A study on data mining techniques for soil classification methods using cone penetration test results

Junghee Park, So Hyun Cho, Jong Sub Lee, Hyun Ki Kim

Research output: Contribution to journalArticlepeer-review


Due to the nature of the conjunctive Cone Penetration Test(CPT), which does not verify the actual sample directly, geotechnical engineers commonly classify the underground geomaterials using CPT results with the classification diagrams proposed by various researchers. However, such classification diagrams may fail to reflect local geotechnical characteristics, potentially resulting in misclassification that does not align with the actual stratification in regions with strong local features. To address this, this paper presents an objective method for more accurate local CPT soil classification criteria, which utilizes C4.5 decision tree models trained with the CPT results from the clay-dominant southern coast of Korea and the sand-dominant region in South Carolina, USA. The results and analyses demonstrate that the C4.5 algorithm, in conjunction with oversampling, outlier removal, and pruning methods, can enhance and optimize the decision tree-based CPT soil classification model.

Original languageEnglish
Pages (from-to)67-80
Number of pages14
JournalGeomechanics and Engineering
Issue number1
Publication statusPublished - 2023 Oct 10

Bibliographical note

Publisher Copyright:
© 2023 Techno-Press, Ltd.


  • cone penetration test
  • data mining
  • decision tree model
  • machine learning
  • soil classification
  • stratification

ASJC Scopus subject areas

  • Civil and Structural Engineering
  • Geotechnical Engineering and Engineering Geology


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