, pp. 144734-144734
Closed Access | Times Cited: 2
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
Exploring the reinforcing mechanism of graphene oxide in cementitious materials through microstructural analysis of synthesised calcium silicate hydrate
Mylvaganam Nithurshan, Yogarajah Elakneswaran, Yuya Yoda, et al.
Cement and Concrete Composites (2024) Vol. 153, pp. 105717-105717
Closed Access | Times Cited: 7
Development of a Machine Learning (ML)-Based Computational Model to Estimate the Engineering Properties of Portland Cement Concrete (PCC)
Rodrigo Polo-Mendoza, Gilberto Martínez-Arguelles, Rita Peñabaena‐Niebles, et al.
Arabian Journal for Science and Engineering (2024) Vol. 49, Iss. 10, pp. 14351-14365
Open Access | Times Cited: 5
A comprehensive study on predicting the elastic modulus and Poisson ratio of hardened cement pastes via micro-scale cement hydration simulations
Van Thong Nguyen, Seon Yeol Lee, Seyoon Yoon, et al.
Case Studies in Construction Materials (2024) Vol. 20, pp. e03285-e03285
Open Access | Times Cited: 5
A comprehensive review of hydrophobic concrete: surface and bulk modifications for enhancing corrosion resistance
Joseph Gnanaraj S, K Vasugi
Engineering Research Express (2024) Vol. 6, Iss. 3, pp. 032101-032101
Closed Access | Times Cited: 4
Comparative study on the prediction of the unconfined compressive strength of the one-part geopolymer stabilized soil by using different hybrid machine learning models
Qinyi Chen, Guo Hu, Jun Wu
Case Studies in Construction Materials (2024) Vol. 21, pp. e03439-e03439
Open Access | Times Cited: 4
Machine Learning Approaches for Predicting Compressive and Shear Strength of EB FRP-Reinforced Concrete Elements: A Comprehensive Review
Ali Benzaamia, Mohamed Ghrici, Redouane Rebouh
Studies in systems, decision and control (2024), pp. 221-249
Closed Access | Times Cited: 4
Predicting the tensile strength of ultra-high performance concrete: New insights into the synergistic effects of steel fiber geometry and distribution
Zichao Que, Jinhui Tang, Huinan Wei, et al.
Construction and Building Materials (2024) Vol. 444, pp. 137822-137822
Closed Access | Times Cited: 4
Evaluation of the strength and brittleness of mortar containing fissures and rubber
Jun Yu, Yilin Yao, Tengfei Xu, et al.
Construction and Building Materials (2025) Vol. 464, pp. 140091-140091
Closed Access
Optimization and Prediction of Colored Pervious Concrete Properties: Enhancing Performance through Augmented Grey Wolf Optimizer and Artificial Neural Networks
Ahmet Tugrul Koc, Sadık Alper Yıldızel
Materials Today Communications (2025), pp. 112069-112069
Closed Access
Compliance of Cement Concrete Produced Based on Previous Prepared Job-Mix Formula: Case Study
Nadheer Albayati
Emerging technologies and engineering journal. (2025), pp. 2-2
Open Access
Sawdust as a Sustainable Additive: Comparative Insights into its Role in Concrete and Brick Applications
Esakki Priya, P. Vasanthi, B Prabhu, et al.
Cleaner Waste Systems (2025), pp. 100286-100286
Open Access
RECENT ADVANCES IN CEMENT CHEMISTRY AND APPLICATIONS: A REVIEW
Ikechkwu O. Alisi, Abuhuraira Ado Musa, A. Jacob
FUDMA Journal of Sciences (2025) Vol. 9, pp. 301-310
Closed Access
Prediction of concrete strength considering thermal damage using a modified strength-maturity model
Ling Wang, Hao Zhou, Junfei Zhang, et al.
Construction and Building Materials (2023) Vol. 400, pp. 132779-132779
Closed Access | Times Cited: 9
Multiscale computational modelling of nano-silica reinforced cement paste: Bridging microstructure and mechanical performance
Mylvaganam Nithurshan, Yogarajah Elakneswaran, Yuya Yoda, et al.
Construction and Building Materials (2024) Vol. 425, pp. 136047-136047
Closed Access | Times Cited: 3
Harnessing machine learning for accurate estimation of concrete strength using non-destructive tests: a comparative study
Iman Kattoof Harith, Mohammed Al-Rubaye, Ahmed M. Abdulhadi, et al.
Multiscale and Multidisciplinary Modeling Experiments and Design (2024) Vol. 8, Iss. 1
Closed Access | Times Cited: 3
A Review of Strength and Durability Testing and Artificial Intelligence Prediction Methods for Various Fiber‐Reinforced Concretes
N. S., P. Kaythry, P. Sangeetha
The Structural Design of Tall and Special Buildings (2025) Vol. 34, Iss. 7
Closed Access
Machine Learning-Based Prediction of the Compressive Strength of Brazilian Concretes: A Dual-Dataset Study
Vitor Pereira Silva, Ruan de Alencar Carvalho, João Henrique da Silva Rêgo, et al.
Materials (2023) Vol. 16, Iss. 14, pp. 4977-4977
Open Access | Times Cited: 7
Microstructure-Informed Deep Learning Model for Accurate Prediction of Multiple Concrete Properties
Ye Li, Yiming Ma, Kang Hai Tan, et al.
Journal of Building Engineering (2024) Vol. 98, pp. 111339-111339
Closed Access | Times Cited: 2
Comprehensive analysis of machine learning models for predicting concrete compressive strength
Saidjon Kamolov
Annals of Mathematics and Computer Science (2024) Vol. 23, pp. 119-130
Open Access
Concrete Strength and Aggregate Properties: In-Depth Analysis of Four Sources
Kamal Hosen, Md Abdulla Al Maruf, Rayhan Howlader, et al.
Civil Engineering Journal (2024) Vol. 10, Iss. 4, pp. 1254-1264
Open Access
Evaluation of the Strength and Brittleness of Mortar Containing Fissures and Rubber
Jun Yu, Yilin Yao, Tengfei Xu, et al.
(2024)
Closed Access
Prediction of the Unconfined Compressive Strength of a One-Part Geopolymer-Stabilized Soil Using Deep Learning Methods with Combined Real and Synthetic Data
Qinyi Chen, Guo Hu, Jun Wu
Buildings (2024) Vol. 14, Iss. 9, pp. 2894-2894
Open Access
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