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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:

Predictive modeling for compressive strength of 3D printed fiber-reinforced concrete using machine learning algorithms
Mana Alyami, Majid Khan, Muhammad Fawad, et al.
Case Studies in Construction Materials (2023) Vol. 20, pp. e02728-e02728
Open Access | Times Cited: 49

Showing 1-25 of 49 citing articles:

Estimating compressive strength of concrete containing rice husk ash using interpretable machine learning-based models
Mana Alyami, Roz‐Ud‐Din Nassar, Majid Khan, et al.
Case Studies in Construction Materials (2024) Vol. 20, pp. e02901-e02901
Open Access | Times Cited: 35
Forecasting the strength of graphene nanoparticles-reinforced cementitious composites using ensemble learning algorithms
Majid Khan, Roz‐Ud‐Din Nassar, Waqar Anwar, et al.
Results in Engineering (2024) Vol. 21, pp. 101837-101837
Open Access | Times Cited: 27
Performance evaluation of concrete made with plastic waste using multi-expression programming
Usama Asif, Muhammad Faisal Javed, Mana Alyami, et al.
Materials Today Communications (2024) Vol. 39, pp. 108789-108789
Closed Access | Times Cited: 16
Computational prediction of workability and mechanical properties of bentonite plastic concrete using multi-expression programming
Majid Khan, Mujahid Ali, Taoufik Najeh, et al.
Scientific Reports (2024) Vol. 14, Iss. 1
Open Access | Times Cited: 15
Metaheuristic optimization algorithms-based prediction modeling for titanium dioxide-Assisted photocatalytic degradation of air contaminants
Muhammad Faisal Javed, Bilal Siddiq, Kennedy C. Onyelowe, et al.
Results in Engineering (2024) Vol. 23, pp. 102637-102637
Open Access | Times Cited: 14
Estimating the compressive and tensile strength of basalt fibre reinforced concrete using advanced hybrid machine learning models
Irfan Ullah, Muhammad Faisal Javed, Hisham Alabduljabbar, et al.
Structures (2025) Vol. 71, pp. 108138-108138
Closed Access | Times Cited: 1
Application of metaheuristic optimization algorithms in predicting the compressive strength of 3D-printed fiber-reinforced concrete
Mana Alyami, Majid Khan, Muhammad Faisal Javed, et al.
Developments in the Built Environment (2023) Vol. 17, pp. 100307-100307
Open Access | Times Cited: 28
Assessment of the split tensile strength of fiber reinforced recycled aggregate concrete using interpretable approaches with graphical user interface
Hisham Alabduljabbar, Furqan Farooq, Mana Alyami, et al.
Materials Today Communications (2024) Vol. 38, pp. 108009-108009
Closed Access | Times Cited: 13
Comparative analysis of various machine learning algorithms to predict strength properties of sustainable green concrete containing waste foundry sand
Muhammad Faisal Javed, Majid Khan, Muhammad Fawad, et al.
Scientific Reports (2024) Vol. 14, Iss. 1
Open Access | Times Cited: 13
Predicting the properties of concrete incorporating graphene nano platelets by experimental and machine learning approaches
Rayed Alyousef, Roz‐Ud‐Din Nassar, Muhammad Fawad, et al.
Case Studies in Construction Materials (2024) Vol. 20, pp. e03018-e03018
Open Access | Times Cited: 12
Application of metaheuristic algorithms for compressive strength prediction of steel fiber reinforced concrete exposed to high temperatures
Muhammad Faisal Javed, Majid Khan, Moncef L. Nehdi, et al.
Materials Today Communications (2024) Vol. 39, pp. 108832-108832
Closed Access | Times Cited: 10
Machine learning for predicting compressive strength of sustainable cement paste incorporating copper mine tailings as supplementary cementitious materials
Eka Oktavia Kurniati, Hang Zeng, Marat I. Latypov, et al.
Case Studies in Construction Materials (2024) Vol. 21, pp. e03373-e03373
Open Access | Times Cited: 9
An explainable machine learning approach to predict the compressive strength of graphene oxide-based concrete
D.P.P. Meddage, Isuri Fonseka, Damith Mohotti, et al.
Construction and Building Materials (2024) Vol. 449, pp. 138346-138346
Open Access | Times Cited: 9
Indirect prediction of graphene nanoplatelets-reinforced cementitious composites compressive strength by using machine learning approaches
Muhammad Fawad, Hisham Alabduljabbar, Furqan Farooq, et al.
Scientific Reports (2024) Vol. 14, Iss. 1
Open Access | Times Cited: 6
Machine learning approaches for predicting mechanical properties in additive manufactured lattice structures
B. Veera Siva Reddy, Ameer Malik Shaik, C. Chandrasekhara Sastry, et al.
Materials Today Communications (2024) Vol. 40, pp. 109937-109937
Closed Access | Times Cited: 6
Data-driven Models for Predicting Compressive Strength of 3D-printed Fiber-Reinforced Concrete using Interpretable Machine Learning Algorithms
Muhammad Arif, Faizullah Jan, A. Rezzoug, et al.
Case Studies in Construction Materials (2024) Vol. 21, pp. e03935-e03935
Open Access | Times Cited: 6
Optimizing compressive strength of quaternary-blended cement concrete through ensemble-instance-based machine learning
Ammar Babiker, Yassir M. Abbas, M. Iqbal Khan, et al.
Materials Today Communications (2024) Vol. 39, pp. 109150-109150
Closed Access | Times Cited: 5
Innovative hybrid machine learning models for estimating the compressive strength of copper mine tailings concrete
Mana Alyami, Kennedy C. Onyelowe, Ali H. AlAteah, et al.
Case Studies in Construction Materials (2024) Vol. 21, pp. e03869-e03869
Open Access | Times Cited: 5
Predicting Penetration Depth in Ultra-High-Performance Concrete Targets under Ballistic Impact: An Interpretable Machine Learning Approach Augmented by Deep Generative Adversarial Network
Majid Khan, Muhammad Faisal Javed, N. Othman, et al.
Results in Engineering (2025), pp. 103909-103909
Open Access
Predictive modeling for durability characteristics of blended cement concrete utilizing machine learning algorithms
Bo Fu, Hua Lei, Irfan Ullah, et al.
Case Studies in Construction Materials (2025), pp. e04209-e04209
Open Access
Filament geometry control of printable geopolymer using experimental and data driven approaches
Ali Rezaei Lori, Mehdi Mehrali
Construction and Building Materials (2025) Vol. 461, pp. 139853-139853
Open Access
Research on 3D Printing Concrete Mechanical Properties Prediction Model Based on Machine Learning
Yonghong Zhang, Suping Cui, Bohao Yang, et al.
Case Studies in Construction Materials (2025), pp. e04254-e04254
Open Access
Revealing the nature of soil liquefaction using machine learning
Sufyan Ghani, Ishwor Thapa, Amrendra Kumar, et al.
Earth Science Informatics (2025) Vol. 18, Iss. 2
Open Access
Optimizing high-strength concrete compressive strength with explainable machine learning
Sanjog Chhetri Sapkota, Christina Panagiotakopoulou, Dipak Dahal, et al.
Multiscale and Multidisciplinary Modeling Experiments and Design (2025) Vol. 8, Iss. 3
Open Access
Predicting the shield effectiveness of carbon fiber reinforced mortars utilizing metaheuristic algorithms
Mana Alyami, Irfan Ullah, Furqan Ahmad, et al.
Case Studies in Construction Materials (2025), pp. e04357-e04357
Open Access
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