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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:

Compressive strength prediction of concrete blended with carbon nanotubes using gene expression programming and random forest: hyper-tuning and optimization
Dawei Yang, Ping Xu, Athar Zaman, et al.
Journal of Materials Research and Technology (2023) Vol. 24, pp. 7198-7218
Open Access | Times Cited: 29

Showing 1-25 of 29 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: 88
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
Compressive strength prediction of sustainable concrete incorporating rice husk ash (RHA) using hybrid machine learning algorithms and parametric analyses
Abul Kashem, Rezaul Karim, Pobithra Das, et al.
Case Studies in Construction Materials (2024) Vol. 20, pp. e03030-e03030
Open Access | Times Cited: 28
Compressive strength prediction models for concrete containing nano materials and exposed to elevated temperatures
Hany A. Dahish, Ahmed D. Almutairi
Results in Engineering (2025), pp. 103975-103975
Open Access | Times Cited: 2
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
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
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
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
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: 9
Assessment of physical and mechanical properties of concrete with carbon nanotubes pre-dispersed in cement
Elvys Dias Reis, Heron Freitas Resende, André Luís Christoforo, et al.
Journal of Building Engineering (2024) Vol. 89, pp. 109255-109255
Open Access | Times Cited: 8
Reverse design for mixture proportions of recycled brick aggregate concrete using machine learning-based meta-heuristic algorithm: A multi-objective driven study
Yuhan Wang, Shuyuan Zhang, Zhe Zhang, et al.
Journal of CO2 Utilization (2024) Vol. 88, pp. 102944-102944
Open Access | Times Cited: 7
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
An AutoGluon-enabled robust machine learning model for concrete tensile and compressive strength forecast
Chukwuemeka Daniel, Edith Komo Neufville
International Journal of Construction Management (2025), pp. 1-12
Closed Access
Stratified Metamodeling to Predict Concrete Compressive Strength Using an Optimized Dual-Layered Architectural Framework
Geraldo F. Neto, Bruno da S. Macêdo, Tales Humberto de Aquino Boratto, et al.
Mathematical and Computational Applications (2025) Vol. 30, Iss. 1, pp. 16-16
Open Access
Predicting Mechanical Properties of Concrete Structures Using Metaheuristic-Optimization-Based Machine Learning Models
Ngoc‐Mai Nguyen
Applied Soft Computing (2025), pp. 112893-112893
Closed Access
Random Forest Algorithm for the Mechanical Strength Prediction of Green Cement-Based Materials Incorporating Waste Materials Under Fire Condition
Lei Zhang, Ruipeng Qiu, Jiabin Xie, et al.
Materials (2025) Vol. 18, Iss. 5, pp. 1025-1025
Open Access
Metaheuristics approaches for sustainable material optimization: Enhancing environmental impact and efficiency
Ankita Yadav, Suphiya Khan, Mohammad Ashfaq
Elsevier eBooks (2025), pp. 25-60
Closed Access
Tensile behavior evaluation of two-stage concrete using an innovative model optimization approach
Muhammad Nasir Amin, Faizullah Jan, Kaffayatullah Khan, et al.
REVIEWS ON ADVANCED MATERIALS SCIENCE (2025) Vol. 64, Iss. 1
Open Access
Prediction of compressive strength of high-performance concrete using optimization machine learning approaches with SHAP analysis
Md Mahamodul Islam, Pobithra Das, Md Mahbubur Rahman, et al.
Journal of Building Pathology and Rehabilitation (2024) Vol. 9, Iss. 2
Closed Access | Times Cited: 4
Multi-strategy Hybrid Coati Optimizer: A Case Study of Prediction of Average Daily Electricity Consumption in China
Gang Hu, Sa Wang, Essam H. Houssein
Journal of Bionic Engineering (2024) Vol. 21, Iss. 5, pp. 2540-2568
Closed Access | Times Cited: 3
Machine learning assisted prediction of the mechanical properties of carbon nanotube‐incorporated concrete
Muhammad Imran, Hassan Amjad, Shayan Ali Khan, et al.
Structural Concrete (2024)
Closed Access | Times Cited: 3
Mathematical model for prediction of compressive strength of ternary blended cement concrete utilizing gene expression programming
Stephen Adeyemi Alabi, Chinwuba Arum, Adekunle P. Adewuyi, et al.
Scientific African (2023) Vol. 22, pp. e01954-e01954
Open Access | Times Cited: 9
Advanced Anomaly Detection in Manufacturing Processes: Leveraging Feature Value Analysis for Normalizing Anomalous Data
Seunghyun Kim, Hyunsoo Seo, Eui Chul Lee
Electronics (2024) Vol. 13, Iss. 7, pp. 1384-1384
Open Access | Times Cited: 2
Estimating the Workability of Concrete with a Stereovision Camera during Mixing
Teemu Ojala, Jouni Punkki
Sensors (2024) Vol. 24, Iss. 14, pp. 4472-4472
Open Access | Times Cited: 1
Comparative Analysis of Automated Machine Learning and Optimized Conventional Machine Learning for Concrete’s Uniaxial Compressive Strength Prediction
Chukwuemeka Daniel
Advances in Civil Engineering (2024) Vol. 2024, Iss. 1
Open Access | Times Cited: 1
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