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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:

Machine learning models to estimate stress wave velocities of cohesionless soils during triaxial compression influenced by particle characteristics
Amir Tophel, Troyee Tanu Dutta, M. Otsubo, et al.
Soil Dynamics and Earthquake Engineering (2022) Vol. 165, pp. 107649-107649
Closed Access | Times Cited: 11

Showing 11 citing articles:

Improving the prediction accuracy of small-strain shear modulus of granular soils through PSD: An investigation enabled by DEM and machine learning technique
Xingyang Liu, Zhanchao Li, Degao Zou, et al.
Computers and Geotechnics (2023) Vol. 157, pp. 105355-105355
Closed Access | Times Cited: 17
Predicting the small strain shear modulus of sands and sand-fines binary mixtures using machine learning algorithms
Naser Khodkari, Pouria Hamidian, Homayoun Khodkari, et al.
Transportation Geotechnics (2023) Vol. 44, pp. 101172-101172
Closed Access | Times Cited: 13
Shear Wave Velocity Prediction Based on the Long Short-Term Memory Network with Attention Mechanism
Xingan Fu, Youhua Wei, Yun Su, et al.
Applied Sciences (2024) Vol. 14, Iss. 6, pp. 2489-2489
Open Access | Times Cited: 4
Utilizing DEM and interpretable ML algorithms to examine particle size distribution's role in small-strain shear modulus of gap-graded granular mixtures
Xingyang Liu, Jiaqi Yang, Degao Zou, et al.
Construction and Building Materials (2024) Vol. 428, pp. 136232-136232
Closed Access | Times Cited: 4
Evaluation of empirical and machine learning models for predicting shear wave velocity of granular soils based on laboratory element tests
Zohreh Mousavi, Meysam Bayat, Jun Yang, et al.
Soil Dynamics and Earthquake Engineering (2024) Vol. 183, pp. 108805-108805
Closed Access | Times Cited: 3
Machine Learning Models for Predicting Shear Wave Velocity of Soils
Zohreh Mousavi, Meysam Bayat, Wei-Qiang Feng
IOP Conference Series Earth and Environmental Science (2024) Vol. 1334, Iss. 1, pp. 012039-012039
Open Access | Times Cited: 2
Innovative correlation relating the destruction of graphite flakes to the morphology characteristics of minerals
Nailing Wang, Xinyang Xu, Yanxin Jiang, et al.
Physicochemical Problems of Mineral Processing (2024)
Open Access | Times Cited: 1
Cumulative strain intelligent evaluation of marine soil from offshore wind farms based on enhanced machine learning
Zhishuai Zhang, Xinran Yu, Bo Han, et al.
Applied Ocean Research (2024) Vol. 153, pp. 104265-104265
Closed Access | Times Cited: 1
Analyzing the influence of particle size distribution on the maximum shear modulus of soil with an interpretable machine learning framework and laboratory test dataset
Xingyang Liu, Degao Zou, Yuan Chen, et al.
Soil Dynamics and Earthquake Engineering (2024) Vol. 188, pp. 109031-109031
Closed Access
Prediction of normalized shear modulus and damping ratio for granular soils over a wide strain range using deep neural network modelling
Wei‐Qiang Feng, Meysam Bayat, Zohreh Mousavi, et al.
Georisk Assessment and Management of Risk for Engineered Systems and Geohazards (2024), pp. 1-30
Open Access
Prediction of Normalized Shear Modulus and Damping Ratio for Granular Soils Over a Wide Strain Range Using Deep Neural Network Modelling
Meysam Bayat, Zohreh Mousavi, Aiguo Li, et al.
Research Square (Research Square) (2023)
Open Access
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