Classification of Sand Using Deep Learning

Linzhu Li, Magued Iskander

Research output: Contribution to journalArticlepeer-review


Identifying sands is an important requirement in many geotechnical exploration projects. Knowledge of the sand type can help in estimating the physical and mechanical properties of a soil. Previous studies revealed that machine learning approaches using neural networks (NN) can correctly identify up to 75% of individual sand particles using size and shape descriptors. This study explores the efficacy of deep learning methods for automatically classifying sand types from individual images of sand particles. Dynamic image analysis (DIA) was employed to generate a large data set of sand particle images, which were then used for training a deep learning model known as convolutional neural networks (CNN). The analysis was based on 40,000 binary particle images for twenty types of sand. The work demonstrates that computer vision has a remarkable ability to automatically classify 64% of individual sand particles among 20 types of sand, the accuracy for sand clusters can reach up to 100%, when a CNN model augmented with size and shape data was employed. The effects of domain size, size and shape parameters, and the selected CNN model on the classification accuracy were also investigated. The results demonstrate that sand classification using a larger number of sand types resulted in a lower classification accuracy. Classification accuracy of individual particles achieved using CNN were 10%-15% better than those achieved using NN. CNN can automatically and adaptively learn the spatial hierarchy of features which is superior to the handcrafted size and shape parameters, used by NN for identifying sand particles. The study suggests that classification accuracy benefits from data augmentation use of more particle orientations, even at the cost of trimming some particles, and suffers from reduction in image resolution. While model training requires a lot of computational work, a pretrained CNN model may potentially be tuned to run on mobile phones, which points to the potential for real-time field deployment to enable automatic soil classification on-site.

Original languageEnglish (US)
Article number04023103
JournalJournal of Geotechnical and Geoenvironmental Engineering
Issue number11
StatePublished - Nov 1 2023


  • Aggregate
  • Augmentation
  • Inception
  • MobileNet
  • ResNet
  • Roundness
  • Sediment
  • Sphericity
  • Visual Geometry Group (VGG)
  • Wadell

ASJC Scopus subject areas

  • General Environmental Science
  • Geotechnical Engineering and Engineering Geology


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