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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-based multi-scale computational framework for granular materials
Shaoheng Guan, Tongming Qu, Y.T. Feng, et al.
Acta Geotechnica (2022) Vol. 18, Iss. 4, pp. 1699-1720
Closed Access | Times Cited: 17

Showing 17 citing articles:

Deep active learning for constitutive modelling of granular materials: From representative volume elements to implicit finite element modelling
Tongming Qu, Shaoheng Guan, Y.T. Feng, et al.
International Journal of Plasticity (2023) Vol. 164, pp. 103576-103576
Open Access | Times Cited: 45
Data-driven multiscale modelling of granular materials via knowledge transfer and sharing
Tongming Qu, Jidong Zhao, Shaoheng Guan, et al.
International Journal of Plasticity (2023) Vol. 171, pp. 103786-103786
Closed Access | Times Cited: 23
Spatial clustering of microscopic dynamics governs the slip avalanche of sheared granular materials
Jiangzhou Mei, Gang Ma, Longwen Tang, et al.
International Journal of Plasticity (2023) Vol. 163, pp. 103570-103570
Closed Access | Times Cited: 15
Data-driven constitutive modelling of granular soils considering multiscale particle morphology
Wei Xiong, Jianfeng Wang, Mengmeng Wu
Computers and Geotechnics (2023) Vol. 162, pp. 105699-105699
Closed Access | Times Cited: 14
Machine Learning-Based Modeling for Structural Engineering: A Comprehensive Survey and Applications Overview
Bassey B. Etim, Alia Al-Ghosoun, Jamil Renno, et al.
Buildings (2024) Vol. 14, Iss. 11, pp. 3515-3515
Open Access | Times Cited: 4
Predicting the stress-strain behavior of gravels with a hybrid deep learning approach
Duo Li, Jingmao Liu, Degao Zou, et al.
Transportation Geotechnics (2025), pp. 101492-101492
Closed Access
A neural network-based material cell for elastoplasticity and its performance in FE analyses of boundary value problems
Shaoheng Guan, Xue Zhang, Sascha Ranftl, et al.
International Journal of Plasticity (2023) Vol. 171, pp. 103811-103811
Open Access | Times Cited: 11
Deep learning‐accelerated multiscale approach for granular material modeling
Qingzheng Guan, Z. X. Yang, Ning Guo, et al.
International Journal for Numerical and Analytical Methods in Geomechanics (2024) Vol. 48, Iss. 5, pp. 1372-1389
Closed Access | Times Cited: 3
The Use of Shape Accel Array for Deformation Monitoring and Parameter Inversion of a 300 m Ultrahigh Rockfill Dam
Zhitao Ai, Gang Ma, Guike Zhang, et al.
Structural Control and Health Monitoring (2023) Vol. 2023, pp. 1-18
Open Access | Times Cited: 7
Deep transfer learning-aided constitutive modelling of granular soils considering out-of-range particle morphology
Wei Xiong, Jianfeng Wang
Tunnelling and Underground Space Technology (2023) Vol. 144, pp. 105547-105547
Closed Access | Times Cited: 5
Digital design and manufacturing of microstructural granular materials
Ruihuan Ge, Qing Liu
Digital engineering. (2024) Vol. 2, pp. 100008-100008
Open Access | Times Cited: 1
Improved mesh-free SPH approach for loose top coal caving modeling
Xiangwei Dong, Qiang Zhang, Yang Liu, et al.
Particuology (2024)
Closed Access | Times Cited: 1
Machine Learning Aided Modeling of Granular Materials: A Review
Mengqi Wang, Krishna Kumar, Y.T. Feng, et al.
Archives of Computational Methods in Engineering (2024)
Open Access | Times Cited: 1
Graph Neural Network Unveils the Spatiotemporal Evolution of Structural Defects in Sheared Granular Materials
Jiangzhou Mei, Gang Ma, Wanda Cao, et al.
International Journal of Plasticity (2024), pp. 104218-104218
Closed Access | Times Cited: 1
An explicit FEM-NN framework and the analysis of error caused by NN-predicted stress
Shaoheng Guan, Y.T. Feng, Gang Ma, et al.
Acta Geotechnica (2023) Vol. 19, Iss. 4, pp. 1815-1834
Closed Access | Times Cited: 3
Machine learning-guided study of residual stress, distortion, and peak temperature in stainless steel laser welding
Yapeng Yang, Nagaraj B. Patil, Shavan Askar, et al.
Applied Physics A (2024) Vol. 131, Iss. 1
Closed Access
GPM-PeNN: A generalized plasticity model-based data-driven constitutive modeling framework using physics-encoded neural network
Jingzhou Wang, Gang Ma, Tongming Qu, et al.
Computer Methods in Applied Mechanics and Engineering (2024) Vol. 436, pp. 117694-117694
Closed Access
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