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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:

A Machine Learning Approach to Prediction of the Compressive Strength of Segregated Lightweight Aggregate Concretes Using Ultrasonic Pulse Velocity
Violeta Migallón, Héctor Penadés, Jose Penadés, et al.
Applied Sciences (2023) Vol. 13, Iss. 3, pp. 1953-1953
Open Access | Times Cited: 9

Showing 9 citing articles:

Splitting tensile strength prediction of Metakaolin concrete using machine learning techniques
Qiang Li, Guoqi Ren, Haoran Wang, et al.
Scientific Reports (2023) Vol. 13, Iss. 1
Open Access | Times Cited: 11
Stratified Metamodeling to Predict Concrete Compressive Strength Using an Optimized Dual-Layered Architectural Framework
Geraldo F. Neto, Bruno da Silva Macêdo, Tales Humberto de Aquino Boratto, et al.
Mathematical and Computational Applications (2025) Vol. 30, Iss. 1, pp. 16-16
Open Access
Ultrasonic detection and deep learning for high-precision concrete strength prediction
Xu Gan, Wei Wang, Chenhui Jiang, et al.
Journal of Building Engineering (2025), pp. 112372-112372
Closed Access
Different machine learning approaches to predict the compressive strength of composite cement concrete
Md. Nafiuzzaman, Tausif Ibn Jakir, Israt Jahan Aditi, et al.
Journal of Building Pathology and Rehabilitation (2025) Vol. 10, Iss. 2
Closed Access
Efficient mix design method for lightweight high strength concrete: A machine learning approach
Mohamed Sifan, Hoang X. Nguyen, Brabha Nagaratnam, et al.
Structures (2023) Vol. 55, pp. 1805-1822
Closed Access | Times Cited: 8
Prediction of frost resistance and multiobjective optimisation of low-carbon concrete on the basis of machine learning
Jinpeng Dai, Zhijie Zhang, Xuwei Dong, et al.
Materials Today Communications (2024) Vol. 40, pp. 109525-109525
Closed Access | Times Cited: 2
A Comprehensive Study on the Estimation of Concrete Compressive Strength Using Machine Learning Models
Yusuf Tahir ALTUNCI
Buildings (2024) Vol. 14, Iss. 12, pp. 3851-3851
Open Access | Times Cited: 2
Predicting compressive strength of RCFST columns under different loading scenarios using machine learning optimization
Feng Wu, Fei Tang, Ruichen Lu, et al.
Scientific Reports (2023) Vol. 13, Iss. 1
Open Access | Times Cited: 5
Interpretable machine learning models for concrete compressive strength prediction
Huong-Giang Thi Hoang, Thuy‐Anh Nguyen, Haï-Bang Ly
Innovative Infrastructure Solutions (2024) Vol. 10, Iss. 1
Closed Access
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