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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:

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

Showing 12 citing articles:

Dimensionless Machine Learning: Dimensional Analysis to Improve LSSVM and ANN models and predict bearing capacity of circular foundations
Hongchao Li, Shahab Hosseini, Behrouz Gordan, et al.
Artificial Intelligence Review (2025) Vol. 58, Iss. 4
Open Access | Times Cited: 1
Effect of Feature Selection Technique on the Pile Capacity Predicted Using Machine Learning
Baturalp Öztürk, Antonio Kodsy, Magued Iskander
Geo-Congress 2019 (2024), pp. 153-163
Closed Access | Times Cited: 5
Prediction of pile-bearing capacity using Least Square Support Vector Regression: individual and hybrid models development
Xiaochuan Yang
Multiscale and Multidisciplinary Modeling Experiments and Design (2024) Vol. 7, Iss. 3, pp. 2701-2715
Closed Access | Times Cited: 2
Forecasting the Bearing Capacity of Open-Ended Pipe Piles Using Machine Learning Ensemble Methods
Baturalp Öztürk, Antonio Kodsy, Magued Iskander
IFCEE 2018 (2024), pp. 146-156
Closed Access | Times Cited: 2
Using Machine Learning to Predict Axial Pile Capacity
Baturalp Öztürk, Antonio Kodsy, Magued Iskander
Transportation Research Record Journal of the Transportation Research Board (2024)
Closed Access | Times Cited: 1
Prediction of seismic-induced bending moment and lateral displacement in closed and open-ended pipe piles: A genetic programming approach
Laith Sadik, Duaa Al-Jeznawi, Saif Alzabeebee, et al.
Artificial Intelligence in Geosciences (2024) Vol. 5, pp. 100076-100076
Open Access | Times Cited: 1
Study on quantitative interpretation of uranium spectral gamma-ray logging based on machine learning algorithm
Yan Zhang, Yujin Ye, Jun Qiu, et al.
Nuclear Engineering and Technology (2024) Vol. 56, Iss. 12, pp. 4959-4965
Open Access | Times Cited: 1
DIA for Classification of Soils Using Machine Learning and Computer Vision
Magued Iskander, Linzhu Li
Springer series in geomechanics and geoengineering (2024), pp. 293-325
Closed Access
Novel Explicit Models for Assessing the Frictional Resistance of Pipe Piles Subjected to Seismic Effects
Duaa Al-Jeznawi, Laith Sadik, Saif Alzabeebee, et al.
Journal of Safety Science and Resilience (2024)
Open Access
Ultimate load bearing of helical piles prediction and evaluation using machine learning-based algorithms
Abolfazl Eslami, Amirhossein Rahimi, Masoud Nobahar
Geomechanics and Geoengineering (2024), pp. 1-26
Closed Access
Load-Bearing Characteristics of PHC Piles Constructed by the Inner Digging Method Based on Ultimate Load Testing and Numerical Simulation
Yiwen Qu, Yi Zhang, Zhitian Wang, et al.
Applied Sciences (2023) Vol. 13, Iss. 9, pp. 5641-5641
Open Access | Times Cited: 1
Estimation of pile-bearing capacity applying hybrid auto-regressed models based on regression decision tree
Yunpeng Li, Dong Shenghuan, Jian Yingbin, et al.
Multiscale and Multidisciplinary Modeling Experiments and Design (2023) Vol. 7, Iss. 3, pp. 2225-2239
Closed Access | Times Cited: 1
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