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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.

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Showing 16 citing articles:

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 | Times Cited: 2
AI-driven design for the compressive strength of ultra-high performance geopolymer concrete (UHPGC): From explainable ensemble models to the graphical user interface
Metin Katlav, Faruk Ergen, İzzeddin Dönmez
Materials Today Communications (2024) Vol. 40, pp. 109915-109915
Closed Access | Times Cited: 12
A comprehensive review of dwarf mongoose optimization algorithm with emerging trends and future research directions
Olanrewaju L. Abraham, Md Asri Ngadi
Decision Analytics Journal (2025), pp. 100551-100551
Open Access | Times Cited: 1
Explainable ensemble algorithms with grey wolf optimization for estimation of the tensile performance of polyethylene fiber-reinforced engineered cementitious composite
Mehmet Emin TABAR, Metin Katlav, Kâzım Türk
Materials Today Communications (2025), pp. 112028-112028
Closed Access | Times Cited: 1
Optimized machine learning models for predicting the tensile strength of high-performance concrete
Divesh Ranjan Kumar, Pramod Kumar, Pradeep Thangavel, et al.
Journal of Structural Integrity and Maintenance (2025) Vol. 10, Iss. 1
Closed Access | Times Cited: 1
Electrical resistivity of eco-friendly hybrid fiber-reinforced SCC: Effect of ground granulated blast furnace slag and copper slag content as well as hooked-end fiber length
Metin Katlav, İzzeddin Dönmez, Kâzım Türk
Construction and Building Materials (2024) Vol. 438, pp. 137235-137235
Closed Access | Times Cited: 4
Interpretable Machine Learning Models for Prediction of UHPC Creep Behavior
Peng Zhu, Wenshuo Cao, Lianzhen Zhang, et al.
Buildings (2024) Vol. 14, Iss. 7, pp. 2080-2080
Open Access | Times Cited: 4
An Experimental Investigation to Predict the Compressive Strength of Lightweight Ceramsite Aggregate UHPC Using Boosting and Bagging Techniques
Md. Sohel Rana, Fangyuan Li
Materials Today Communications (2024), pp. 110759-110759
Closed Access | Times Cited: 4
AI-guided design framework for bond behavior of steel-concrete in steel reinforced concrete composites: From dataset cleaning to feature engineering
Metin Katlav, Mehmet Emin Tabar, Kâzım Türk
Materials Today Communications (2024) Vol. 42, pp. 111286-111286
Closed Access | Times Cited: 4
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
Assessment of Peak Particle Velocity of Blast Vibration using Hybrid Soft Computing Approaches
Haiping Yuan, Yangyao Zou, Hengzhe Li, et al.
Journal of Computational Design and Engineering (2025)
Open Access
An Interpretable XGBoost-SHAP Machine Learning Model for Reliable Prediction of Mechanical Properties in Waste Foundry Sand-Based Eco-Friendly Concrete
Meysam Alizamir, Mo Wang, Rana Muhammad Adnan Ikram, et al.
Results in Engineering (2025), pp. 104307-104307
Open Access
Enhancing the Predictive Accuracy of Marshall Design Tests Using Generative Adversarial Networks and Advanced Machine Learning Techniques
Usama Asif, Waseem Akhtar Khan, Khawaja Atif Naseem, et al.
Materials Today Communications (2025), pp. 112379-112379
Closed Access
Restrained shrinkage of ultra‐high performance concrete in bridge deck shear pockets and optimization of countermeasures
Yue Li, Xin Ruan, Weiyu Dou, et al.
Structural Concrete (2025)
Closed Access
A robust approach for bond strength prediction of mortar using machine learning with SHAP interpretability
Kai Wu, Sihao Zhou, Qiang Li, et al.
Materials Today Communications (2024), pp. 110667-110667
Closed Access | Times Cited: 2
Developing an efficient explainable artificial intelligence approach for accurate reverse osmosis desalination plant performance prediction: application of SHAP analysis
Meysam Alizamir, Mo Wang, Rana Muhammad Adnan Ikram, et al.
Engineering Applications of Computational Fluid Mechanics (2024) Vol. 18, Iss. 1
Open Access
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