Locally Specified CPT Soil Classification Based on Machine Learning Techniques

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.