Using deep learning to capture gravel soil microstructure and hydraulic characteristics

Using deep learning to capture gravel soil microstructure and hydraulic characteristics

Abstract The special hydraulic properties of gravel soil, attributed to its varying fine particle content, can be effectively analyzed using the Wasserstein Generative Adversarial Networks (WGANs) technique. This approach enables the reconstruction of 3D digital samples of gravel soil, allowing for...

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Bibliographic Details
Main Authors: Bin Zhu, Yu-Fei Xie, Xiang-Gang Hu, Dai-Rong Su
Format: Article
Language:English
Published: Nature Portfolio 2025-07-01
Series:Scientific Reports
Subjects:
WGANs (Wasserstein generative adversarial networks)
Machine learning
Porous structure
Pore-scale CFD simulation
Hydraulic properties imitation
Online Access:https://doi.org/10.1038/s41598-025-04879-4
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https://doi.org/10.1038/s41598-025-04879-4

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