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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:

Development of predictive models for sustainable concrete via genetic programming-based algorithms
Lingling Chen, Zhiyuan Wang, Aftab Ahmad Khan, et al.
Journal of Materials Research and Technology (2023) Vol. 24, pp. 6391-6410
Open Access | Times Cited: 47

Showing 1-25 of 47 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: 90
Evaluation of water quality indexes with novel machine learning and SHapley Additive ExPlanation (SHAP) approaches
Ali Aldrees, Majid Khan, Abubakr Taha Bakheit Taha, et al.
Journal of Water Process Engineering (2024) Vol. 58, pp. 104789-104789
Closed Access | Times Cited: 56
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
Machine learning-driven predictive models for compressive strength of steel fiber reinforced concrete subjected to high temperatures
Rayed Alyousef, Muhammad Faisal Rehman, Majid Khan, et al.
Case Studies in Construction Materials (2023) Vol. 19, pp. e02418-e02418
Open Access | Times Cited: 42
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
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: 16
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: 16
Towards sustainable construction: Machine learning based predictive models for strength and durability characteristics of blended cement concrete
Majid Khan, Muhammad Faisal Javed
Materials Today Communications (2023) Vol. 37, pp. 107428-107428
Closed Access | Times Cited: 38
Optimizing durability assessment: Machine learning models for depth of wear of environmentally-friendly concrete
Majid Khan, Roz‐Ud‐Din Nassar, Asad U. Khan, et al.
Results in Engineering (2023) Vol. 20, pp. 101625-101625
Open Access | Times Cited: 33
Forecasting the strength characteristics of concrete incorporating waste foundry sand using advance machine algorithms including deep learning
Rayed Alyousef, Roz‐Ud‐Din Nassar, Majid Khan, et al.
Case Studies in Construction Materials (2023) Vol. 19, pp. e02459-e02459
Open Access | Times Cited: 31
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
Supplementary cementitious materials in blended cement concrete: Advancements in predicting compressive strength through machine learning
Fahid Aslam, Muhammad Zubair Shahab
Materials Today Communications (2023) Vol. 38, pp. 107725-107725
Closed Access | Times Cited: 24
Intelligent prediction modeling for flexural capacity of FRP-strengthened reinforced concrete beams using machine learning algorithms
Majid Khan, Adil Khan, Asad U. Khan, et al.
Heliyon (2023) Vol. 10, Iss. 1, pp. e23375-e23375
Open Access | Times Cited: 21
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: 11
Machine and Deep Learning Methods for Concrete Strength Prediction: A Bibliometric and Content Analysis Review of Research Trends and Future Directions
Raman Kumar, Essam Althaqafi, S. Gopal Krishna Patro, et al.
Applied Soft Computing (2024) Vol. 164, pp. 111956-111956
Closed Access | Times Cited: 10
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
Evaluation of machine learning models for predicting TiO2 photocatalytic degradation of air contaminants
Muhammad Faisal Javed, Muhammad Zubair Shahab, Usama Asif, et al.
Scientific Reports (2024) Vol. 14, Iss. 1
Open Access | Times Cited: 9
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: 7
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: 20
Predictive modeling for depth of wear of concrete modified with fly ash: A comparative analysis of genetic programming-based algorithms
Adil Khan, Majid Khan, Mohsin Ali, et al.
Case Studies in Construction Materials (2023) Vol. 20, pp. e02744-e02744
Open Access | Times Cited: 19
Explainable Prediction of Compressive Strength and Elastic Modulus for Concrete Containing Waste Foundry Sand Using Bayesian-Optimized XGBoost with 10-Fold Cross-Validation
Yang Sun
Journal of Sustainable Metallurgy (2024) Vol. 10, Iss. 1, pp. 335-359
Closed Access | Times Cited: 6
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