CNN-Based Intelligent Method for Identifying GSD of Granular Soils
Publication: International Journal of Geomechanics
Volume 21, Issue 12
Abstract
Different from conventional methodology, this study presents an intelligent approach to fast identify the grain-size distribution (GSD) of granular soils using a convolutional neural network (CNN) under a deep learning framework. A database including 279 images of granular soils with their GSDs is first created. Then, the framework of the CNN is tailored to identify GSD. The CNN-based model is trained to predict 11 grain sizes corresponding to 1%, 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, and 100% of granular soils passing (i.e., d1, d10, …, d100) using 80% of images, followed by the model testing using the rest 20% of images. By feeding an image of a soil sample into the proposed CNN-based model, the GSD can be predicted within several seconds. The predicted GSD exhibits excellent agreement with the measured one with an average error of 2.29% on the testing sets. It can be concluded that the proposed CNN-based model successfully provides a new intelligent way to fast, accurately, and conveniently identify the GSD of granular soils through images of soils.
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Acknowledgments
This research was financially supported by the Research Grants Council (RGC) of Hong Kong Special Administrative Region Government (HKSARG) of China (Grant No. 15220221).
Notation
The following symbols are used in this paper:
- f, s
- size and stride of the kernel;
- size of the feature map along the ith dimension in the input and convolutional layers; and
- yp, ya,
- predicted, actual, and mean of actual grain size.
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Published In
International Journal of Geomechanics
Volume 21 • Issue 12 • December 2021
Copyright
© 2021 American Society of Civil Engineers.
History
Received: Jan 21, 2021
Accepted: Aug 8, 2021
Published online: Sep 21, 2021
Published in print: Dec 1, 2021
Discussion open until: Feb 21, 2022
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