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Use of machine learning for classification of sand particles

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Abstract

Particle classification is essential for geotechnical engineering practice since particle shapes correlate with the mechanical and hydraulic properties of sand layers. Traditional shape classification is tedious, subjective, and time-consuming because it depends on manual visual comparison with reference particles. This study demonstrates the feasibility of employing machine learning algorithms for sand classification. Machine learning (ML) models are increasingly being introduced for automatic identification and classification of various objects. Nine types of sand were selected, and the analysis was based on 2000 binary images of each sand that were obtained from dynamic image analysis (DIA). Each particle was represented by six engineering size and four shape descriptors. The efficacy of seven ML models for automatically classifying individual sand particles was explored. The study demonstrates that the size and shape descriptors are efficient and robust to identify up to 75% of sand particles, using a neural network classifier. In addition, use of scale-invariant feature transform (SIFT) features was also explored to permit future generalization of sand classification using image datasets containing images with different scales and resolutions. Adding SIFT to size and shape can increase classification accuracy to 83% using a random forest classifier. The analysis also reveals that histograms of orientation gradients of SIFT keypoints in sand appear well correlated with sphericity and convexity of particles. This study suggests that a dataset of 2000 particles per sand is sufficient for optimal classification performance and that image preprocessing of DIA images was not necessary.

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All images used are proprietary to the authors, and they can be made available upon reasonable request.

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All the python scripts used are proprietary to the authors and are not available online.

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Funding

No funding was received for conducting this study.

Author information

Authors and Affiliations

  1. Postdoctoral Research Associate, Civil and Urban Engineering Deptartment, NYU Tandon School of Engineering, Brooklyn, NY, USA

    Linzhu Li

  2. Chair, Civil and Urban Engineering Department, NYU Tandon School of Engineering, Brooklyn, NY, USA

    Magued Iskander

Authors
  1. Linzhu Li
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  2. Magued Iskander
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Contributions

All authors contributed to the study conception and design. The specific roles are as follows: LL contributed to the data curation, software development, investigation, formal analysis, methodology, visualization, and writing—original draft. MI was involved in the conceptualization, supervision, validation, project administration, provision of resources, and writing—review and editing.

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Correspondence to Magued Iskander.

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Appendix: Hyperparameters used in machine learning classifiers

Appendix: Hyperparameters used in machine learning classifiers

 

Support vector machines (SVMs)

Decision tree

Naïve Bayes

K-nearest neighbors (KNN)

Neural network (MLP)

Random forest

Hyperparameters

Kernel function: Gaussian

Maximum number of splits: 210

Kernel type: Gaussian

Number of neighbors: 9

Number of fully connected layers: 1

Maximum number of splits: 523

Kernel scale: 0.020709

Split criterion: Gini’s diversity index

…

Distance metric: cosine

First layer size: 25

Number of trees: 24

Multiclass method: one-vs-all

…

…

Distance weight: inverse

Iteration limit: 1000

…

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Li, L., Iskander, M. Use of machine learning for classification of sand particles. Acta Geotech. 17, 4739–4759 (2022). https://doi.org/10.1007/s11440-021-01443-y

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  • DOI: https://doi.org/10.1007/s11440-021-01443-y

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