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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:

Boosting-based ensemble machine learning models for predicting unconfined compressive strength of geopolymer stabilized clayey soil
Gamil M. S. Abdullah, Mahmood Ahmad, Muhammad Babur, et al.
Scientific Reports (2024) Vol. 14, Iss. 1
Open Access | Times Cited: 16

Showing 16 citing articles:

Efficient prediction of California bearing ratio in solid waste-cement-stabilized soil using improved hybrid extreme gradient boosting model
Yiliang Tu, Qianglong Yao, Shuitao Gu, et al.
Materials Today Communications (2025) Vol. 43, pp. 111627-111627
Closed Access | Times Cited: 1
Enhancing unconfined compressive strength prediction in nano-silica stabilized soil: a comparative analysis of ensemble and deep learning models
Ishwor Thapa, Sufyan Ghani
Modeling Earth Systems and Environment (2024) Vol. 10, Iss. 4, pp. 5079-5102
Closed Access | Times Cited: 10
Machine learning algorithms for predictive modeling of dyslipidemia-associated cardiovascular disease risk in pregnancy: a comparison of boosting, random forest, and decision tree regression
Idris Zubairu Sadiq, Fatima Sadiq Abubakar, Muhammad Auwal Saliu, et al.
Bulletin of the National Research Centre/Bulletin of the National Research Center (2025) Vol. 49, Iss. 1
Open Access
Insights into the strength development in cement-treated soils: An explainable AI-based approach for optimized mix design
Muhammad Hasnain Ayub Khan, Adel Abdallah, Olivier Cuisinier
Computers and Geotechnics (2025) Vol. 180, pp. 107103-107103
Closed Access
Environmentally friendly PAEs alternatives with desired synthesizability by machine learning methods
Pengfei Qiu, Hao Yang
Journal of environmental chemical engineering (2025), pp. 115946-115946
Closed Access
Improved prediction of soil shear strength using machine learning algorithms: interpretability analysis using SHapley Additive exPlanations
Mahmood Ahmad, Mohammad Al-Zubi, Hamad Almujibah, et al.
Frontiers in Earth Science (2025) Vol. 13
Open Access
Integrated Machine Learning and Response Surface Methodology for Comprehensive Rheological Characterization of low-carbon binders
Munir Iqbal, Sohaib Nazar, Jian Yang, et al.
Case Studies in Construction Materials (2025), pp. e04475-e04475
Open Access
Machine Learning and Sustainable Geopolymer Materials: A Systematic Review
Ho Anh Thu Nguyen, Duy Hoang Pham, Yonghan Ahn, et al.
Materials Today Sustainability (2025), pp. 101095-101095
Open Access
Support vector machine-based prediction of unconfined compressive strength of Multi-Walled Carbon nanotube doped soil-fly ash mixes
Anish Kumar, Sanjeev Sinha
Multiscale and Multidisciplinary Modeling Experiments and Design (2024) Vol. 7, Iss. 6, pp. 5365-5386
Closed Access | Times Cited: 4
Advancing earth science in geotechnical engineering: A data-driven soft computing technique for unconfined compressive strength prediction in soft soil
Ishwor Thapa, Sufyan Ghani
Journal of Earth System Science (2024) Vol. 133, Iss. 3
Closed Access | Times Cited: 3
Explainable Artificial Intelligence for Predicting the Compressive Strength of Soil and Ground Granulated Blast Furnace Slag Mixtures
Ahmed Mohammed Awad Mohammed, Omayma Husain, Muyideen Abdulkareem, et al.
Results in Engineering (2024) Vol. 25, pp. 103637-103637
Open Access | Times Cited: 3
High-performance Carbonaceous Absorbers: From Heterogeneous Absorbents to Data-driven Metamaterials
Diana Estévez, Faxiang Qin
Carbon (2024), pp. 119850-119850
Closed Access | Times Cited: 2
Estimation of Compressive Strength of Rubberised Slag Based Geopolymer Concrete Using Various Machine Learning Techniques Based Models
Sesha Choudary Yeluri, Karan Singh, Akshay Kumar, et al.
Iranian Journal of Science and Technology Transactions of Civil Engineering (2024)
Closed Access | Times Cited: 1
Prediction of swelling pressure of expansive soil using machine learning methods
Sweta Gahlot, Rajat Mangal, Abhishek Arya, et al.
Asian Journal of Civil Engineering (2024)
Closed Access | Times Cited: 1
Effect of multicollinearity in assessing the compaction and strength parameters of lime-treated expansive soil using artificial intelligence techniques
Amit Kumar Jangid, Jitendra Khatti, Kamaldeep Singh Grover
Multiscale and Multidisciplinary Modeling Experiments and Design (2024) Vol. 8, Iss. 1
Closed Access | Times Cited: 1
Evaluation of artificial neurocomputing algorithms and their metacognitive robustness in predictive modeling of fuel consumption rates during tillage
Frankline Mwiti, Ayub Njoroge Gitau, Duncan Mbuge
Computers and Electronics in Agriculture (2024) Vol. 224, pp. 109221-109221
Closed Access
Predictive modeling of shear strength in fly ash-stabilized clayey soils using artificial neural networks and support vector regression
Nadeem Mehraj Wani, Parwati Thagunna
Asian Journal of Civil Engineering (2024)
Closed Access
Prediction of the Unconfined Compressive Strength of a One-Part Geopolymer-Stabilized Soil Using Deep Learning Methods with Combined Real and Synthetic Data
Qinyi Chen, Guo Hu, Jun Wu
Buildings (2024) Vol. 14, Iss. 9, pp. 2894-2894
Open Access
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