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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:

Strength evaluation of eco-friendly waste-derived self-compacting concrete via interpretable genetic-based machine learning models
Zhiqiang Chen, Bawar Iftikhar, Ayaz Ahmad, et al.
Materials Today Communications (2023) Vol. 37, pp. 107356-107356
Closed Access | Times Cited: 10

Showing 10 citing articles:

Metaheuristic optimization of machine learning models for strength prediction of high-performance self-compacting alkali-activated slag concrete
Suraj Kumar Parhi, Soumyaranjan Panda, Saswat Dwibedy, et al.
Multiscale and Multidisciplinary Modeling Experiments and Design (2024) Vol. 7, Iss. 3, pp. 2901-2928
Closed Access | Times Cited: 16
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: 10
Machine learning approaches for real-time prediction of compressive strength in self-compacting concrete
Sufyan Ghani, Nishant Kumar, Megha Gupta, et al.
Asian Journal of Civil Engineering (2023) Vol. 25, Iss. 3, pp. 2743-2760
Closed Access | Times Cited: 17
Comparison of boosting and genetic programming techniques for prediction of tensile strain capacity of Engineered Cementitious Composites (ECC)
Waleed Bin Inqiad, Muhammad Faisal Javed, Muhammad Shahid Siddique, et al.
Materials Today Communications (2024) Vol. 39, pp. 109222-109222
Closed Access | Times Cited: 7
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
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
A systematic literature review of AI-based prediction methods for self-compacting, geopolymer, and other eco-friendly concrete types: Advancing sustainable concrete
Tariq Ali, Mohamed Hechmi El Ouni, Muhammad Zeeshan Qureshi, et al.
Construction and Building Materials (2024) Vol. 440, pp. 137370-137370
Closed Access | Times Cited: 4
Prediction and modeling of mechanical properties of concrete modified with ceramic waste using artificial neural network and regression model
Pravin R. Kshirsagar, Kamal Upreti, Virendra Singh Kushwah, et al.
Signal Image and Video Processing (2024) Vol. 18, Iss. S1, pp. 183-197
Closed Access | Times Cited: 3
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: 3
Assessing the compressive strength of eco-friendly concrete made with rice husk ash: A hybrid artificial intelligence-aided technique
Ramin Kazemi, Seyed Ali Emamian, Mehrdad Arashpour
Structures (2024) Vol. 68, pp. 107050-107050
Closed Access | Times Cited: 2
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