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

2 Citations (Scopus)


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
Issue number12
Publication statusPublished - 1 Dec 2021


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

ASJC Scopus subject areas

  • Geotechnical Engineering and Engineering Geology
  • Soil Science


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