Locally Specified CPT Soil Classification Based on Machine Learning Techniques

Sohyun Cho, Han-Saem Kim and Hyunki Kim ()
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Sohyun Cho: Department of Civil and Environmental Engineering, Kookmin University, Seoul 02707, Republic of Korea
Han-Saem Kim: Earthquake Research Center, Korea Institute of Geoscience and Mineral Resources, Daejeon 34132, Republic of Korea
Hyunki Kim: Department of Civil and Environmental Engineering, Kookmin University, Seoul 02707, Republic of Korea

Sustainability, 2023, vol. 15, issue 4, 1-11

Abstract: Cone penetration tests (CPTs) can provide highly accurate and detailed information and characteristics relevant to the stiffness, strength, and consolidation of tested geomaterials, but they do not directly recover real soil samples. Thus, when CPT results are applied to soil classification, experience-based classification charts or tables are generally used. However, such charts or tables have the inherent drawback of being derived from the test data applied to each classification method, which promotes their failure to cover the engineering features of soils from other places. This study proposes a machine learning approach using C4.5 decision tree algorithm to develop a locally specified CPT-based soil classification system. The findings demonstrate that a locally specified soil classification scheme can be attained by utilizing a simple and trained decision tree model with appropriate combinations of training data and input attributes. Additionally, it is confirmed that oversampling the minor classes makes the classification accuracy for data with highly unbalanced classes appear more balanced for each class.

Keywords: classification; cone penetration test; machine learning; data mining; decision tree model (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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