CNN-Based Intelligent Method for Identifying GSD of Granular Soils

Pin Zhang, Zhen Yu Yin, Wen Bo Chen, Yin Fu Jin

Research output: Journal article publicationJournal articleAcademic researchpeer-review

1 Citation (Scopus)

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.

Original languageEnglish
Article number04021229
JournalInternational Journal of Geomechanics
Volume21
Issue number12
DOIs
Publication statusPublished - 1 Dec 2021

Keywords

  • Grain-size distribution
  • Gravels
  • Image recognition
  • Neural networks
  • Sand

ASJC Scopus subject areas

  • Geotechnical Engineering and Engineering Geology
  • Soil Science

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