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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:

Application of machine learning boosting and bagging methods to predict compressive and flexural strength of marble cement mortar
Zhiqiang Chen
Materials Today Communications (2024) Vol. 39, pp. 108600-108600
Closed Access | Times Cited: 9

Showing 9 citing articles:

Optimizing machine learning techniques and SHapley Additive exPlanations (SHAP) analysis for the compressive property of self-compacting concrete
Zhiyuan Wang, Huihui Liu, Muhammad Nasir Amin, et al.
Materials Today Communications (2024) Vol. 39, pp. 108804-108804
Closed Access | Times Cited: 5
Integrating Experimental Analysis and Gradient Boosting for the Durability Assessment of Lime-Based Mortar in Acidic Environment
Ali Taheri, Nima Azimi, Daniel V. Oliveira, et al.
Buildings (2025) Vol. 15, Iss. 3, pp. 408-408
Open Access
Modeling the impact of SiO2, Al2O3, CaO, and Fe2O3 on the compressive strength of cement modified with nano-silica and silica fume
Mohammed A. Jamal, Ahmed Salih Mohammed, Jagar A. Ali
Multiscale and Multidisciplinary Modeling Experiments and Design (2025) Vol. 8, Iss. 2
Closed Access
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
Analyzing the efficacy of waste marble and glass powder for the compressive strength of self-compacting concrete using machine learning strategies
Qing Guan, Zhong Ling Tong, Muhammad Nasir Amin, et al.
REVIEWS ON ADVANCED MATERIALS SCIENCE (2024) Vol. 63, Iss. 1
Open Access | Times Cited: 2
Hybrid Machine Learning Model Based on GWO and PSO Optimization for Prediction of Oilwell Cement Compressive Strength under Acidic Corrosion
Lianzhou Wang, Sheng Huang, Zaoyuan Li, et al.
SPE Journal (2024) Vol. 29, Iss. 09, pp. 4684-4695
Closed Access | Times Cited: 1
Experimenting the effectiveness of waste materials in improving the compressive strength of plastic-based mortar
Mengchen Yun, LI Xue-feng, Muhammad Nasir Amin, et al.
Case Studies in Construction Materials (2024) Vol. 21, pp. e03543-e03543
Open Access | Times Cited: 1
Evaluating the strength loss and the effectiveness of glass and eggshell powder for cement mortar under acidic conditions
Hao Liu, Suleman Ayub Khan, Muhammad Nasir Amin, et al.
REVIEWS ON ADVANCED MATERIALS SCIENCE (2024) Vol. 63, Iss. 1
Open Access
Aprendizado de máquina para predição de resistência à compressão de argamassas com e sem resíduo de construção
Nilson Jorge Leão Júnior, Raniere Moisés da Cruz Fonseca, Sérgio Silva, et al.
Matéria (Rio de Janeiro) (2024) Vol. 29, Iss. 4
Open Access
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