2021
DOI: 10.1002/nag.3296
|View full text |Cite
|
Sign up to set email alerts
|

Three‐dimensional quantitative analysis on granular particle shape using convolutional neural network

Abstract: To identify all desired shape parameters of granular particles with less computational cost, this study proposes a three-dimensional convolutional neural network (3D-CNN) based model. Datasets are made of 100 ballast and 100 Fujian sand particles, and the shape parameters (i.e., aspect ratio, roundness, sphericity, and convexity) obtained by conventional methods are used to label all particles. For the model training, by feeding the slice images of particles into the model, the contour of particles is automati… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

0
0
0

Year Published

2022
2022
2024
2024

Publication Types

Select...
5

Relationship

0
5

Authors

Journals

citations
Cited by 14 publications
references
References 39 publications
0
0
0
Order By: Relevance