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Geomechanics and Engineering Volume 35, Number 1, October10 2023 , pages 067-80 DOI: https://doi.org/10.12989/gae.2023.35.1.067 |
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A study on data mining techniques for soil classification methods using cone penetration test results |
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Junghee Park, So-Hyun Cho, Jong-Sub Lee and Hyun-Ki Kim
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| Abstract | ||
| 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. | ||
| Key Words | ||
| cone penetration test; data mining; decision tree model; machine learning; soil classification; stratification | ||
| Address | ||
| Junghee Park: Department of Civil and Environmental Engineering, Incheon National University, Incheon 22012, Republic of Korea So-Hyun Cho and Hyun-Ki Kim: Department of Civil and Environmental Engineering, Kookmin University, Seoul 02707, Republic of Korea Jong-Sub Lee: School of Civil, Environmental and Architectural Engineering, Korea University, Seoul 02841, Republic of Korea | ||