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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:

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

Showing 1-25 of 38 citing articles:

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
Splitting tensile strength of basalt fiber reinforced coral aggregate concrete: Optimized XGBoost models and experimental validation
Zhen Sun, Yalin Li, Yuxi Yang, et al.
Construction and Building Materials (2024) Vol. 416, pp. 135133-135133
Closed Access | Times Cited: 50
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
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
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
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
Predicting the crack repair rate of self-healing concrete using soft-computing tools
Yuanfeng Lou, Huiling Wang, Muhammad Nasir Amin, et al.
Materials Today Communications (2024) Vol. 38, pp. 108043-108043
Closed Access | Times Cited: 14
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
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
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
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
Interpretable machine learning models for predicting the bond strength between UHPC and normal-strength concrete
Kaihua Liu, Tingrui Wu, Zhuorong Shi, et al.
Materials Today Communications (2024) Vol. 40, pp. 110006-110006
Closed Access | Times Cited: 6
The Influence of Fly Ash and Slag on the Mechanical Properties of Geopolymer Concrete
Zhenyang Zhang, Tian Su, L. Zhang, et al.
Buildings (2024) Vol. 14, Iss. 9, pp. 2720-2720
Open Access | Times Cited: 5
Advanced and hybrid machine learning techniques for predicting compressive strength in palm oil fuel ash-modified concrete with SHAP analysis
Tariq Ali, Kennedy C. Onyelowe, Muhammad Sarmad Mahmood, et al.
Scientific Reports (2025) Vol. 15, Iss. 1
Open Access
Exploratory literature review and scientometric analysis of artificial intelligence applied to geopolymeric materials
Aldo Ribeiro de Carvalho, Romário Parreira Pita, Thaís Mayra de Oliveira, et al.
Engineering Applications of Artificial Intelligence (2025) Vol. 145, pp. 110210-110210
Closed Access
Eco-enhanced concrete: Harnessing marble powder for sustainable strength and durability
J. Karthick, K. Suguna, Puthiyaveettil N. Raghunath, et al.
Innovative Infrastructure Solutions (2025) Vol. 10, Iss. 4
Closed Access
Viscosity and melting temperature prediction of mold fluxes based on explainable machine learning and SHapley additive exPlanations
Wei Yan, Yangyang Shen, Shoujie Chen, et al.
Journal of Non-Crystalline Solids (2024) Vol. 636, pp. 123037-123037
Closed Access | Times Cited: 4
Optimized prediction modeling of micropollutant removal efficiency in forward osmosis membrane systems using explainable machine learning algorithms
Ali Aldrees, Muhammad Faisal Javed, Majid Khan, et al.
Journal of Water Process Engineering (2024) Vol. 66, pp. 105937-105937
Closed Access | Times Cited: 4
Toward sustainability: Integrating experimental study and data-driven modeling for eco-friendly paver blocks containing plastic waste
Usama Asif, Muhammad Faisal Javed, Deema Mohammed Alsekait, et al.
REVIEWS ON ADVANCED MATERIALS SCIENCE (2024) Vol. 63, Iss. 1
Open Access | Times Cited: 4
Machine learning models for estimating the compressive strength of rubberized concrete subjected to elevated temperature: Optimization and hyper-tuning
Turki S. Alahmari, Irfan Ullah, Furqan Farooq
Sustainable Chemistry and Pharmacy (2024) Vol. 42, pp. 101763-101763
Closed Access | Times Cited: 4
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
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