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 8 citing articles:

Utilization of Tree-Based Ensemble Models for Predicting the Shear Strength of Soil
Ahsan Rabbani, Jan Afzal Muslih, Mukul Saxena, et al.
Transportation Infrastructure Geotechnology (2024) Vol. 11, Iss. 4, pp. 2382-2405
Closed Access | Times Cited: 11

A multi‐level approach to predict the seismic response of rigid rocking structures using artificial neural networks
Seyed Amir Banimahd, Anastasios I. Giouvanidis, Shaghayegh Karımzadeh, et al.
Earthquake Engineering & Structural Dynamics (2024) Vol. 53, Iss. 6, pp. 2185-2208
Open Access | Times Cited: 5

Forecasting the Capacity of Open-Ended Pipe Piles Using Machine Learning
Baturalp Öztürk, Antonio Kodsy, Magued Iskander
Infrastructures (2023) Vol. 8, Iss. 1, pp. 12-12
Open Access | Times Cited: 12

Re-purposing of shallow wind turbine foundations for power capacity increase
Behrouz Badrkhani Ajaei, M. Hesham El Naggar
Soil Dynamics and Earthquake Engineering (2023) Vol. 171, pp. 107959-107959
Closed Access | Times Cited: 7

Effect of Footing Shape on the Rocking Behavior of Shallow Foundations
Ali Khezri, Mohamadali Moradi, Seyed Majdeddin Mir Mohammad Hosseini, et al.
Buildings (2024) Vol. 14, Iss. 3, pp. 573-573
Open Access | Times Cited: 2

Seismic Rocking Response Classification Through the Lens of a Machine Learning Methodology
Samuel Kai Wah Chu, Anastasios I. Giouvanidis, Chengning Loong, et al.
Lecture notes in civil engineering (2024), pp. 763-772
Closed Access

Prediction of Acceleration Amplification Ratio of Rocking Foundations Using Machine Learning and Deep Learning Models
Sivapalan Gajan
Applied Sciences (2023) Vol. 13, Iss. 23, pp. 12791-12791
Open Access | Times Cited: 1

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